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Earlier puberty is widely linked with future obesity and cardiometabolic disease. We examined whether age at puberty onset likely influences adiposity and cardiometabolic traits independent of childhood adiposity. One-sample Mendelian randomisation (MR) analyses were conducted on up to 3,611 white-European female and male offspring from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort recruited at birth via mothers between 1 April 1991 and 31 December 1992. Time-sensitive exposures were age at menarche and age at voice breaking. Outcomes measured at age 18 y were body mass index (BMI), dual-energy X-ray absorptiometry-based fat and lean mass indices, blood pressure, and 230 cardiometabolic traits derived from targeted metabolomics (150 concentrations plus 80 ratios from nuclear magnetic resonance [NMR] spectroscopy covering lipoprotein subclasses of cholesterol and triglycerides, amino acids, inflammatory glycoproteins, and others). Adjustment was made for pre-pubertal BMI measured at age 8 y. For negative control MR analyses, BMI and cardiometabolic trait measures taken at age 8 y (before puberty, and which therefore cannot be an outcome of puberty itself) were used. For replication analyses, 2-sample MR was conducted using summary genome-wide association study data on up to 322,154 adults for post-pubertal BMI, 24,925 adults for post-pubertal NMR cardiometabolic traits, and 13,848 children for pre-pubertal obesity (negative control). Like observational estimates, 1-sample MR estimates in ALSPAC using 351 polymorphisms for age at menarche (explaining 10.6% of variance) among 2,053 females suggested that later age at menarche (per year) was associated with -1.38 kg/m2 of BMI at age 18 y (or -0.34 SD units, 95% CI -0.46, -0.23; P = 9.77 × 10-09). This coefficient attenuated 10-fold upon adjustment for BMI at age 8 y, to -0.12 kg/m2 (or -0.03 SDs, 95% CI -0.13, 0.07; P = 0.55). Associations with blood pressure were similar, but associations across other traits were small and inconsistent. In negative control MR analyses, later age at menarche was associated with -0.77 kg/m2 of pre-pubertal BMI measured at age 8 y (or -0.39 SDs, 95% CI -0.50, -0.29; P = 6.28 × 10-13), indicating that variants influencing menarche also influence BMI before menarche. Cardiometabolic trait associations were weaker and less consistent among males and both sexes combined. Higher BMI at age 8 y (per 1 kg/m2 using 95 polymorphisms for BMI explaining 3.4% of variance) was associated with earlier menarche among 2,648 females (by -0.26 y, 95% CI -0.37, -0.16; P = 1.16 × 10-06), likewise among males and both sexes combined. In 2-sample MR analyses using 234 polymorphisms and inverse variance weighted (IVW) regression, each year later age at menarche was associated with -0.81 kg/m2 of adult BMI (or -0.17 SD units, 95% CI -0.21, -0.12; P = 4.00 × 10-15). Associations were weaker with cardiometabolic traits. Using 202 polymorphisms, later menarche was associated with lower odds of childhood obesity (IVW-based odds ratio = 0.52 per year later, 95% CI 0.48, 0.57; P = 6.64 × 10-15). Study limitations include modest sample sizes for 1-sample MR, lack of inference to non-white-European populations, potential selection bias through modest completion rates of puberty questionnaires, and likely disproportionate measurement error of exposures by sex. The cardiometabolic traits examined were heavily lipid-focused and did not include hormone-related traits such as insulin and insulin-like growth factors. Our results suggest that puberty timing has a small influence on adiposity and cardiometabolic traits and that preventive interventions should instead focus on reducing childhood adiposity.

DNA methylation data have become a valuable source of information for biomarker development, because, unlike static genetic risk estimates, DNA methylation varies dynamically in relation to diverse exogenous and endogenous factors, including environmental risk factors and complex disease pathology. Reliable methods for genome-wide measurement at scale have led to the proliferation of epigenome-wide association studies and subsequently to the development of DNA methylation-based predictors across a wide range of health-related applications, from the identification of risk factors or exposures, such as age and smoking, to early detection of disease or progression in cancer, cardiovascular and neurological disease. This Review evaluates the progress of existing DNA methylation-based predictors, including the contribution of machine learning techniques, and assesses the uptake of key statistical best practices needed to ensure their reliable performance, such as data-driven feature selection, elimination of data leakage in performance estimates and use of generalizable, adequately powered training samples.DNA methylation-based predictors of health aim to predict outcomes such as exposure, phenotype or disease on the basis of genome-wide levels of DNA methylation. The authors review applications of existing DNA methylation-based predictors and highlight key statistical best practices to ensure their reliable performance.

Results from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using a strategy known as 2-sample Mendelian randomization (2SMR) and bypassing the need for individual-level data. However, 2SMR methods are evolving rapidly and GWAS results are often insufficiently curated, undermining efficient implementation of the approach. We therefore developed MR-Base ( http://www.mrbase.org ): a platform that integrates a curated database of complete GWAS results (no restrictions according to statistical significance) with an application programming interface, web app and R packages that automate 2SMR. The software includes several sensitivity analyses for assessing the impact of horizontal pleiotropy and other violations of assumptions. The database currently comprises 11 billion single nucleotide polymorphism-trait associations from 1673 GWAS and is updated on a regular basis. Integrating data with software ensures more rigorous application of hypothesis-driven analyses and allows millions of potential causal relationships to be efficiently evaluated in phenome-wide association studies. Our health is affected by many exposures and risk factors, including aspects of our lifestyles, our environments, and our biology. It can, however, be hard to work out the causes of health outcomes because ill-health can influence risk factors and risk factors tend to influence each other. To work out whether particular interventions influence health outcomes, scientists will ideally conduct a so-called randomized controlled trial, where some randomly-chosen participants are given an intervention that modifies the risk factor and others are not. But this type of experiment can be expensive or impractical to conduct. Alternatively, scientists can also use genetics to mimic a randomized controlled trial. This technique – known as Mendelian randomization – is possible for two reasons. First, because it is essentially random whether a person has one version of a gene or another. Second, because our genes influence different risk factors. For example, people with one version of a gene might be more likely to drink alcohol than people with another version. Researchers can compare people with different versions of the gene to infer what effect alcohol drinking has on their health. Every day, new studies investigate the role of genetic variants in human health, which scientists can draw on for research using Mendelian randomization. But until now, complete results from these studies have not been organized in one place. At the same time, statistical methods for Mendelian randomization are continually being developed and improved. To take advantage of these advances, Hemani, Zheng, Elsworth et al. produced a computer programme and online platform called “MR-Base”, combining up-to-date genetic data with the latest statistical methods. MR-Base automates the process of Mendelian randomization, making research much faster: analyses that previously could have taken months can now be done in minutes. It also makes studies more reliable, reducing the risk of human error and ensuring scientists use the latest methods. MR-Base contains over 11 billion associations between people’s genes and health-related outcomes. This will allow researchers to investigate many potential causes of poor health. As new statistical methods and new findings from genetic studies are added to MR-Base, its value to researchers will grow.

Excess body adiposity is an established cause of renal cancer, but underlying molecular pathways mediating this relationship remain unclear. This study aimed to systematically evaluate a panel of obesity-related risk factors as potential mediators in renal cancer etiology. We used two complementary approaches to evaluate obesity-related risk factors in renal cancer etiology: (i) direct risk factor assessment in longitudinal cohorts and (ii) genetically proxied risk factors through two-sample mendelian randomization (MR). Direct risk-factor association-analyses (i.e., cohort analyses) were based on the UK Biobank cohort study (472,337 cohort participants, including 1,382 incident renal cancer cases diagnosed during 5,586,414 person years of follow-up) and the Northern Sweden Health and Disease Study (NSHDS) for fasting insulin (204 pairs of cases and controls, ongoing recruitment and follow-up since 1985). We used Cox proportional hazards regression models to evaluate the association between risk factors and renal cancer risk with adjustment for age, sex, center of recruitment, education, smoking and alcohol drinking status. Two-sample MR analyses were based on a genome-wide association study (GWAS) of renal cancer (27,213 cases, 486,846 controls). We used the inverse-variance weighted (IVW) approach to estimate the association between risk factors and renal cancer risk. Mediation analyses were performed for traits displaying directionally consistent associations with renal cancer risk in both the cohort and MR analyses using the product method. We found consistent positive associations with renal cancer risk for fasting insulin (odds ratio per standard deviation increment [ORMR]: 2.24, 95% confidence interval [95% CI]: 1.19, 4.22; p = 0.01; hazard ratio per standard deviation increment [HRcohort]: 1.43, 95% CI: 1.02, 2.00; p = 0.04), triglycerides (ORMR: 1.11, 95% CI: 1.05, 1.17; p < 0.001, HRcohort: 1.23, 95% CI: 1.11, 1.38; p < 0.001), diastolic blood pressure (DBP) (ORMR: 1.14, 95% CI: 1.04, 1.26; p < 0.001, HRcohort: 1.11, 95% CI: 1.05, 1.17; p < 0.001) and consistent inverse associations with renal cancer risk for sex-hormone binding globulin (SHBG) (ORMR: 0.80, 95% CI: 0.70, 0.90; p < 0.001, HRcohort: 0.67, 95% CI: 0.58, 0.76; p < 0.001) and high-density lipoprotein (HDL) cholesterol (ORMR: 0.93, 95% CI: 0.88, 0.98; p < 0.001, HRcohort: 0.72, 95% CI: 0.66, 0.77; p < 0.001). The main limitation of this study was that we had limited statistical power to evaluate some risk factors. Our study highlights roles for fasting insulin, HDL cholesterol, DBP, triglycerides and SHBG in mediating the relationship between body adiposity and renal cancer risk.

Background DNA methylation (DNAm) performs excellently in the discrimination of current and former smokers from never smokers, where AUCs > 0.9 are regularly reported using a single CpG site (cg05575921; AHRR ). However, there is a paucity of DNAm models which attempt to distinguish current, former and never smokers as individual classes. Derivation of a robust DNAm model that accurately distinguishes between current, former and never smokers would be particularly valuable to epidemiological research (as a more accurate smoking definition vs. self-report) and could potentially translate to clinical settings. Therefore, we appraise 4 DNAm models of ternary smoking status (that is, current, former and never smokers): methylation at cg05575921 (AHRR model), weighted scores from 13 CpGs created by Maas et al. (Maas model), weighted scores from a LASSO model of candidate smoking CpGs from the literature (candidate CpG LASSO model), and weighted scores from a LASSO model supplied with genome-wide 450K data (agnostic LASSO model). Discrimination is assessed by AUC, whilst classification accuracy is assessed by accuracy and kappa, derived from confusion matrices. Results We find that DNAm can classify ternary smoking status with reasonable accuracy, including when applied to external data. Ternary classification using only DNAm far exceeds the classification accuracy of simply assigning all classes as the most prevalent class (63.7% vs. 36.4%). Further, we develop a DNAm classifier which performs well in discriminating current from former smokers (agnostic LASSO model AUC in external validation data: 0.744). Finally, across our DNAm models, we show evidence of enrichment for biological pathways and human phenotype ontologies relevant to smoking, such as haemostasis, molybdenum cofactor synthesis, body fatness and social behaviours, providing evidence of the generalisability of our classifiers. Conclusions Our findings suggest that DNAm can classify ternary smoking status with close to 65% accuracy. Both the ternary smoking status classifiers and current versus former smoking status classifiers address the present lack of former smoker classification in epigenetic literature; essential if DNAm classifiers are to adequately relate to real-world populations. To improve performance further, additional focus on improving discrimination of current from former smokers is necessary.

Obesity is associated with an increased risk of advanced, recurrent and fatal prostate cancer. Adipokines may mediate this relationship. We conducted a systematic review and meta-analysis of associations of leptin and adiponectin with overall and aggressive prostate cancer. Bibliographic databases were s ystematically searched up to 1st April 2017. Log Odds Ratios (ORs) per 2.5 unit increase in adiponectin or leptin levels were derived and pooled. All analyses were stratified by study type (cross-sectional/prospective). 746 papers were retrieved, 34 eligible studies identified, 31 of these could be included in the meta-analysis. Leptin was not consistently associated with overall prostate cancer (pooled OR 1.00, 95%CI 0.98–1.02, per 2.5 ng/ml increase, prospective study OR 0.97, 95%CI 0.95–0.99, cross-sectional study OR 1.19, 95%CI 1.13–1.26) and there was weak evidence of a positive association with aggressive disease (OR 1.03, 95%CI 1.00–1.06). There was also weak evidence of a small inverse association of adiponectin with overall prostate cancer (OR 0.96, 95%CI 0.93–0.99, per 2.5 µg/ml increase), but less evidence of an association with aggressive disease (OR 0.98, 95%CI 0.94–1.01). The magnitude of any effects are small, therefore levels of circulating adiponectin or leptin alone are unlikely to be useful biomarkers of risk or prognosis.

Background Biological age, estimated by DNA methylation-based (DNAm) clocks, has been reported to be associated with lung cancer risk. However, the extent to which tobacco smoking behaviours can explain this association and the extent to which DNAm clocks and their components can inform risk assessment for lung cancer remains to be elucidated. This study aimed to evaluate the relationship between DNAm clocks, smoking, and lung cancer risk. Methods We analyzed four prospective cohorts (MCCS, Australia, 324 cases/324 controls; NSHDS, Sweden, 190 cases/190 controls; EPIC, Italy, 160 cases/107 controls; and NOWAC, Norway, 115 case/70 controls) with blood samples collected before lung cancer diagnosis. Study participants were restricted to those with a history of smoking. Incidence sampling was used to match one control to each of the lung cancer cases by cohort, sex, date of blood collection, age, and smoking status in MCCS and NSHDS. The risk discriminative performance of age-adjusted DNAm clocks and their components was compared with that of the Prostate, Lung, Colorectal, and Ovarian model 2012 (PLCO m2012 ) lung cancer risk model. Results We found several DNAm clocks positively associated with lung cancer risk (Hannum: OR = 1.13, 95% CI = 1.02–1.26; PhenoAge: OR = 1.25, 95% CI = 1.12–1.40; DunedinPACE: OR = 1.44, 95% CI = 1.29–1.62; PCGrimAge (a principal component-denoised GrimAge): OR = 1.79, 95% CI = 1.56–2.06), after adjustment for age and tobacco smoking. Tobacco smoking explained a modest proportion of variance in most age-adjusted DNAm clocks ( R 2  < 11%), except for PCGrimAge, where it accounted for ~ 30% of variance in both lung cancer cases and controls. Detailed smoking adjustments attenuated the PCGrimAge association with lung cancer risk by 13%. In a secondary analysis adjusting for PCGrimAge components and the PLCO m2012 score, DNA methylation-predicted packyears emerged as an independent predictor of lung cancer risk (OR = 2.23, 95% CI = 1.58–3.14). The area under the receiver operating characteristic curve (AUC) for the PLCO m2012 model was 0.66 (95% CI = 0.61–0.71) compared with 0.72 (95% CI = 0.67–0.77) for the PCGrimAge model ( P difference  = 0.03). Combining PCGrimAge with PLCO m2012 provided similar risk discrimination as PCGrimAge alone (AUC = 0.72, 95% CI = 0.67–0.77). Conclusions Methylation-based biological clocks capture epigenetic marks left by exposure to tobacco smoke, and some clocks may inform lung cancer risk assessment by complementing or replacing traditional prediction models.

Background DNA methylation (DNAm) variation is an established predictor for several traits. In the context of oropharyngeal cancer (OPC), where 5-year survival is ~ 65%, DNA methylation may act as a prognostic biomarker. We examined the accuracy of DNA methylation biomarkers of 4 complex exposure traits (alcohol consumption, body mass index [BMI], educational attainment and smoking status) in predicting all-cause mortality in people with OPC. Results DNAm predictors of alcohol consumption, BMI, educational attainment and smoking status were applied to 364 individuals with OPC in the Head and Neck 5000 cohort (HN5000; 19.6% of total OPC cases in the study), followed up for median 3.9 years; inter-quartile range (IQR) 3.3 to 5.2 years (time-to-event—death or censor). The proportion of phenotypic variance explained in each trait was as follows: 16.5% for alcohol consumption, 22.7% for BMI, 0.4% for educational attainment and 51.1% for smoking. We then assessed the relationship between each DNAm predictor and all-cause mortality using Cox proportional-hazard regression analysis. DNAm prediction of smoking was most consistently associated with mortality risk (hazard ratio [HR], 1.38 per standard deviation (SD) increase in smoking DNAm score; 95% confidence interval [CI] 1.04 to 1.83; P 0.025, in a model adjusted for demographic, lifestyle, health and biological variables). Finally, we examined the accuracy of each DNAm predictor of mortality. DNAm predictors explained similar levels of variance in mortality to self-reported phenotypes. Receiver operator characteristic (ROC) curves for the DNAm predictors showed a moderate discrimination of alcohol consumption (area under the curve [AUC] 0.63), BMI (AUC 0.61) and smoking (AUC 0.70) when predicting mortality. The DNAm predictor for education showed poor discrimination (AUC 0.57). Z tests comparing AUCs between self-reported phenotype ROC curves and DNAm score ROC curves did not show evidence for difference between the two (alcohol consumption P 0.41, BMI P 0.62, educational attainment P 0.49, smoking P 0.19). Conclusions In the context of a clinical cohort of individuals with OPC, DNAm predictors for smoking, alcohol consumption, educational attainment and BMI exhibit similar predictive values for all-cause mortality compared to self-reported data. These findings may have translational utility in prognostic model development, particularly where phenotypic data are not available.

Clonal hematopoiesis (CH), the clonal expansion of a blood stem cell and its progeny driven by somatic driver mutations, affects over a third of people, yet remains poorly understood. Here we analyze genetic data from 200,453 UK Biobank participants to map the landscape of inherited predisposition to CH, increasing the number of germline associations with CH in European-ancestry populations from 4 to 14. Genes at new loci implicate DNA damage repair ( PARP1 , ATM , CHEK2 ), hematopoietic stem cell migration/homing ( CD164 ) and myeloid oncogenesis ( SETBP1 ). Several associations were CH-subtype-specific including variants at TCL1A and CD164 that had opposite associations with DNMT3A - versus TET2 -mutant CH, the two most common CH subtypes, proposing key roles for these two loci in CH development. Mendelian randomization analyses showed that smoking and longer leukocyte telomere length are causal risk factors for CH and that genetic predisposition to CH increases risks of myeloproliferative neoplasia, nonhematological malignancies, atrial fibrillation and blood epigenetic ageing. Analysis of whole-exome sequencing data from 200,453 UK Biobank participants identifies loci associated with clonal hematopoiesis and highlights causal links between clonal hematopoiesis and other traits.