Showing papers by "Debbie A Lawlor published in 2019"
TL;DR: It is shown that being a morning person is causally associated with better mental health but does not affect BMI or risk of Type 2 diabetes, and the chronotype loci associate with sleep timing.
Abstract: Being a morning person is a behavioural indicator of a person's underlying circadian rhythm. Using genome-wide data from 697,828 UK Biobank and 23andMe participants we increase the number of genetic loci associated with being a morning person from 24 to 351. Using data from 85,760 individuals with activity-monitor derived measures of sleep timing we find that the chronotype loci associate with sleep timing: the mean sleep timing of the 5% of individuals carrying the most morningness alleles is 25 min earlier than the 5% carrying the fewest. The loci are enriched for genes involved in circadian regulation, cAMP, glutamate and insulin signalling pathways, and those expressed in the retina, hindbrain, hypothalamus, and pituitary. Using Mendelian Randomisation, we show that being a morning person is causally associated with better mental health but does not affect BMI or risk of Type 2 diabetes. This study offers insights into circadian biology and its links to disease in humans.
363 citations
Nicole M. Warrington1, Robin N Beaumont2, Momoko Horikoshi3, Felix R. Day4 +242 more•Institutions (79)
TL;DR: An expanded GWAS of birth weight and subsequent analysis using structural equation modeling and Mendelian randomization decomposes maternal and fetal genetic contributions and causal links between birth weight, blood pressure and glycemic traits.
Abstract: Birth weight variation is influenced by fetal and maternal genetic and non-genetic factors, and has been reproducibly associated with future cardio-metabolic health outcomes. In expanded genome-wide association analyses of own birth weight (n = 321,223) and offspring birth weight (n = 230,069 mothers), we identified 190 independent association signals (129 of which are novel). We used structural equation modeling to decompose the contributions of direct fetal and indirect maternal genetic effects, then applied Mendelian randomization to illuminate causal pathways. For example, both indirect maternal and direct fetal genetic effects drive the observational relationship between lower birth weight and higher later blood pressure: maternal blood pressure-raising alleles reduce offspring birth weight, but only direct fetal effects of these alleles, once inherited, increase later offspring blood pressure. Using maternal birth weight-lowering genotypes to proxy for an adverse intrauterine environment provided no evidence that it causally raises offspring blood pressure, indicating that the inverse birth weight-blood pressure association is attributable to genetic effects, and not to intrauterine programming.
323 citations
Harvard University1, Broad Institute2, University of Exeter3, Brigham and Women's Hospital4, Northeastern University5, University of Manchester6, University of Bristol7, University of Murcia8, University of Oxford9, Massachusetts Institute of Technology10, Aston University11, University of Freiburg12, VA Boston Healthcare System13, Erasmus University Medical Center14, Beth Israel Deaconess Medical Center15
TL;DR: Performing GWAS for self-reported habitual sleep duration in adults, supported by accelerometer-derived measures, and identifying genetic correlation with psychiatric and metabolic traits provides insights into the genetic basis for inter-individual variation in sleep duration implicating multiple biological pathways.
Abstract: Sleep is an essential state of decreased activity and alertness but molecular factors regulating sleep duration remain unknown. Through genome-wide association analysis in 446,118 adults of European ancestry from the UK Biobank, we identify 78 loci for self-reported habitual sleep duration (p < 5 × 10−8; 43 loci at p < 6 × 10−9). Replication is observed for PAX8, VRK2, and FBXL12/UBL5/PIN1 loci in the CHARGE study (n = 47,180; p < 6.3 × 10−4), and 55 signals show sign-concordant effects. The 78 loci further associate with accelerometer-derived sleep duration, daytime inactivity, sleep efficiency and number of sleep bouts in secondary analysis (n = 85,499). Loci are enriched for pathways including striatum and subpallium development, mechanosensory response, dopamine binding, synaptic neurotransmission and plasticity, among others. Genetic correlation indicates shared links with anthropometric, cognitive, metabolic, and psychiatric traits and two-sample Mendelian randomization highlights a bidirectional causal link with schizophrenia. This work provides insights into the genetic basis for inter-individual variation in sleep duration implicating multiple biological pathways.
299 citations
Erasmus University Rotterdam1, University of Southampton2, University Hospital Southampton NHS Foundation Trust3, University of Porto4, Sorbonne5, Paris Descartes University6, University of Southern California7, University of Crete8, Maastricht University9, National and Kapodistrian University of Athens10, University Medical Center Groningen11, Université de Sherbrooke12, Norwegian Institute of Public Health13, University of Bologna14, Nofer Institute of Occupational Medicine15, University of California, Davis16, Harvard University17, University of Illinois at Chicago18, University of Valencia19, National Institutes of Health20, University of Turku21, University of Bristol22, Helmholtz Centre for Environmental Research - UFZ23, Jagiellonian University Medical College24, Åbo Akademi University25, Harokopio University26, Public Health Research Institute27, University of Copenhagen28, University of Southern Denmark29, La Trobe University30, University of Helsinki31, University of Turin32, Radboud University Nijmegen33, University of Trieste34, University of Bergen35, Ludwig Maximilian University of Munich36, Slovak Medical University37, Utrecht University38, Pompeu Fabra University39
TL;DR: In this meta-analysis of pooled individual participant data from 25 cohort studies, the risk for adverse maternal and infant outcomes varied by gestational weight gain and across the range of prepregnancy weights, however, the optimal gestations weight gain ranges had limited predictive value for the outcomes assessed.
Abstract: Importance Both low and high gestational weight gain have been associated with adverse maternal and infant outcomes, but optimal gestational weight gain remains uncertain and not well defined for all prepregnancy weight ranges. Objectives To examine the association of ranges of gestational weight gain with risk of adverse maternal and infant outcomes and estimate optimal gestational weight gain ranges across prepregnancy body mass index categories. Design, Setting, and Participants Individual participant-level meta-analysis using data from 196 670 participants within 25 cohort studies from Europe and North America (main study sample). Optimal gestational weight gain ranges were estimated for each prepregnancy body mass index (BMI) category by selecting the range of gestational weight gain that was associated with lower risk for any adverse outcome. Individual participant-level data from 3505 participants within 4 separate hospital-based cohorts were used as a validation sample. Data were collected between 1989 and 2015. The final date of follow-up was December 2015. Exposures Gestational weight gain. Main Outcomes and Measures The main outcome termedany adverse outcomewas defined as the presence of 1 or more of the following outcomes: preeclampsia, gestational hypertension, gestational diabetes, cesarean delivery, preterm birth, and small or large size for gestational age at birth. Results Of the 196 670 women (median age, 30.0 years [quartile 1 and 3, 27.0 and 33.0 years] and 40 937 were white) included in the main sample, 7809 (4.0%) were categorized at baseline as underweight (BMI Conclusions and Relevance In this meta-analysis of pooled individual participant data from 25 cohort studies, the risk for adverse maternal and infant outcomes varied by gestational weight gain and across the range of prepregnancy weights. The estimates of optimal gestational weight gain may inform prenatal counseling; however, the optimal gestational weight gain ranges had limited predictive value for the outcomes assessed.
286 citations
Erasmus University Medical Center1, University of Porto2, University of Western Australia3, Stockholm County Council4, Paris Descartes University5, Maastricht University6, French Institute of Health and Medical Research7, National and Kapodistrian University of Athens8, University Medical Center Groningen9, University of Valencia10, University of Southampton11, Liverpool School of Tropical Medicine12, Université de Sherbrooke13, Norwegian Institute of Public Health14, University of Bologna15, University of Crete16, University Hospital Southampton NHS Foundation Trust17, Ludwig Maximilian University of Munich18, Nofer Institute of Occupational Medicine19, University of California20, Harvard University21, University of Illinois at Chicago22, National Institutes of Health23, Wageningen University and Research Centre24, University of Turku25, Helmholtz Centre for Environmental Research - UFZ26, Jagiellonian University Medical College27, Åbo Akademi University28, Harokopio University29, University College Dublin30, University of Calgary31, Boston Children's Hospital32, University of Copenhagen33, University College Cork34, VU University Medical Center35, University of Helsinki36, University of Turin37, Radboud University Nijmegen38, University of Trieste39, University of Bergen40, Slovak Medical University41, Utrecht University42, Pompeu Fabra University43, Bradford Royal Infirmary44, University of Bristol45
TL;DR: In this paper, the separate and combined associations of maternal pre-pregnancy body mass index (BMI) and gestational weight gain with the risks of pregnancy complications and their population impact were assessed.
258 citations
Erasmus University Medical Center1, Medical University of Warsaw2, University of Valencia3, University of Porto4, Stockholm County Council5, Sorbonne6, Paris Descartes University7, University of Crete8, Maastricht University9, University of Southern California10, French Institute of Health and Medical Research11, National and Kapodistrian University of Athens12, University Medical Center Groningen13, University of Southampton14, Liverpool School of Tropical Medicine15, Norwegian Institute of Public Health16, Karolinska Institutet17, University of Bologna18, University Hospital Southampton NHS Foundation Trust19, Ludwig Maximilian University of Munich20, Nofer Institute of Occupational Medicine21, University of California, Davis22, University of Illinois at Chicago23, University of Western Australia24, National Institutes of Health25, University College Cork26, University of Bristol27, University of Turku28, Helmholtz Centre for Environmental Research - UFZ29, Jagiellonian University Medical College30, Åbo Akademi University31, Harokopio University32, University College Dublin33, University of Calgary34, Public Health Research Institute35, University of Copenhagen36, University of Southern Denmark37, La Trobe University38, Harvard University39, University of Helsinki40, University of Turin41, University of Trieste42, University of Bergen43, Slovak Medical University44, Boston Children's Hospital45, Utrecht University46, Pompeu Fabra University47, Bradford Royal Infirmary48
TL;DR: In this article, the authors conducted an individual participant data meta-analysis of data from 162,129 mothers and children from 37 pregnancy and birth cohort studies from Europe, North-America and Australia, using multilevel binary logistic regression models with a random intercept at cohort level adjusted for maternal socio-demographic and life style related characteristics.
Abstract: Background:
Maternal obesity and excessive gestational weight gain may have persistent effects on offspring fat development. However, it remains unclear whether these risks differ by severity of obesity, and whether these effects are restricted to the extremes of maternal body mass index (BMI) and gestational weight gain. We aimed to assess the separate and combined associations of maternal BMI and gestational weight gain with the risk of overweight/obesity throughout childhood, and their population impact.
Methods and Findings:
We conducted an individual participant data meta-analysis of data from 162,129 mothers and children from 37 pregnancy and birth cohort studies from Europe, North-America and Australia. We assessed the individual and combined associations of maternal pre-pregnancy BMI and gestational weight gain, both in clinical categories and across their full ranges with the risks of overweight/obesity in early- (2.0-5.0 years), mid- (5.0-10.0 years) and late childhood (10.0-18.0 years), using multilevel binary logistic regression models with a random intercept at cohort level adjusted for maternal socio-demographic and life style related characteristics. We observed that a higher maternal pre-pregnancy BMI and gestational weight gain both in clinical categories and across their full ranges were associated with higher risks of childhood overweight/obesity, with the strongest effects in late childhood (Odds Ratios (OR) for overweight/obesity in early-, mid- and late childhood, respectively: 1.66 (95% Confidence Interval (CI): 1.56, 1.78), OR 1.91 (95% CI: 1.85, 1.98), and OR 2.28 (95% CI: 2.08, 2.50) for maternal overweight, OR 2.43 (95% CI: 2.24, 2.64), OR 3.12 (95% CI: 2.98, 3.27), and OR 4.47 (95% CI: 3.99, 5.23) for maternal obesity, and OR 1.39 (95% CI: 1.30, 1.49), OR 1.55 (95% CI: 1.49, 1.60), and 1.72 (95% CI: 1.56, 1.91) for excessive gestational weight gain. The proportions of childhood overweight/obesity prevalence attributable to maternal overweight, maternal obesity and excessive gestational weight gain ranged from 10.2 to 21.6%. Relative to the effect of maternal BMI, excessive gestational weight gain only slightly increased the risk of childhood overweight/obesity within each clinical BMI category (P-values for interactions of maternal BMI with gestational weight gain: p=0.038, p<0.001 and p=0.637, in early-, mid- and late childhood, respectively). Limitations of this study include the self-report of maternal BMI and gestational weight gain for some of the cohorts, and the potential of residual confounding. Also, as this study only included participants from Europe, North-America and Australia, results need to be interpreted with caution with respect to other populations.
Conclusions:
In this study, higher maternal pre-pregnancy BMI and gestational weight gain were associated with an increased risk of childhood overweight/obesity, with the strongest effects at later ages. The additional effect of gestational weight gain in women who are overweight or obese before pregnancy is small. Given the large population impact, future intervention trials aiming to reduce the prevalence of childhood overweight and obesity should focus on maternal weight status before pregnancy, in addition to weight gain during pregnancy.
248 citations
TL;DR: The use of modified weights within two-sample summary-data MR studies is proposed for accurately quantifying heterogeneity and detecting outliers in the presence of weak instruments.
Abstract: BACKGROUND Two-sample summary-data Mendelian randomization (MR) incorporating multiple genetic variants within a meta-analysis framework is a popular technique for assessing causality in epidemiology. If all genetic variants satisfy the instrumental variable (IV) and necessary modelling assumptions, then their individual ratio estimates of causal effect should be homogeneous. Observed heterogeneity signals that one or more of these assumptions could have been violated. METHODS Causal estimation and heterogeneity assessment in MR require an approximation for the variance, or equivalently the inverse-variance weight, of each ratio estimate. We show that the most popular 'first-order' weights can lead to an inflation in the chances of detecting heterogeneity when in fact it is not present. Conversely, ostensibly more accurate 'second-order' weights can dramatically increase the chances of failing to detect heterogeneity when it is truly present. We derive modified weights to mitigate both of these adverse effects. RESULTS Using Monte Carlo simulations, we show that the modified weights outperform first- and second-order weights in terms of heterogeneity quantification. Modified weights are also shown to remove the phenomenon of regression dilution bias in MR estimates obtained from weak instruments, unlike those obtained using first- and second-order weights. However, with small numbers of weak instruments, this comes at the cost of a reduction in estimate precision and power to detect a causal effect compared with first-order weighting. Moreover, first-order weights always furnish unbiased estimates and preserve the type I error rate under the causal null. We illustrate the utility of the new method using data from a recent two-sample summary-data MR analysis to assess the causal role of systolic blood pressure on coronary heart disease risk. CONCLUSIONS We propose the use of modified weights within two-sample summary-data MR studies for accurately quantifying heterogeneity and detecting outliers in the presence of weak instruments. Modified weights also have an important role to play in terms of causal estimation (in tandem with first-order weights) but further research is required to understand their strengths and weaknesses in specific settings.
225 citations
Broad Institute1, Harvard University2, University of Exeter3, Norwegian University of Science and Technology4, Oslo University Hospital5, Brigham and Women's Hospital6, University of Bristol7, Northeastern University8, University College London9, University of Manchester10, Massachusetts Institute of Technology11, Aston University12, University of Oxford13, Erasmus University Rotterdam14, University of Michigan15, Beth Israel Deaconess Medical Center16, University of Freiburg17, John Radcliffe Hospital18, Manchester Academic Health Science Centre19
TL;DR: Genome-wide association analyses identify 57 loci associated with insomnia symptoms and provide evidence of shared genetic architecture between insomnia and cardiometabolic, behavioral, psychiatric and reproductive traits.
Abstract: Insomnia is a common disorder linked with adverse long-term medical and psychiatric outcomes. The underlying pathophysiological processes and causal relationships of insomnia with disease are poorly understood. Here we identified 57 loci for self-reported insomnia symptoms in the UK Biobank (n = 453,379) and confirmed their effects on self-reported insomnia symptoms in the HUNT Study (n = 14,923 cases and 47,610 controls), physician-diagnosed insomnia in the Partners Biobank (n = 2,217 cases and 14,240 controls), and accelerometer-derived measures of sleep efficiency and sleep duration in the UK Biobank (n = 83,726). Our results suggest enrichment of genes involved in ubiquitin-mediated proteolysis and of genes expressed in multiple brain regions, skeletal muscle, and adrenal glands. Evidence of shared genetic factors was found between frequent insomnia symptoms and restless legs syndrome, aging, and cardiometabolic, behavioral, psychiatric, and reproductive traits. Evidence was found for a possible causal link between insomnia symptoms and coronary artery disease, depressive symptoms, and subjective well-being.
207 citations
University of London1, University of Cambridge2, Innsbruck Medical University3, Harvard University4, Lund University5, University of Minnesota6, German Cancer Research Center7, University of Western Australia8, University of Copenhagen9, Osaka University10, University of Groningen11, St George's Hospital12, National Institute for Health and Welfare13, Ludwig Maximilian University of Munich14, Technische Universität München15, University of Ulm16, Erasmus University Rotterdam17, University of Greifswald18, University College London19, Kyushu University20, University of Padua21, University of Bristol22, Laval University23, University of New South Wales24, University of California, San Diego25, University of Gothenburg26, University of Edinburgh27, Medical University of South Carolina28, University of Eastern Finland29, Utrecht University30, VU University Amsterdam31, Tel Aviv University32, University of Southampton33, Istituto Superiore di Sanità34, University of Washington35, University of Pittsburgh36, Portland State University37, Centers for Disease Control and Prevention38, Monash University39, Norwegian Institute of Public Health40, City University of New York41, Northwestern University42, Albert Einstein College of Medicine43, University of Glasgow44
TL;DR: Older age, smoking, and adiposity were consistently associated with higher VTE risk, and there was inconsistent associations of VTEs with diabetes and blood pressure across ERFC and UK Biobank.
Abstract: Importance: It is uncertain to what extent established cardiovascular risk factors are associated with venous thromboembolism (VTE). Objective: To estimate the associations of major cardiovascular risk factors with VTE, ie, deep vein thrombosis and pulmonary embolism. Design, Setting, and Participants: This study included individual participant data mostly from essentially population-based cohort studies from the Emerging Risk Factors Collaboration (ERFC; 731 728 participants; 75 cohorts; years of baseline surveys, February 1960 to June 2008; latest date of follow-up, December 2015) and the UK Biobank (421 537 participants; years of baseline surveys, March 2006 to September 2010; latest date of follow-up, February 2016). Participants without cardiovascular disease at baseline were included. Data were analyzed from June 2017 to September 2018. Exposures: A panel of several established cardiovascular risk factors. Main Outcomes and Measures: Hazard ratios (HRs) per 1-SD higher usual risk factor levels (or presence/absence). Incident fatal outcomes in ERFC (VTE, 1041; coronary heart disease [CHD], 25 131) and incident fatal/nonfatal outcomes in UK Biobank (VTE, 2321; CHD, 3385). Hazard ratios were adjusted for age, sex, smoking status, diabetes, and body mass index (BMI). Results: Of the 731 728 participants from the ERFC, 403 396 (55.1%) were female, and the mean (SD) age at the time of the survey was 51.9 (9.0) years; of the 421 537 participants from the UK Biobank, 233 699 (55.4%) were female, and the mean (SD) age at the time of the survey was 56.4 (8.1) years. Risk factors for VTE included older age (ERFC: HR per decade, 2.67; 95% CI, 2.45-2.91; UK Biobank: HR, 1.81; 95% CI, 1.71-1.92), current smoking (ERFC: HR, 1.38; 95% CI, 1.20-1.58; UK Biobank: HR, 1.23; 95% CI, 1.08-1.40), and BMI (ERFC: HR per 1-SD higher BMI, 1.43; 95% CI, 1.35-1.50; UK Biobank: HR, 1.37; 95% CI, 1.32-1.41). For these factors, there were similar HRs for pulmonary embolism and deep vein thrombosis in UK Biobank (except adiposity was more strongly associated with pulmonary embolism) and similar HRs for unprovoked vs provoked VTE. Apart from adiposity, these risk factors were less strongly associated with VTE than CHD. There were inconsistent associations of VTEs with diabetes and blood pressure across ERFC and UK Biobank, and there was limited ability to study lipid and inflammation markers. Conclusions and Relevance: Older age, smoking, and adiposity were consistently associated with higher VTE risk.
171 citations
TL;DR: In this article, a meta-analysis of epigenome-wide association studies of 8,825 neonates from 24 birth cohorts in the Pregnancy And Childhood Epigenetics Consortium was conducted, and the authors found that DNA methylation in neonatal blood is associated with birthweight at 914 sites, with a difference in birthweight ranging from -183 to 178 grams per 10% increase in methylation.
Abstract: Birthweight is associated with health outcomes across the life course, DNA methylation may be an underlying mechanism. In this meta-analysis of epigenome-wide association studies of 8,825 neonates from 24 birth cohorts in the Pregnancy And Childhood Epigenetics Consortium, we find that DNA methylation in neonatal blood is associated with birthweight at 914 sites, with a difference in birthweight ranging from -183 to 178 grams per 10% increase in methylation (PBonferroni < 1.06 x 10-7). In additional analyses in 7,278 participants, <1.3% of birthweight-associated differential methylation is also observed in childhood and adolescence, but not adulthood. Birthweight-related CpGs overlap with some Bonferroni-significant CpGs that were previously reported to be related to maternal smoking (55/914, p = 6.12 x 10-74) and BMI in pregnancy (3/914, p = 1.13x10-3), but not with those related to folate levels in pregnancy. Whether the associations that we observe are causal or explained by confounding or fetal growth influencing DNA methylation (i.e. reverse causality) requires further research.
128 citations
01 Jan 2019
TL;DR: In this article, the authors examined the association of ranges of gestational weight gain with risk of adverse maternal and infant outcomes and estimated optimal gestational body mass index (BMI) ranges across prepregnancy body mass Index categories.
Abstract: Importance Both low and high gestational weight gain have been associated with adverse maternal and infant outcomes, but optimal gestational weight gain remains uncertain and not well defined for all prepregnancy weight ranges. Objectives To examine the association of ranges of gestational weight gain with risk of adverse maternal and infant outcomes and estimate optimal gestational weight gain ranges across prepregnancy body mass index categories. Design, Setting, and Participants Individual participant-level meta-analysis using data from 196 670 participants within 25 cohort studies from Europe and North America (main study sample). Optimal gestational weight gain ranges were estimated for each prepregnancy body mass index (BMI) category by selecting the range of gestational weight gain that was associated with lower risk for any adverse outcome. Individual participant-level data from 3505 participants within 4 separate hospital-based cohorts were used as a validation sample. Data were collected between 1989 and 2015. The final date of follow-up was December 2015. Exposures Gestational weight gain. Main Outcomes and Measures The main outcome termed any adverse outcome was defined as the presence of 1 or more of the following outcomes: preeclampsia, gestational hypertension, gestational diabetes, cesarean delivery, preterm birth, and small or large size for gestational age at birth. Results Of the 196 670 women (median age, 30.0 years [quartile 1 and 3, 27.0 and 33.0 years] and 40 937 were white) included in the main sample, 7809 (4.0%) were categorized at baseline as underweight (BMI <18.5); 133 788 (68.0%), normal weight (BMI, 18.5-24.9); 38 828 (19.7%), overweight (BMI, 25.0-29.9); 11 992 (6.1%), obesity grade 1 (BMI, 30.0-34.9); 3284 (1.7%), obesity grade 2 (BMI, 35.0-39.9); and 969 (0.5%), obesity grade 3 (BMI, ≥40.0). Overall, any adverse outcome occurred in 37.2% (n = 73 161) of women, ranging from 34.7% (2706 of 7809) among women categorized as underweight to 61.1% (592 of 969) among women categorized as obesity grade 3. Optimal gestational weight gain ranges were 14.0 kg to less than 16.0 kg for women categorized as underweight; 10.0 kg to less than 18.0 kg for normal weight; 2.0 kg to less than 16.0 kg for overweight; 2.0 kg to less than 6.0 kg for obesity grade 1; weight loss or gain of 0 kg to less than 4.0 kg for obesity grade 2; and weight gain of 0 kg to less than 6.0 kg for obesity grade 3. These gestational weight gain ranges were associated with low to moderate discrimination between those with and those without adverse outcomes (range for area under the receiver operating characteristic curve, 0.55-0.76). Results for discriminative performance in the validation sample were similar to the corresponding results in the main study sample (range for area under the receiver operating characteristic curve, 0.51-0.79). Conclusions and Relevance In this meta-analysis of pooled individual participant data from 25 cohort studies, the risk for adverse maternal and infant outcomes varied by gestational weight gain and across the range of prepregnancy weights. The estimates of optimal gestational weight gain may inform prenatal counseling; however, the optimal gestational weight gain ranges had limited predictive value for the outcomes assessed.
TL;DR: Head-to-head comparisons of four algorithms recommended by primary prevention guidelines, before and after ‘recalibration’, a method that adapts risk algorithms to take account of differences in the risk characteristics of the populations being studied suggest simple recalibration nearly equalized their performance and improved modelled targeting of preventive action to clinical need.
Abstract: AIMS: There is debate about the optimum algorithm for cardiovascular disease (CVD) risk estimation. We conducted head-to-head comparisons of four algorithms recommended by primary prevention guidelines, before and after 'recalibration', a method that adapts risk algorithms to take account of differences in the risk characteristics of the populations being studied. METHODS AND RESULTS: Using individual-participant data on 360 737 participants without CVD at baseline in 86 prospective studies from 22 countries, we compared the Framingham risk score (FRS), Systematic COronary Risk Evaluation (SCORE), pooled cohort equations (PCE), and Reynolds risk score (RRS). We calculated measures of risk discrimination and calibration, and modelled clinical implications of initiating statin therapy in people judged to be at 'high' 10 year CVD risk. Original risk algorithms were recalibrated using the risk factor profile and CVD incidence of target populations. The four algorithms had similar risk discrimination. Before recalibration, FRS, SCORE, and PCE over-predicted CVD risk on average by 10%, 52%, and 41%, respectively, whereas RRS under-predicted by 10%. Original versions of algorithms classified 29-39% of individuals aged ≥40 years as high risk. By contrast, recalibration reduced this proportion to 22-24% for every algorithm. We estimated that to prevent one CVD event, it would be necessary to initiate statin therapy in 44-51 such individuals using original algorithms, in contrast to 37-39 individuals with recalibrated algorithms. CONCLUSION: Before recalibration, the clinical performance of four widely used CVD risk algorithms varied substantially. By contrast, simple recalibration nearly equalized their performance and improved modelled targeting of preventive action to clinical need. © The Author(s) 2018. Published by Oxford University Press on behalf of the European Society of Cardiology. (Less)
Broad Institute1, Brigham and Women's Hospital2, Harvard University3, University of Exeter4, Norwegian University of Science and Technology5, University of Bristol6, Oslo University Hospital7, National Institutes of Health8, Helsinki University Central Hospital9, University of Helsinki10, Northeastern University11, University of the West Indies12, University of Manchester13, University of Oxford14, Aston University15, Massachusetts Institute of Technology16, University of Freiburg17, Case Western Reserve University18, University of Michigan19, John Radcliffe Hospital20, Manchester Academic Health Science Centre21, Beth Israel Deaconess Medical Center22
TL;DR: 42 genome-wide significant loci for self-reported daytime sleepiness in GWAS of 452,071 individuals from the UK Biobank that cluster into two biological subtypes of either sleep propensity or sleep fragmentation.
Abstract: Excessive daytime sleepiness (EDS) affects 10–20% of the population and is associated with substantial functional deficits. Here, we identify 42 loci for self-reported daytime sleepiness in GWAS of 452,071 individuals from the UK Biobank, with enrichment for genes expressed in brain tissues and in neuronal transmission pathways. We confirm the aggregate effect of a genetic risk score of 42 SNPs on daytime sleepiness in independent Scandinavian cohorts and on other sleep disorders (restless legs syndrome, insomnia) and sleep traits (duration, chronotype, accelerometer-derived sleep efficiency and daytime naps or inactivity). However, individual daytime sleepiness signals vary in their associations with objective short vs long sleep, and with markers of sleep continuity. The 42 sleepiness variants primarily cluster into two predominant composite biological subtypes - sleep propensity and sleep fragmentation. Shared genetic links are also seen with obesity, coronary heart disease, psychiatric diseases, cognitive traits and reproductive ageing. A main symptom of chronic insufficient sleep is excessive daytime sleepiness. Here, Wang et al. report 42 genome-wide significant loci for self-reported daytime sleepiness in 452,071 individuals from the UK Biobank that cluster into two biological subtypes of either sleep propensity or sleep fragmentation.
01 Jan 2019
TL;DR: A meta-analysis of epigenome-wide association studies of 8,825 neonates from 24 birth cohorts in the Pregnancy And Childhood Epigenetics Consortium finds that DNA methylation in neonatal blood is associated with birthweight at 914 sites, with a difference in birthweight ranging from −183 to 178 grams per 10% increase in methylation.
TL;DR: Findings provide some support for insulin resistance resulting in NAFLD, which in turn increases T2D risk, and genetically predicted higher circulating ALT and AST are markers of nonalcoholic fatty liver disease.
Abstract: Liver dysfunction and type 2 diabetes (T2D) are consistently associated. However, it is currently unknown whether liver dysfunction contributes to, results from, or is merely correlated with T2D due to confounding. We used Mendelian randomization to investigate the presence and direction of any causal relation between liver function and T2D risk including up to 64,094 T2D case and 607,012 control subjects. Several biomarkers were used as proxies of liver function (i.e., alanine aminotransferase [ALT], aspartate aminotransferase [AST], alkaline phosphatase [ALP], and γ-glutamyl transferase [GGT]). Genetic variants strongly associated with each liver function marker were used to investigate the effect of liver function on T2D risk. In addition, genetic variants strongly associated with T2D risk and with fasting insulin were used to investigate the effect of predisposition to T2D and insulin resistance, respectively, on liver function. Genetically predicted higher circulating ALT and AST were related to increased risk of T2D. There was a modest negative association of genetically predicted ALP with T2D risk and no evidence of association between GGT and T2D risk. Genetic predisposition to higher fasting insulin, but not to T2D, was related to increased circulating ALT. Since circulating ALT and AST are markers of nonalcoholic fatty liver disease (NAFLD), these findings provide some support for insulin resistance resulting in NAFLD, which in turn increases T2D risk.
University of Texas Health Science Center at Houston1, National Institutes of Health2, George Washington University3, University of Bristol4, University College London5, Massachusetts Institute of Technology6, Imperial College London7, Pompeu Fabra University8, University of California, San Diego9, International Agency for Research on Cancer10, Wake Forest University11, Stanford University12, Brigham and Women's Hospital13, Harvard University14, King's College London15, University of Cambridge16, University of Hawaii17, Ohio State University18, University of British Columbia19, Oregon Health & Science University20, Boston University21, Oswaldo Cruz Foundation22, St Thomas' Hospital23, University of Edinburgh24, Albert Einstein College of Medicine25, National University of Singapore26, Duke University27, Vanderbilt University28, National Institute for Health Research29, Örebro University30, Cancer Epidemiology Unit31, Huntsman Cancer Institute32, University of Texas MD Anderson Cancer Center33
TL;DR: Comparing metabolomics platforms used by COMETS cohorts showed that the overlap between any 2 different laboratories ranged from 6 to 121 metabolites at 5 leading laboratories, and the median Spearman correlation comparing 111 overlapping metabolites captured by Metabolon and the Broad Institute was 0.79.
Abstract: The Consortium of Metabolomics Studies (COMETS) was established in 2014 to facilitate large-scale collaborative research on the human metabolome and its relationship with disease etiology, diagnosis, and prognosis. COMETS comprises 47 cohorts from Asia, Europe, North America, and South America that together include more than 136,000 participants with blood metabolomics data on samples collected from 1985 to 2017. Metabolomics data were provided by 17 different platforms, with the most frequently used labs being Metabolon, Inc. (14 cohorts), the Broad Institute (15 cohorts), and Nightingale Health (11 cohorts). Participants have been followed for a median of 23 years for health outcomes including death, cancer, cardiovascular disease, diabetes, and others; many of the studies are ongoing. Available exposure-related data include common clinical measurements and behavioral factors, as well as genome-wide genotype data. Two feasibility studies were conducted to evaluate the comparability of metabolomics platforms used by COMETS cohorts. The first study showed that the overlap between any 2 different laboratories ranged from 6 to 121 metabolites at 5 leading laboratories. The second study showed that the median Spearman correlation comparing 111 overlapping metabolites captured by Metabolon and the Broad Institute was 0.79 (interquartile range, 0.56-0.89).
TL;DR: In this paper, the authors used genomic inbreeding coefficients (FROH) for >1.4 million individuals and found that FROH is significantly associated with apparently deleterious changes in 32 out of 100 traits analysed.
Abstract: In many species, the offspring of related parents suffer reduced reproductive success, a phenomenon known as inbreeding depression. In humans, the importance of this effect has remained unclear, partly because reproduction between close relatives is both rare and frequently associated with confounding social factors. Here, using genomic inbreeding coefficients (FROH) for >1.4 million individuals, we show that FROH is significantly associated (p < 0.0005) with apparently deleterious changes in 32 out of 100 traits analysed. These changes are associated with runs of homozygosity (ROH), but not with common variant homozygosity, suggesting that genetic variants associated with inbreeding depression are predominantly rare. The effect on fertility is striking: FROH equivalent to the offspring of first cousins is associated with a 55% decrease [95% CI 44-66%] in the odds of having children. Finally, the effects of FROH are confirmed within full-sibling pairs, where the variation in FROH is independent of all environmental confounding.
Erasmus University Medical Center1, Medical University of Warsaw2, University of Valencia3, University of Porto4, Stockholm County Council5, Paris Descartes University6, Sorbonne7, University of Crete8, Maastricht University9, University of Southern California10, French Institute of Health and Medical Research11, National and Kapodistrian University of Athens12, University Medical Center Groningen13, University of Southampton14, Liverpool School of Tropical Medicine15, Norwegian Institute of Public Health16, Karolinska Institutet17, University of Bologna18, University Hospital Southampton NHS Foundation Trust19, Ludwig Maximilian University of Munich20, Nofer Institute of Occupational Medicine21, University of California, Davis22, University of Illinois at Chicago23, University of Western Australia24, National Institutes of Health25, University College Cork26, University of Bristol27, University of Turku28, Helmholtz Centre for Environmental Research - UFZ29, Jagiellonian University Medical College30, Åbo Akademi University31, Harokopio University32, University College Dublin33, University of Calgary34, Public Health Research Institute35, University of Copenhagen36, University of Southern Denmark37, La Trobe University38, Harvard University39, University of Helsinki40, University of Turin41, University of Trieste42, University of Bergen43, Slovak Medical University44, Boston Children's Hospital45, Utrecht University46, Pompeu Fabra University47, Bradford Royal Infirmary48
TL;DR: Higher maternal pre-pregnancy BMI and gestational weight gain were associated with an increased risk of childhood overweight/obesity, with the strongest effects at later ages, and future intervention trials should focus on maternal weight status before pregnancy, in addition to weight gain during pregnancy.
Abstract: Background:
Maternal obesity and excessive gestational weight gain may have persistent effects on offspring fat development. However, it remains unclear whether these risks differ by severity of obesity, and whether these effects are restricted to the extremes of maternal body mass index (BMI) and gestational weight gain. We aimed to assess the separate and combined associations of maternal BMI and gestational weight gain with the risk of overweight/obesity throughout childhood, and their population impact.
Methods and Findings:
We conducted an individual participant data meta-analysis of data from 162,129 mothers and children from 37 pregnancy and birth cohort studies from Europe, North-America and Australia. We assessed the individual and combined associations of maternal pre-pregnancy BMI and gestational weight gain, both in clinical categories and across their full ranges with the risks of overweight/obesity in early- (2.0-5.0 years), mid- (5.0-10.0 years) and late childhood (10.0-18.0 years), using multilevel binary logistic regression models with a random intercept at cohort level adjusted for maternal socio-demographic and life style related characteristics. We observed that a higher maternal pre-pregnancy BMI and gestational weight gain both in clinical categories and across their full ranges were associated with higher risks of childhood overweight/obesity, with the strongest effects in late childhood (Odds Ratios (OR) for overweight/obesity in early-, mid- and late childhood, respectively: 1.66 (95% Confidence Interval (CI): 1.56, 1.78), OR 1.91 (95% CI: 1.85, 1.98), and OR 2.28 (95% CI: 2.08, 2.50) for maternal overweight, OR 2.43 (95% CI: 2.24, 2.64), OR 3.12 (95% CI: 2.98, 3.27), and OR 4.47 (95% CI: 3.99, 5.23) for maternal obesity, and OR 1.39 (95% CI: 1.30, 1.49), OR 1.55 (95% CI: 1.49, 1.60), and 1.72 (95% CI: 1.56, 1.91) for excessive gestational weight gain. The proportions of childhood overweight/obesity prevalence attributable to maternal overweight, maternal obesity and excessive gestational weight gain ranged from 10.2 to 21.6%. Relative to the effect of maternal BMI, excessive gestational weight gain only slightly increased the risk of childhood overweight/obesity within each clinical BMI category (P-values for interactions of maternal BMI with gestational weight gain: p=0.038, p<0.001 and p=0.637, in early-, mid- and late childhood, respectively). Limitations of this study include the self-report of maternal BMI and gestational weight gain for some of the cohorts, and the potential of residual confounding. Also, as this study only included participants from Europe, North-America and Australia, results need to be interpreted with caution with respect to other populations.
Conclusions:
In this study, higher maternal pre-pregnancy BMI and gestational weight gain were associated with an increased risk of childhood overweight/obesity, with the strongest effects at later ages. The additional effect of gestational weight gain in women who are overweight or obese before pregnancy is small. Given the large population impact, future intervention trials aiming to reduce the prevalence of childhood overweight and obesity should focus on maternal weight status before pregnancy, in addition to weight gain during pregnancy.
Imperial College London1, University of Surrey2, University of Cambridge3, University College London4, Erasmus University Rotterdam5, Boston Children's Hospital6, University of Queensland7, University of Western Australia8, University of London9, Children's Hospital of Philadelphia10, University of Eastern Finland11, University of Helsinki12, Ludwig Maximilian University of Munich13, University of Bristol14, University of Oulu15, University of Pennsylvania16, Statens Serum Institut17, Aarhus University18, Agency for Science, Technology and Research19, University Hospital Southampton NHS Foundation Trust20, Southampton General Hospital21, University of Copenhagen22, Harvard University23, Technische Universität München24, Paris Descartes University25, University of Canterbury26, National Institutes of Health27, Royal Devon and Exeter Hospital28, National Institute for Health Research29, University of Lincoln30, University of Southampton31, Great Ormond Street Hospital32, University of South Australia33, University of Oxford34, Karolinska Institutet35, Stanford University36, Clinical Trial Service Unit37, University of Adelaide38, University of Auckland39, King's College London40, Kingston University41, Brunel University London42, John Radcliffe Hospital43
TL;DR: A robust overlap is found between the genetics of child and adult body mass index (BMI), with variants associated with adult BMI acting as early as 4 to 6 years old, and a completely distinct genetic makeup for peak BMI during infancy is demonstrated, influenced by variation at the LEPR/LEPROT locus.
Abstract: Early childhood growth patterns are associated with adult health, yet the genetic factors and the developmental stages involved are not fully understood. Here, we combine genome-wide association studies with modeling of longitudinal growth traits to study the genetics of infant and child growth, followed by functional, pathway, genetic correlation, risk score, and colocalization analyses to determine how developmental timings, molecular pathways, and genetic determinants of these traits overlap with those of adult health. We found a robust overlap between the genetics of child and adult body mass index (BMI), with variants associated with adult BMI acting as early as 4 to 6 years old. However, we demonstrated a completely distinct genetic makeup for peak BMI during infancy, influenced by variation at the LEPR/LEPROT locus. These findings suggest that different genetic factors control infant and child BMI. In light of the obesity epidemic, these findings are important to inform the timing and targets of prevention strategies.
TL;DR: A new statistical model is created which can be used to estimate the effect of maternal genotypes on offspring outcomes conditional on offspring genotype, using both individual-level and summary-results data, even when the extent of sample overlap is unknown.
Abstract: There is considerable interest in estimating the causal effect of a range of maternal environmental exposures on offspring health-related outcomes. Previous attempts to do this using Mendelian randomization methodologies have been hampered by the paucity of epidemiological cohorts with large numbers of genotyped mother-offspring pairs. We describe a new statistical model that we have created which can be used to estimate the effect of maternal genotypes on offspring outcomes conditional on offspring genotype, using both individual-level and summary-results data, even when the extent of sample overlap is unknown. We describe how the estimates obtained from our method can subsequently be used in large-scale two-sample Mendelian randomization studies to investigate the causal effect of maternal environmental exposures on offspring outcomes. This includes studies that aim to assess the causal effect of in utero exposures related to fetal growth restriction on future risk of disease in offspring. We illustrate our framework using examples related to offspring birthweight and cardiometabolic disease, although the general principles we espouse are relevant for many other offspring phenotypes. We advocate for the establishment of large-scale international genetics consortia that are focused on the identification of maternal genetic effects and committed to the public sharing of genome-wide summary-results data from such efforts. This information will facilitate the application of powerful two-sample Mendelian randomization studies of maternal exposures and offspring outcomes.
University of Bristol1, Broad Institute2, Harvard University3, University of Exeter4, Norwegian University of Science and Technology5, Manchester Academic Health Science Centre6, University of Manchester7, International Agency for Research on Cancer8, University Hospitals Bristol NHS Foundation Trust9
TL;DR: In this article, the authors examined whether sleep traits have a causal effect on risk of breast cancer and found consistent evidence for a protective effect of morning preference and suggestive evidence for an adverse effect of increased sleep duration on breast cancer risk.
Abstract: Objective To examine whether sleep traits have a causal effect on risk of breast cancer. Design Mendelian randomisation study. Setting UK Biobank prospective cohort study and Breast Cancer Association Consortium (BCAC) case-control genome-wide association study. Participants 156 848 women in the multivariable regression and one sample mendelian randomisation (MR) analysis in UK Biobank (7784 with a breast cancer diagnosis) and 122 977 breast cancer cases and 105 974 controls from BCAC in the two sample MR analysis. Exposures Self reported chronotype (morning or evening preference), insomnia symptoms, and sleep duration in multivariable regression, and genetic variants robustly associated with these sleep traits. Main outcome measure Breast cancer diagnosis. Results In multivariable regression analysis using UK Biobank data on breast cancer incidence, morning preference was inversely associated with breast cancer (hazard ratio 0.95, 95% confidence interval 0.93 to 0.98 per category increase), whereas there was little evidence for an association between sleep duration and insomnia symptoms. Using 341 single nucleotide polymorphisms (SNPs) associated with chronotype, 91 SNPs associated with sleep duration, and 57 SNPs associated with insomnia symptoms, one sample MR analysis in UK Biobank provided some supportive evidence for a protective effect of morning preference on breast cancer risk (0.85, 0.70, 1.03 per category increase) but imprecise estimates for sleep duration and insomnia symptoms. Two sample MR using data from BCAC supported findings for a protective effect of morning preference (inverse variance weighted odds ratio 0.88, 95% confidence interval 0.82 to 0.93 per category increase) and adverse effect of increased sleep duration (1.19, 1.02 to 1.39 per hour increase) on breast cancer risk (both oestrogen receptor positive and oestrogen receptor negative), whereas evidence for insomnia symptoms was inconsistent. Results were largely robust to sensitivity analyses accounting for horizontal pleiotropy. Conclusions Findings showed consistent evidence for a protective effect of morning preference and suggestive evidence for an adverse effect of increased sleep duration on breast cancer risk.
University of Bristol1, National Institutes of Health2, Erasmus University Medical Center3, University of Helsinki4, Oslo University Hospital5, Anschutz Medical Campus6, Harvard University7, University of Southampton8, National University of Ireland, Galway9, Max Planck Society10, Emory University11, Norwegian Institute of Public Health12, University of Southern Denmark13, National Jewish Health14, Brigham and Women's Hospital15, University of Rochester Medical Center16, Columbia University17, University of Memphis18, University of Colorado Hospital19, University of Copenhagen20
TL;DR: In this paper, the association between preeclampsia and DNA methylation was found to be associated with low birth weight, shorter gestational age, and increased risk of maternal and offspring cardiovascular diseases later in life.
Abstract: Hypertensive disorders of pregnancy (HDP) are associated with low birth weight, shorter gestational age, and increased risk of maternal and offspring cardiovascular diseases later in life. The mechanisms involved are poorly understood, but epigenetic regulation of gene expression may play a part. We performed meta-analyses in the Pregnancy and Childhood Epigenetics Consortium to test the association between either maternal HDP (10 cohorts; n=5242 [cases=476]) or preeclampsia (3 cohorts; n=2219 [cases=135]) and epigenome-wide DNA methylation in cord blood using the Illumina HumanMethylation450 BeadChip. In models adjusted for confounders, and with Bonferroni correction, HDP and preeclampsia were associated with DNA methylation at 43 and 26 CpG sites, respectively. HDP was associated with higher methylation at 27 (63%) of the 43 sites, and across all 43 sites, the mean absolute difference in methylation was between 0.6% and 2.6%. Epigenome-wide associations of HDP with offspring DNA methylation were modestly consistent with the equivalent epigenome-wide associations of preeclampsia with offspring DNA methylation (R2=0.26). In longitudinal analyses conducted in 1 study (n=108 HDP cases; 550 controls), there were similar changes in DNA methylation in offspring of those with and without HDP up to adolescence. Pathway analysis suggested that genes located at/near HDP-associated sites may be involved in developmental, embryogenesis, or neurological pathways. HDP is associated with offspring DNA methylation with potential relevance to development.
TL;DR: A consistent linear dose-dependent association of maternal smoking with fetal growth was observed from the early second trimester onwards, while no major growth deficit was found in women who quit smoking early in pregnancy except for a shorter FL during late gestation.
Abstract: Background Maternal smoking during pregnancy is an established risk factor for low infant birth weight, but evidence on critical exposure windows and timing of fetal growth restriction is limited. Here we investigate the associations of maternal quitting, reducing, and continuing smoking during pregnancy with longitudinal fetal growth by triangulating evidence from 3 analytical approaches to strengthen causal inference. Methods and findings We analysed data from 8,621 European liveborn singletons in 2 population-based pregnancy cohorts (the Generation R Study, the Netherlands 2002–2006 [n = 4,682]) and the Born in Bradford study, United Kingdom 2007–2010 [n = 3,939]) with fetal ultrasound and birth anthropometric measures, parental smoking during pregnancy, and maternal genetic data. Associations with trajectories of estimated fetal weight (EFW) and individual fetal parameters (head circumference, femur length [FL], and abdominal circumference [AC]) from 12–16 to 40 weeks’ gestation were analysed using multilevel fractional polynomial models. We compared results from (1) confounder-adjusted multivariable analyses, (2) a Mendelian randomization (MR) analysis using maternal rs1051730 genotype as an instrument for smoking quantity and ease of quitting, and (3) a negative control analysis comparing maternal and mother’s partner’s smoking associations. In multivariable analyses, women who continued smoking during pregnancy had a smaller fetal size than non-smokers from early gestation (16–20 weeks) through to birth (p-value for each parameter < 0.001). Fetal size reductions in continuing smokers followed a dose-dependent pattern (compared to non-smokers, difference in mean EFW [95% CI] at 40 weeks’ gestation was −144 g [−182 to −106], −215 g [−248 to −182], and −290 g [−334 to −247] for light, moderate, and heavy smoking, respectively). Overall, fetal size reductions were most pronounced for FL. The fetal growth trajectory in women who quit smoking in early pregnancy was similar to that of non-smokers, except for a shorter FL and greater AC around 36–40 weeks’ gestation. In MR analyses, each genetically determined 1-cigarette-per-day increase was associated with a smaller EFW from 20 weeks’ gestation to birth in smokers (p = 0.01, difference in mean EFW at 40 weeks = −45 g [95% CI −81 to −10]) and a greater EFW from 32 weeks’ gestation onwards in non-smokers (p = 0.03, difference in mean EFW at 40 weeks = 26 g [95% CI 5 to 47]). There was no evidence that partner smoking was associated with fetal growth. Study limitations include measurement error due to maternal self-report of smoking and the modest sample size for MR analyses resulting in unconfounded estimates being less precise. The apparent positive association of the genetic instrument with fetal growth in non-smokers suggests that genetic pleiotropy may have masked a stronger association in smokers. Conclusions A consistent linear dose-dependent association of maternal smoking with fetal growth was observed from the early second trimester onwards, while no major growth deficit was found in women who quit smoking early in pregnancy except for a shorter FL during late gestation. These findings reinforce the importance of smoking cessation advice in preconception and antenatal care and show that smoking reduction can lower the risk of impaired fetal growth in women who struggle to quit.
01 Jan 2019
TL;DR: In this paper, the authors combine genome-wide association studies with modeling of longitudinal growth traits to study the genetics of infant and child growth, followed by functional, pathway, genetic correlation, risk score, and colocalization analyses to determine how developmental timings, molecular pathways, and genetic determinants of these traits overlap with those of adult health.
Abstract: Longitudinal data find a new variant controlling BMI in infancy and reveal genetic differences between infant and adult BMI. Early childhood growth patterns are associated with adult health, yet the genetic factors and the developmental stages involved are not fully understood. Here, we combine genome-wide association studies with modeling of longitudinal growth traits to study the genetics of infant and child growth, followed by functional, pathway, genetic correlation, risk score, and colocalization analyses to determine how developmental timings, molecular pathways, and genetic determinants of these traits overlap with those of adult health. We found a robust overlap between the genetics of child and adult body mass index (BMI), with variants associated with adult BMI acting as early as 4 to 6 years old. However, we demonstrated a completely distinct genetic makeup for peak BMI during infancy, influenced by variation at the LEPR/LEPROT locus. These findings suggest that different genetic factors control infant and child BMI. In light of the obesity epidemic, these findings are important to inform the timing and targets of prevention strategies.
Statens Serum Institut1, Lundbeck2, Mental Health Services3, Royal Devon and Exeter Hospital4, Imperial College London5, University of Gothenburg6, Wellcome Trust Centre for Human Genetics7, Northwestern University8, Erasmus University Rotterdam9, Pompeu Fabra University10, University of Copenhagen11, University of Turku12, University of Newcastle13, Sahlgrenska University Hospital14, University of Bristol15, March of Dimes16, University of Iowa17, University of Cincinnati18, Icahn School of Medicine at Mount Sinai19, University of Helsinki20, University College London21, Churchill Hospital22, University of Pittsburgh23, University of Pennsylvania24, Turku University Hospital25, Haukeland University Hospital26, Norwegian Institute of Public Health27, University of Bergen28, Lund University29, Cincinnati Children's Hospital Medical Center30, Aarhus University31, Aarhus University Hospital32, University of Tampere33, University of South Australia34, Health Protection Agency35, University of Oulu36, Stanford University37
TL;DR: A fetal genome-wide association meta-analysis is performed and it is found that a locus on chromosome 2q13 is associated with pregnancy duration and further show that the lead SNP rs7594852 changes the binding properties of transcriptional repressor HIC1.
Abstract: The duration of pregnancy is influenced by fetal and maternal genetic and non-genetic factors. Here we report a fetal genome-wide association meta-analysis of gestational duration, and early preterm, preterm, and postterm birth in 84,689 infants. One locus on chromosome 2q13 is associated with gestational duration; the association is replicated in 9,291 additional infants (combined P = 3.96 × 10-14). Analysis of 15,588 mother-child pairs shows that the association is driven by fetal rather than maternal genotype. Functional experiments show that the lead SNP, rs7594852, alters the binding of the HIC1 transcriptional repressor. Genes at the locus include several interleukin 1 family members with roles in pro-inflammatory pathways that are central to the process of parturition. Further understanding of the underlying mechanisms will be of great public health importance, since giving birth either before or after the window of term gestation is associated with increased morbidity and mortality.
TL;DR: There is a need for more robust methods to explore the role of epigenetic mechanisms as possible mediators of effects of exposure to GDM on future risk of obesity and type 2 diabetes.
Abstract: Epigenetics encapsulates a group of molecular mechanisms including DNA methylation, histone modification and microRNAs (miRNAs). Gestational diabetes (GDM) increases the risk of adverse perinatal outcomes and is associated with future offspring risk of obesity and type 2 diabetes. It has been hypothesised that epigenetic mechanisms mediate an effect of GDM on offspring adiposity and type 2 diabetes and this could provide a modifiable mechanism to reduce type 2 diabetes in the next generation. Evidence for this hypothesis is lacking. Epigenetic epidemiology could also contribute to reducing type 2 diabetes by identifying biomarkers that accurately predict risk of GDM and its associated future adverse outcomes. We reviewed published human studies that explored associations between any of maternal GDM, type 2 diabetes, gestational fasting or post-load glucose and any epigenetic marker (DNA methylation, histone modification or miRNA). Of the 81 relevant studies we identified, most focused on the potential role of epigenetic mechanisms in mediating intrauterine effects of GDM on offspring outcomes. Studies were small (median total number of participants 58; median number of GDM cases 27) and most did not attempt replication. The most common epigenetic measure analysed was DNA methylation. Most studies that aimed to explore epigenetic mediation examined associations of in utero exposure to GDM with offspring cord or infant blood/placenta DNA methylation. Exploration of any causal effect, or effect on downstream offspring outcomes, was lacking. There is a need for more robust methods to explore the role of epigenetic mechanisms as possible mediators of effects of exposure to GDM on future risk of obesity and type 2 diabetes. Research to identify epigenetic biomarkers to improve identification of women at risk of GDM and its associated adverse (maternal and offspring) outcomes is currently rare but could contribute to future tools for accurate risk stratification.
University of Bristol1, California Pacific Medical Center2, Beth Israel Deaconess Medical Center3, University of Aberdeen4, University of Dundee5, Harvard University6, Broad Institute7, Oregon Health & Science University8, University of California, Davis9, University of Western Australia10, Sir Charles Gairdner Hospital11, King's College London12, McGill University13, University of Gothenburg14, Umeå University15, University of Queensland16
TL;DR: The first genomewide association study (GWAS) meta‐analysis of dual‐energy X‐ray absorptiometry (DXA)‐derived hip shape is reported, identifying eight SNPs independently associated with hip shape that were associated with height and/or mapped close to endochondral bone formation genes, consistent with a contribution of processes involved in limb growth to hip shape and pathological sequelae.
Abstract: We aimed to report the first genomewide association study (GWAS) meta-analysis of dual-energy X-ray absorptiometry (DXA)-derived hip shape, which is thought to be related to the risk of both hip osteoarthritis and hip fracture. Ten hip shape modes (HSMs) were derived by statistical shape modeling using SHAPE software, from hip DXA scans in the Avon Longitudinal Study of Parents and Children (ALSPAC; adult females), TwinsUK (mixed sex), Framingham Osteoporosis Study (FOS; mixed), Osteoporotic Fractures in Men study (MrOS), and Study of Osteoporotic Fractures (SOF; females) (total N = 15,934). Associations were adjusted for age, sex, and ancestry. Five genomewide significant (p 0.5) were identified, which intersected with open chromatin sites as detected by ATAC-seq performed on embryonic mouse proximal femora. In conclusion, we identified eight SNPs independently associated with hip shape, most of which were associated with height and/or mapped close to endochondral bone formation genes, consistent with a contribution of processes involved in limb growth to hip shape and pathological sequelae. These findings raise the possibility that genetic studies of hip shape might help in understanding potential pathways involved in hip osteoarthritis and hip fracture. © 2018 The Authors. Journal of Bone and Mineral Research Published by Wiley Periodicals, Inc.
TL;DR: Genetic evidence for the involvement of the brain in both sweet taste perception and sugar intake is shown and genes additional to those involved in the peripheral receptor system are also associated with the sweet taste Perception and intake of sweet-tasting foods.
TL;DR: It is suggested that elevated morning cortisol is a causal risk factor for CVD and strategies targeted at lowering cortisol action should be evaluated for their effects on CVD.
Abstract: Objective: The identification of new causal risk factors has the potential to improve cardiovascular disease (CVD) risk prediction and the development of new treatments to reduce CVD deaths. In the ...
TL;DR: Smoking exposure even at low levels and intensity of alcohol use were associated individually and together with increased arterial stiffness, and public health strategies need to prevent adoption of these habits in adolescence to preserve or restore arterial health.
Abstract: Aims To determine the impact of smoking and alcohol exposure during adolescence on arterial stiffness at 17 years. Methods and results Smoking and alcohol use were assessed by questionnaires at 13, 15, and 17 years in 1266 participants (425 males and 841 females) from the ALSPAC study. Smoking status (smokers and non-smoker) and intensity ('high' ≥100, 'moderate' 20-99, and 'low or never' 10 drinks on a typical drinking day)]. Carotid to femoral pulse wave velocity (PWV) was assessed at 17 years [mean ± standard deviation and/or mean difference (95% confidence intervals)]. Current smokers had higher PWV compared with non-smokers (P = 0.003). Higher smoking exposure was associated with higher PWV compared with non-smokers [5.81 ± 0.725 vs. 5.71 ± 0.677 m/s, mean adjusted difference 0.211 (0.087-0.334) m/s, P = 0.001]. Participants who stopped smoking had similar PWV to never smokers (P = 0.160). High-intensity drinkers had increased PWV [HI 5.85 ± 0.8 vs. LI 5.67 ± 0.604 m/s, mean adjusted difference 0.266 (0.055-0.476) m/s, P = 0.013]. There was an additive effect of smoking intensity and alcohol intensity, so that 'high' smokers who were also HI drinkers had higher PWV compared with never-smokers and LI drinkers [mean adjusted increase 0.603 (0.229-0.978) m/s, P = 0.002]. Conclusion Smoking exposure even at low levels and intensity of alcohol use were associated individually and together with increased arterial stiffness. Public health strategies need to prevent adoption of these habits in adolescence to preserve or restore arterial health.