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15 results on '"692/308/174"'

1. Stunting in infancy, pubertal trajectories and adult body composition: the Birth to Twenty Plus cohort, South Africa

Author

Glory Chidumwa, Lukhanyo H. Nyati, Shane A. Norris, Rihlat Said-Mohamed, Lisa K. Micklesfield, Alessandra Prioreschi, Tinashe Chikowore, Lisa J. Ware, Juliana Kagura, Feziwe Mpondo, Said-Mohamed, Rihlat [0000-0003-1592-5412], and Apollo - University of Cambridge Repository

Subjects
Adult, Adolescent, Epidemiology, Population, Medicine (miscellaneous), 030209 endocrinology & metabolism, Adult obesity, Article, Fat mass, Body Mass Index, 03 medical and health sciences, South Africa, Young Adult, 0302 clinical medicine, Medicine, Humans, 692/308/174, 030212 general & internal medicine, Young adult, education, Child, Tanner scale, Growth Disorders, 2. Zero hunger, education.field_of_study, Nutrition and Dietetics, Anthropometry, business.industry, Infant, Paediatrics, Body Height, Child, Preschool, Cohort, Body Composition, 692/700/1720, business, Body mass index, human activities, Demography
Abstract

Background/objectives Childhood rapid growth and earlier puberty onset have been associated with adult obesity. However, the association between childhood stunting, pubertal timing and adult obesity is unclear. We examined whether the relationship between stunting at age 2 years (y) and body composition at 23 years is mediated by adolescent body mass index, and pubertal development, using the Birth-to-Twenty Plus cohort (South Africa). Subjects/methods For 1036 participants, data on anthropometrics between birth and 23 years, maternal factors, and pubertal development (Tanner scale at 9–16 years) were collected. Stunting at 2 years (height-for-age z-score Results Stunting was directly associated with slower pubertal development and with shorter adult stature, but was not associated with adolescent BMI trajectories, adult FM or FFM. However, stunting was indirectly associated with adult FM and FFM through the direct associations between slower pubertal development and lower FM and between shorter height and lower FFM. BMI trajectories were independently associated with FM and FFM. Conclusions Being stunted in this population predicted adult body composition through slower pubertal development and shorter adult stature.

Published
2020

2. Differences in diet quality and socioeconomic patterning of diet quality across ethnic groups: cross-sectional data from the HELIUS Dietary Patterns study

Author

Mary Nicolaou, Jean Adams, Amy Yau, Martin White, Public and occupational health, APH - Health Behaviors & Chronic Diseases, APH - Methodology, Yau, Amy [0000-0001-8889-523X], Adams, Jean [0000-0002-5733-7830], White, Martin [0000-0002-1861-6757], Nicolaou, Mary [0000-0002-0419-5816], and Apollo - University of Cambridge Repository

Subjects
0301 basic medicine, Adult, Male, Cross-sectional study, Turkish, Epidemiology, Occupational prestige, Ethnic group, Medicine (miscellaneous), 030209 endocrinology & metabolism, Ghana, Article, Helius, 03 medical and health sciences, 0302 clinical medicine, Ethnicity, Prevalence, Medicine, Humans, 692/308/174, Socioeconomic status, Minority Groups, Netherlands, 2. Zero hunger, Cross-sectional data, 030109 nutrition & dietetics, Nutrition and Dietetics, biology, business.industry, biology.organism_classification, 692/700/478/174, language.human_language, humanities, 3. Good health, Diet, Cross-Sectional Studies, Diet quality, Risk factors, language, Educational Status, Female, business, 692/499, Demography
Abstract

Background/objectives: Socioeconomic inequalities in diet quality are consistently reported, but few studies have investigated whether and how such inequalities vary across ethnic groups. This study aimed to examine differences in diet quality and socioeconomic patterning of diet quality across ethnic groups. Subjects/methods: Cross-sectional data from the HELIUS study were used. Dutch, South-Asian Surinamese, African Surinamese, Ghanaian, Turkish and Moroccan adults (aged 18–70 years) were randomly sampled, stratified by ethnicity. Dietary intake was estimated among a subsample (n = 4602) from 200-item, ethnic-specific food frequency questionnaires, and diet quality was assessed using the Dutch Healthy Diet Index 2015 (DHD15-Index). Wald tests were used to compare non-Dutch and Dutch participants. Adjusted linear regression models were used to examine differences in DHD15-Index by three indicators of socioeconomic position: educational level, occupational status and perceived financial difficulties. All analyses were stratified by sex. Results: Dutch participants had lower median DHD15-Index than most ethnic minority participants (P < 0.001). Lower educational level was associated with lower DHD15-Index among Dutch men (Ptrend < 0.0001), South-Asian Surinamese men (Ptrend = 0.01), Dutch women (Ptrend = 0.0001), African Surinamese women (Ptrend = 0.002) and Moroccan women (Ptrend = 0.04). Lower occupational status was associated with lower DHD15-Index in Dutch men, β −7.8 (95% CI −11.7, −3.9) and all women (β −4.4 to −8.8), except Turkish women. DHD15-Index was not associated with perceived financial difficulties in most groups. Conclusions: We observed variations in diet quality across ethnic groups. Low socioeconomic position was not consistently associated with poor diet quality in all ethnic groups. This may be due to ethnicity-specific retention of traditional diets, irrespective of socioeconomic position.

Published
2020
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3. Selecting likely causal risk factors from high-throughput experiments using multivariable Mendelian randomization

Author

Johanna M. Colijn, Verena Zuber, Caroline C W Klaver, Stephen Burgess, Zuber, Verena [0000-0001-9827-1877], Colijn, Johanna Maria [0000-0002-8324-7898], Klaver, Caroline [0000-0002-2355-5258], Burgess, Stephen [0000-0001-5365-8760], Apollo - University of Cambridge Repository, Epidemiology, and Ophthalmology

Subjects
0301 basic medicine, Statistical methods, Epidemiology, Computer science, Science, 631/208/205/2138, 45/43, Specific risk, General Physics and Astronomy, Genome-wide association study, 45/47, Bayesian inference, Genome-wide association studies, Sensory disorders Donders Center for Medical Neuroscience [Radboudumc 12], General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, Bayes' theorem, All institutes and research themes of the Radboud University Medical Center, 0302 clinical medicine, 692/53, Risk Factors, Linear regression, Statistics, Mendelian randomization, Humans, 692/308/174, 030212 general & internal medicine, lcsh:Science, Multidisciplinary, Models, Genetic, Multivariable calculus, Instrumental variable, article, Bayes Theorem, General Chemistry, 631/114/2415, Mendelian Randomization Analysis, 3. Good health, 030104 developmental biology, lcsh:Q, Biomarkers, Genome-Wide Association Study
Abstract

Modern high-throughput experiments provide a rich resource to investigate causal determinants of disease risk. Mendelian randomization (MR) is the use of genetic variants as instrumental variables to infer the causal effect of a specific risk factor on an outcome. Multivariable MR is an extension of the standard MR framework to consider multiple potential risk factors in a single model. However, current implementations of multivariable MR use standard linear regression and hence perform poorly with many risk factors. Here, we propose a two-sample multivariable MR approach based on Bayesian model averaging (MR-BMA) that scales to high-throughput experiments. In a realistic simulation study, we show that MR-BMA can detect true causal risk factors even when the candidate risk factors are highly correlated. We illustrate MR-BMA by analysing publicly-available summarized data on metabolites to prioritise likely causal biomarkers for age-related macular degeneration., Multivariable Mendelian randomization (MR) extends the standard MR framework to consider multiple risk factors in a single model. Here, Zuber et al. propose MR-BMA, a Bayesian variable selection approach to identify the likely causal determinants of a disease from many candidate risk factors as for example high-throughput data sets.

Published
2020

4. A robust and efficient method for Mendelian randomization with hundreds of genetic variants

Author

James R Staley, Christopher N. Foley, Joanna M. M. Howson, Stephen Burgess, Elias Allara, Burgess, Stephen [0000-0001-5365-8760], Allara, Elias [0000-0002-1634-8330], Howson, Joanna MM [0000-0001-7618-0050], Apollo - University of Cambridge Repository, Howson, Joanna M M [0000-0001-7618-0050], and Howson, Joanna M. M. [0000-0001-7618-0050]

Subjects
0301 basic medicine, Epidemiology, General Physics and Astronomy, Coronary Disease, Body Mass Index, Correlation, Prognostic markers, 0302 clinical medicine, Risk Factors, 692/308/174, lcsh:Science, Molecular Epidemiology, Multidisciplinary, Instrumental variable, Genetic Pleiotropy, 631/208/205, 3. Good health, Phenotype, Research Design, Mean squared error, Science, Computational biology, Biology, General Biochemistry, Genetics and Molecular Biology, Article, 631/443/592/2727, 03 medical and health sciences, 692/53/2422, Genetic variation, Mendelian randomization, Humans, Genetic Predisposition to Disease, Risk factor, Genetic Association Studies, Triglycerides, Genetic association study, Models, Genetic, Mechanism (biology), Cholesterol, HDL, Genetic Variation, Reproducibility of Results, General Chemistry, Cardiovascular genetics, Cholesterol, LDL, Mendelian Randomization Analysis, 030104 developmental biology, Diabetes Mellitus, Type 2, lcsh:Q, 030217 neurology & neurosurgery
Abstract

Mendelian randomization (MR) is an epidemiological technique that uses genetic variants to distinguish correlation from causation in observational data. The reliability of a MR investigation depends on the validity of the genetic variants as instrumental variables (IVs). We develop the contamination mixture method, a method for MR with two modalities. First, it identifies groups of genetic variants with similar causal estimates, which may represent distinct mechanisms by which the risk factor influences the outcome. Second, it performs MR robustly and efficiently in the presence of invalid IVs. Compared to other robust methods, it has the lowest mean squared error across a range of realistic scenarios. The method identifies 11 variants associated with increased high-density lipoprotein-cholesterol, decreased triglyceride levels, and decreased coronary heart disease risk that have the same directions of associations with various blood cell traits, suggesting a shared mechanism linking lipids and coronary heart disease risk mediated via platelet aggregation., Mendelian randomization (MR) is a method for inferring causal relationships between risk factors and outcomes via associated genetic variants. Here, Burgess et al. develop the contamination mixture method which yields robust MR results in the presence of invalid instrumental variables and groups variants by their effect estimates.

Published
2020

5. Sounds of COVID-19: exploring realistic performance of audio-based digital testing

Author

Han, Jing, Xia, Tong, Spathis, Dimitris, Bondareva, Erika, Brown, Chloë, Chauhan, Jagmohan, Dang, Ting, Grammenos, Andreas, Hasthanasombat, Apinan, Floto, Andres, Cicuta, Pietro, Mascolo, Cecilia, Han, Jing [0000-0001-5776-6849], Xia, Tong [0000-0002-6994-6318], Spathis, Dimitris [0000-0001-9761-951X], Cicuta, Pietro [0000-0002-9193-8496], and Apollo - University of Cambridge Repository

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Sound (cs.SD), Epidemiology, Computer applications to medicine. Medical informatics, cs.LG, R858-859.7, Medicine (miscellaneous), Health Informatics, 692/308/53, Computer Science - Sound, Machine Learning (cs.LG), 03 medical and health sciences, 0302 clinical medicine, Health Information Management, Audio and Speech Processing (eess.AS), FOS: Electrical engineering, electronic engineering, information engineering, 692/308/174, 030212 general & internal medicine, 030304 developmental biology, 0303 health sciences, article, 3. Good health, Computer Science Applications, cs.SD, eess.AS, Biomarkers, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

To identify Coronavirus disease (COVID-19) cases efficiently, affordably, and at scale, recent work has shown how audio (including cough, breathing and voice) based approaches can be used for testing. However, there is a lack of exploration of how biases and methodological decisions impact these tools’ performance in practice. In this paper, we explore the realistic performance of audio-based digital testing of COVID-19. To investigate this, we collected a large crowdsourced respiratory audio dataset through a mobile app, alongside symptoms and COVID-19 test results. Within the collected dataset, we selected 5240 samples from 2478 English-speaking participants and split them into participant-independent sets for model development and validation. In addition to controlling the language, we also balanced demographics for model training to avoid potential acoustic bias. We used these audio samples to construct an audio-based COVID-19 prediction model. The unbiased model took features extracted from breathing, coughs and voice signals as predictors and yielded an AUC-ROC of 0.71 (95% CI: 0.65–0.77). We further explored several scenarios with different types of unbalanced data distributions to demonstrate how biases and participant splits affect the performance. With these different, but less appropriate, evaluation strategies, the performance could be overestimated, reaching an AUC up to 0.90 (95% CI: 0.85–0.95) in some circumstances. We found that an unrealistic experimental setting can result in misleading, sometimes over-optimistic, performance. Instead, we reported complete and reliable results on crowd-sourced data, which would allow medical professionals and policy makers to accurately assess the value of this technology and facilitate its deployment.

Published
2022
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6. Improving local prevalence estimates of SARS-CoV-2 infections using a causal debiasing framework

Author

George Nicholson, Brieuc Lehmann, Tullia Padellini, Koen B. Pouwels, Radka Jersakova, James Lomax, Ruairidh E. King, Ann-Marie Mallon, Peter J. Diggle, Sylvia Richardson, Marta Blangiardo, Chris Holmes, Nicholson, George [0000-0001-9588-6075], Lehmann, Brieuc [0000-0002-7302-4391], Pouwels, Koen B [0000-0001-7097-8950], King, Ruairidh E [0000-0001-6733-8805], Richardson, Sylvia [0000-0003-1998-492X], Apollo - University of Cambridge Repository, Pouwels, Koen B. [0000-0001-7097-8950], and King, Ruairidh E. [0000-0001-6733-8805]

Subjects
Microbiology (medical), Epidemiology, Immunology, Basic Reproduction Number, Applied Microbiology and Biotechnology, Microbiology, 03 medical and health sciences, 0302 clinical medicine, COVID-19 Testing, Spatio-Temporal Analysis, Bias, 692/699/255/2514, 1108 Medical Microbiology, Genetics, Prevalence, Humans, 030212 general & internal medicine, 692/308/174, 030304 developmental biology, 0303 health sciences, Models, Statistical, SARS-CoV-2, article, COVID-19, Reproducibility of Results, Cell Biology, United Kingdom, 3. Good health, Viral infection, Forecasting, 0605 Microbiology
Abstract

Global and national surveillance of SARS-CoV-2 epidemiology is mostly based on targeted schemes focused on testing individuals with symptoms. These tested groups are often unrepresentative of the wider population and exhibit test positivity rates that are biased upwards compared with the true population prevalence. Such data are routinely used to infer infection prevalence and the effective reproduction number, Rt, which affects public health policy. Here, we describe a causal framework that provides debiased fine-scale spatiotemporal estimates by combining targeted test counts with data from a randomized surveillance study in the United Kingdom called REACT. Our probabilistic model includes a bias parameter that captures the increased probability of an infected individual being tested, relative to a non-infected individual, and transforms observed test counts to debiased estimates of the true underlying local prevalence and Rt. We validated our approach on held-out REACT data over a 7-month period. Furthermore, our local estimates of Rt are indicative of 1-week- and 2-week-ahead changes in SARS-CoV-2-positive case numbers. We also observed increases in estimated local prevalence and Rt that reflect the spread of the Alpha and Delta variants. Our results illustrate how randomized surveys can augment targeted testing to improve statistical accuracy in monitoring the spread of emerging and ongoing infectious disease., A causal debiasing framework provides accurate estimates of local prevalence and effective reproduction number for surveillance of SARS-CoV-2 cases using data from randomized testing schemes to model ascertainment bias in targeted subpopulation data.

Published
2021
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7. Comparative performance of lung cancer risk models to define lung screening eligibility in the United Kingdom

Author

Ruth C. Travis, Philip A.J. Crosbie, Rebecca Landy, David R Baldwin, Karine Alcala, Anthony J. Swerdlow, Mattias Johansson, Matthew E.J. Callister, Minouk J. Schoemaker, Hilary A. Robbins, Nicholas J. Wareham, Robbins, Hilary A. [0000-0001-6041-6866], Alcala, Karine [0000-0003-2308-9880], Johansson, Mattias [0000-0002-3116-5081], Apollo - University of Cambridge Repository, and Robbins, Hilary A [0000-0001-6041-6866]

Subjects
Male, Cancer Research, Lung Neoplasms, Epidemiology, 631/67/1612, Risk prediction models, State Medicine, Cohort Studies, 0302 clinical medicine, Risk Factors, 030212 general & internal medicine, 692/308/174, Early Detection of Cancer, United Kingdom/epidemiology, article, Middle Aged, Biobank, medicine.anatomical_structure, Oncology, 030220 oncology & carcinogenesis, Calibration, Female, Lung cancer, Adult, medicine.medical_specialty, Lung Neoplasms/diagnosis, MEDLINE, 631/67/2324, Risk Assessment, Cancer screening, 03 medical and health sciences, Cancer epidemiology, 692/699/67/1612, Predictive Value of Tests, Internal medicine, 631/67/2322, medicine, Humans, Socioeconomic status, Aged, Lung, Models, Statistical, business.industry, Patient Selection, Correction, medicine.disease, National health service, United Kingdom, Social Class, Early Detection of Cancer/methods, business, Lung cancer screening
Abstract

Background The National Health Service England (NHS) classifies individuals as eligible for lung cancer screening using two risk prediction models, PLCOm2012 and Liverpool Lung Project-v2 (LLPv2). However, no study has compared the performance of lung cancer risk models in the UK. Methods We analysed current and former smokers aged 40–80 years in the UK Biobank (N = 217,199), EPIC-UK (N = 30,813), and Generations Study (N = 25,777). We quantified model calibration (ratio of expected to observed cases, E/O) and discrimination (AUC). Results Risk discrimination in UK Biobank was best for the Lung Cancer Death Risk Assessment Tool (LCDRAT, AUC = 0.82, 95% CI = 0.81–0.84), followed by the LCRAT (AUC = 0.81, 95% CI = 0.79–0.82) and the Bach model (AUC = 0.80, 95% CI = 0.79–0.81). Results were similar in EPIC-UK and the Generations Study. All models overestimated risk in all cohorts, with E/O in UK Biobank ranging from 1.20 for LLPv3 (95% CI = 1.14–1.27) to 2.16 for LLPv2 (95% CI = 2.05–2.28). Overestimation increased with area-level socioeconomic status. In the combined cohorts, USPSTF 2013 criteria classified 50.7% of future cases as screening eligible. The LCDRAT and LCRAT identified 60.9%, followed by PLCOm2012 (58.3%), Bach (58.0%), LLPv3 (56.6%), and LLPv2 (53.7%). Conclusion In UK cohorts, the ability of risk prediction models to classify future lung cancer cases as eligible for screening was best for LCDRAT/LCRAT, very good for PLCOm2012, and lowest for LLPv2. Our results highlight the importance of validating prediction tools in specific countries.

Published
2021
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8. Genome-wide association analysis of type 2 diabetes in the EPIC-InterAct study

Author

Peter M. Nilsson, Olov Rolandsson, Eleanor Wheeler, Nicholas J. Wareham, Nita G. Forouhi, Matthias B. Schulze, Kim Overvad, Claudia Langenberg, Nicola D. Kerrison, Pilar Amiano, Catalina Bonet, Mark I. McCarthy, Inês Barroso, Yvonne T. van der Schouw, Lina Cai, Giovanna Masala, José María Huerta, Eva Ardanaz, Anne Tjønneland, Miguel Rodríguez-Barranco, Panos Deloukas, Leif Groop, Guy Fagherazzi, Jian'an Luan, Valeria Pala, Elio Riboli, Carlotta Sacerdote, Rosario Tumino, Paul W. Franks, Annemieke M.W. Spijkerman, Stephen J. Sharp, Salvatore Panico, Rudolf Kaaks, Cai, Lina [0000-0003-2598-8388], Wheeler, Eleanor [0000-0002-8616-6444], Luan, Jian'an [0000-0003-3137-6337], Deloukas, Panos [0000-0001-9251-070X], Groop, Leif C [0000-0002-0187-3263], Huerta, José María [0000-0002-9637-3869], Masala, Giovanna [0000-0002-5758-9069], Nilsson, Peter M [0000-0002-5652-8459], Overvad, Kim [0000-0001-6429-7921], Schulze, Matthias B [0000-0002-0830-5277], Tumino, Rosario [0000-0003-2666-414X], van der Schouw, Yvonne T [0000-0002-4605-435X], Forouhi, Nita G [0000-0002-5041-248X], Barroso, Inês [0000-0001-5800-4520], Langenberg, Claudia [0000-0002-5017-7344], Apollo - University of Cambridge Repository, Luan, Jian’an [0000-0003-3137-6337], Groop, Leif C. [0000-0002-0187-3263], Nilsson, Peter M. [0000-0002-5652-8459], Schulze, Matthias B. [0000-0002-0830-5277], van der Schouw, Yvonne T. [0000-0002-4605-435X], and Forouhi, Nita G. [0000-0002-5041-248X]

Subjects
Gerontology, Data Descriptor, endocrine system diseases, Epidemiology, Type 2 diabetes, 0302 clinical medicine, Risk Factors, Genetics research, 692/699/2743/137/773, 692/308/174, Prospective Studies, lcsh:Science, Prospective cohort study, 0303 health sciences, Diabetis, Diabetes, Computer Science Applications, Europe, Cardiovascular diseases, Endokrinologi och diabetes, Cohort, Statistics, Probability and Uncertainty, data-descriptor, Information Systems, Statistics and Probability, medicine.medical_specialty, MEDLINE, 030209 endocrinology & metabolism, Single-nucleotide polymorphism, Endocrinology and Diabetes, Library and Information Sciences, Education, 03 medical and health sciences, medicine, Mortalitat, Humans, Mortality, Life Style, 030304 developmental biology, Genetic association, business.industry, Malalties cardiovasculars, Public health, 692/308/2056, nutritional and metabolic diseases, medicine.disease, Summary statistics, Diabetes Mellitus, Type 2, lcsh:Q, business, Genome-Wide Association Study
Abstract

Funder: European Union FP6 programme grant number LSHM_CT_2006_037197, Type 2 diabetes (T2D) is a global public health challenge. Whilst the advent of genome-wide association studies has identified >400 genetic variants associated with T2D, our understanding of its biological mechanisms and translational insights is still limited. The EPIC-InterAct project, centred in 8 countries in the European Prospective Investigations into Cancer and Nutrition study, is one of the largest prospective studies of T2D. Established as a nested case-cohort study to investigate the interplay between genetic and lifestyle behavioural factors on the risk of T2D, a total of 12,403 individuals were identified as incident T2D cases, and a representative sub-cohort of 16,154 individuals was selected from a larger cohort of 340,234 participants with a follow-up time of 3.99 million person-years. We describe the results from a genome-wide association analysis between more than 8.9 million SNPs and T2D risk among 22,326 individuals (9,978 cases and 12,348 non-cases) from the EPIC-InterAct study. The summary statistics to be shared provide a valuable resource to facilitate further investigations into the genetics of T2D.

Published
2020
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9. Independent influences of maternal obesity and fetal sex on maternal cardiovascular adaptation to pregnancy: a prospective cohort study

Author

Gordon C. S. Smith, Ulla Sovio, Angela M. Wood, Susan E. Ozanne, Noor E. W. D. Teulings, Catherine E. Aiken, Teulings, Noor E. W. D. [0000-0001-8176-179X], Sovio, Ulla [0000-0002-0799-1105], Apollo - University of Cambridge Repository, and Teulings, Noor EWD [0000-0001-8176-179X]

Subjects
Adult, Male, medicine.medical_specialty, Epidemiology, Endocrinology, Diabetes and Metabolism, Placenta, Medicine (miscellaneous), 030204 cardiovascular system & hematology, Overweight, Umbilical Arteries, Body Mass Index, Obesity, Maternal, 03 medical and health sciences, Young Adult, 0302 clinical medicine, Sex Factors, Pregnancy, Fetal sex, medicine, Humans, Prospective Studies, 692/308/174, Prospective cohort study, Fetus, 030219 obstetrics & reproductive medicine, Nutrition and Dietetics, Obstetrics, business.industry, article, Ultrasonography, Doppler, medicine.disease, Obesity, Adaptation, Physiological, 692/699/75, Sexual dimorphism, Uterine Artery, Cardiovascular diseases, Risk factors, Pulsatile Flow, Gestation, Female, Vascular Resistance, medicine.symptom, business, 692/499
Abstract

Background/Objectives Successful pregnancy requires the de novo creation of low-resistance utero-placental and feto-placental circulations and incomplete remodeling of this vasculature can lead to maternal or fetal compromise. Maternal BMI and fetal sex are known to influence vascular compliance and placental development, but it is unknown if these are independent or synergistic effects. Here we aim to investigate the impact of maternal obesity, fetal sex, and any interaction thereof on maternal cardiovascular adaptation to pregnancy, by assessing the physiological drop of uterine artery doppler pulsatility (UtA-PI) and umbilical artery doppler pulsatility index (UA-PI) over gestation. Subjects/Methods Nulliparous women with a singleton pregnancy participating in a prospective cohort study (n = 4212) underwent serial UtA-PI and UA-PI measurements at 20-, 28- and 36-weeks gestation. Linear mixed regression models were employed to investigate the influence of maternal BMI, fetal sex and interactions thereof on the magnitude of change in UtA-PI and UA-PI. Results Throughout gestation, UtA-PI was higher for male fetuses and UA-PI was higher for female fetuses. The physiological drop of UtA-PI was significantly smaller in overweight (change −24.3% [95%CI −22.3, −26.2]) and obese women (change −21.3% [−18.3, −24.3]), compared to normal-weight women (change −25.7% [−24.3, −27.0]) but did not differ by fetal sex. The physiological drop in UA-PI was greater for female than male fetuses (–32.5% [−31.5, −33.5] vs. −30.7% [−29.8, −31.7]) but did not differ by maternal BMI. No interactions between maternal BMI and fetal sex were found. Conclusions Maternal cardiovascular adaptation to pregnancy is independently associated with maternal BMI and fetal sex. Our results imply sexual dimorphism in both maternal cardiovascular adaptation and feto-placental resistance.

Published
2020
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10. Appetite disinhibition rather than hunger explains genetic effects on adult BMI trajectory

Author

Archana Singh-Manoux, Koutatsu Maruyama, Adam G. Tabak, David Boniface, Noriko Cable, Daryth Stallone, Hiroyasu Iso, Ayako Hiyoshi, Meena Kumari, Aroon D. Hingorani, Claudia Langenberg, Eric J. Brunner, Nicholas J. Wareham, Mika Kivimäki, Martin J. Shipley, Clare H. Llewellyn, John Wilson, Brunner, Eric J. [0000-0002-0595-4474], Tabak, Adam [0000-0002-6234-3936], Singh-Manoux, Archana [0000-0002-1244-5037], Hingorani, Aroon [0000-0001-8365-0081], Kivimäki, Mika [0000-0002-4699-5627], Llewellyn, Clare [0000-0002-0066-2827], Apollo - University of Cambridge Repository, and Brunner, Eric J [0000-0002-0595-4474]

Subjects
Male, Lifestyle modification, Longitudinal study, Hunger, Epidemiology, Endocrinology, Diabetes and Metabolism, media_common.quotation_subject, Population, Medicine (miscellaneous), Appetite, 030209 endocrinology & metabolism, Weight Gain, Polymorphism, Single Nucleotide, Body Mass Index, 03 medical and health sciences, 0302 clinical medicine, Genetic predisposition, Medicine, Humans, Genetic Predisposition to Disease, 692/308/174, 030212 general & internal medicine, Longitudinal Studies, Obesity, Overeating, education, media_common, Aged, education.field_of_study, Nutrition and Dietetics, business.industry, 692/700/459/284, article, Correction, Feeding Behavior, Middle Aged, medicine.disease, Inhibition, Psychological, Disinhibition, Linear Models, Female, medicine.symptom, business, Weight gain, Demography
Abstract

Background/objectives The mediating role of eating behaviors in genetic susceptibility to weight gain during mid-adult life is not fully understood. This longitudinal study aims to help us understand contributions of genetic susceptibility and appetite to weight gain. Subjects/methods We followed the body-mass index (BMI) trajectories of 2464 adults from 45 to 65 years of age by measuring weight and height on four occasions at 5-year intervals. Genetic risk of obesity (gene risk score: GRS) was ascertained, comprising 92 BMI-associated single-nucleotide polymorphisms and split at a median (=high and low risk). At the baseline, the Eating Inventory was used to assess appetite-related traits of ‘disinhibition’, indicative of opportunistic eating or overeating and ‘hunger’ which is susceptibility to/ability to cope with the sensation of hunger. Roles of the GRS and two appetite-related scores for BMI trajectories were examined using a mixed model adjusted for the cohort effect and sex. Results Disinhibition was associated with higher BMI (β = 2.96; 95% CI: 2.66–3.25 kg/m2), and accounted for 34% of the genetically-linked BMI difference at age 45. Hunger was also associated with higher BMI (β = 1.20; 0.82–1.59 kg/m2) during mid-life and slightly steeper weight gain, but did not attenuate the effect of disinhibition. Conclusions Appetite disinhibition is most likely to be a defining characteristic of genetic susceptibility to obesity. High levels of appetite disinhibition, rather than hunger, may underlie genetic vulnerability to obesogenic environments in two-thirds of the population of European ancestry.

Published
2020

11. Epidemiological data from the COVID-19 outbreak, real-time case information

Author

Chieh-Hsi Wu, Sarah C. Hill, Oliver G. Pybus, David M. Pigott, Bernardo Gutierrez, Moritz U. G. Kraemer, Lauren Goodwin, Kara Sewalk, Alexander E. Zarebski, John S. Brownstein, Yulin Hswen, Bo Xu, Samuel V. Scarpino, María Mercedes Cobo, Erin Hulland, Lin Wang, Emily Cohn, Katelynn O’Brien, Alyssa Loskill, Sabrina Li, Sumiko R. Mekaru, Julia D. Morgan, Hswen, Yulin [0000-0003-3203-1322], Pigott, David M. [0000-0002-6731-4034], Kraemer, Moritz U. G. [0000-0001-8838-7147], Apollo - University of Cambridge Repository, Pigott, David M [0000-0002-6731-4034], and Kraemer, Moritz UG [0000-0001-8838-7147]

Subjects
Data Descriptor, Epidemiology, Geographic Mapping, Disease, 0302 clinical medicine, Pandemic, 692/308/174, Viral, 030212 general & internal medicine, lcsh:Science, 0303 health sciences, Geography, Transmission (medicine), Computer Science Applications, Infectious Diseases, Public Health, Statistics, Probability and Uncertainty, data-descriptor, Coronavirus Infections, 692/499, Information Systems, Statistics and Probability, medicine.medical_specialty, China, Viral epidemiology, Pneumonia, Viral, MEDLINE, Library and Information Sciences, COVID-19, Viral infection, Risk factors, Education, 03 medical and health sciences, Betacoronavirus, 692/699/255/2514, Clinical Research, Environmental health, medicine, Humans, Climate-Related Exposures and Conditions, Epidemics, Pandemics, 030304 developmental biology, SARS-CoV-2, Public health, Outbreak, Pneumonia, Emerging Infectious Diseases, Good Health and Well Being, 631/326/596/2562, lcsh:Q
Abstract

Cases of a novel coronavirus were first reported in Wuhan, Hubei province, China, in December 2019 and have since spread across the world. Epidemiological studies have indicated human-to-human transmission in China and elsewhere. To aid the analysis and tracking of the COVID-19 epidemic we collected and curated individual-level data from national, provincial, and municipal health reports, as well as additional information from online reports. All data are geo-coded and, where available, include symptoms, key dates (date of onset, admission, and confirmation), and travel history. The generation of detailed, real-time, and robust data for emerging disease outbreaks is important and can help to generate robust evidence that will support and inform public health decision making., Measurement(s)coronavirus infectious disease • Viral EpidemiologyTechnology Type(s)digital curationFactor Type(s)geolocation • date • travel history • age • sexSample Characteristic - OrganismHomo sapiens • SARS-CoV-2 • Betacoronavirus Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11974344

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2020
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12. Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure

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Shah, Sonia, Henry, Albert, Roselli, Carolina, Lin, Honghuang, Sveinbjornsson, Gardar, Fatemifar, Ghazaleh, Hedman, Asa K, Wilk, Jemma B, Morley, Michael P, Chaffin, Mark D, Helgadottir, Anna, Verweij, Niek, Dehghan, Abbas, Almgren, Peter, Andersson, Charlotte, Aragam, Krishna G, Arnlov, Johan, Backman, Joshua D, Biggs, Mary L, Bloom, Heather L, Brandimarto, Jeffrey, Brown, Michael R, Buckbinder, Leonard, Carey, David J, Chasman, Daniel I, Chen, Xing, Chen, Xu, Chung, Jonathan, Chutkow, William, Cook, James P, Delgado, Graciela E, Denaxas, Spiros, Doney, Alexander S, Doerr, Marcus, Dudley, Samuel C, Dunn, Michael E, Engstrom, Gunnar, Esko, Tonu, Felix, Stephan B, Finan, Chris, Ford, Ian, Ghanbari, Mohsen, Ghasemi, Sahar, Giedraitis, Vilmantas, Giulianini, Franco, Gottdiener, John S, Gross, Stefan, Gudbjartsson, Daniel F, Gutmann, Rebecca, Haggerty, Christopher M, van der Harst, Pim, Hyde, Craig L, Ingelsson, Erik, Jukema, J Wouter, Kavousi, Maryam, Khaw, Kay-Tee, Kleber, Marcus E, Kober, Lars, Koekemoer, Andrea, Langenberg, Claudia, Lind, Lars, Lindgren, Cecilia M, London, Barry, Lotta, Luca A, Lovering, Ruth C, Luan, Jian'an, Magnusson, Patrik, Mahajan, Anubha, Margulies, Kenneth B, Maerz, Winfried, Melander, Olle, Mordi, Ify R, Morgan, Thomas, Morris, Andrew D, Morris, Andrew P, Morrison, Alanna C, Nagle, Michael W, Nelson, Christopher P, Niessner, Alexander, Niiranen, Teemu, O'Donoghue, Michelle L, Owens, Anjali T, Palmer, Colin NA, Parry, Helen M, Perola, Markus, Portilla-Fernandez, Eliana, Psaty, Bruce M, Rice, Kenneth M, Ridker, Paul M, Romaine, Simon PR, Rotter, Jerome I, Salo, Perttu, Salomaa, Veikko, van Setten, Jessica, Shalaby, Alaa A, Smelser, Diane T, Smith, Nicholas L, Stender, Steen, Stott, David J, Svensson, Per, Tammesoo, Mari-Liis, Taylor, Kent D, Teder-Laving, Maris, Teumer, Alexander, Thorgeirsson, Gudmundur, Thorsteinsdottir, Unnur, Torp-Pedersen, Christian, Trompet, Stella, Tyl, Benoit, Uitterlinden, Andre G, Veluchamy, Abirami, Voelker, Uwe, Voors, Adriaan A, Wang, Xiaosong, Wareham, Nicholas J, Waterworth, Dawn, Weeke, Peter E, Weiss, Raul, Wiggins, Kerri L, Xing, Heming, Yerges-Armstrong, Laura M, Yu, Bing, Zannad, Faiez, Zhao, Jing Hua, Hemingway, Harry, Samani, Nilesh J, McMurray, John JV, Yang, Jian, Visscher, Peter M, Newton-Cheh, Christopher, Malarstig, Anders, Holm, Hilma, Lubitz, Steven A, Sattar, Naveed, Holmes, Michael V, Cappola, Thomas P, Asselbergs, Folkert W, Hingorani, Aroon D, Kuchenbaecker, Karoline, Ellinor, Patrick T, Lang, Chim C, Stefansson, Kari, Smith, J Gustav, Vasan, Ramachandran S, Swerdlow, Daniel I, Lumbers, R Thomas, Abecasis, Goncalo, Backman, Joshua, Bai, Xiaodong, Balasubramanian, Suganthi, Banerjee, Nilanjana, Baras, Aris, Barnard, Leland, Beechert, Christina, Blumenfeld, Andrew, Cantor, Michael, Chai, Yating, Coppola, Giovanni, Damask, Amy, Dewey, Frederick, Economides, Aris, Eom, Gisu, Forsythe, Caitlin, Fuller, Erin D, Gu, Zhenhua, Gurski, Lauren, Guzzardo, Paloma M, Habegger, Lukas, Hahn, Young, Hawes, Alicia, van Hout, Cristopher, Jones, Marcus B, Khalid, Shareef, Lattari, Michael, Li, Alexander, Lin, Nan, Liu, Daren, Lopez, Alexander, Manoochehri, Kia, Marchini, Jonathan, Marcketta, Anthony, Maxwell, Evan K, McCarthy, Shane, Mitnaul, Lyndon J, O'Dushlaine, Colm, Overton, John D, Padilla, Maria Sotiropoulos, Paulding, Charles, Penn, John, Pradhan, Manasi, Reid, Jeffrey G, Schleicher, Thomas D, Schurmann, Claudia, Shuldiner, Alan, Staples, Jeffrey C, Sun, Dylan, Toledo, Karina, Ulloa, Ricardo H, Widom, Louis, Wolf, Sarah E, Yadav, Ashish, Ye, Bin, Ctr, Regeneron Genetics, Shah, Sonia [0000-0001-5860-4526], Henry, Albert [0000-0001-7422-2288], Roselli, Carolina [0000-0001-5267-6756], Lin, Honghuang [0000-0003-3043-3942], Chaffin, Mark D. [0000-0002-1234-5562], Helgadottir, Anna [0000-0002-1806-2467], Verweij, Niek [0000-0002-4303-7685], Almgren, Peter [0000-0002-0473-0241], Chen, Xu [0000-0002-7299-3238], Ghanbari, Mohsen [0000-0002-9476-7143], Giedraitis, Vilmantas [0000-0003-3423-2021], Gross, Stefan [0000-0003-4121-7161], Guðbjartsson, Daníel F. [0000-0002-5222-9857], Hyde, Craig L. [0000-0002-6939-287X], Ingelsson, Erik [0000-0003-2256-6972], Jukema, J. Wouter [0000-0002-3246-8359], Kleber, Marcus E. [0000-0003-0663-7275], Koekemoer, Andrea [0000-0001-8222-3547], Langenberg, Claudia [0000-0002-5017-7344], Lindgren, Cecilia M. [0000-0002-4903-9374], Lovering, Ruth C. [0000-0002-9791-0064], Luan, Jian’an [0000-0003-3137-6337], Magnusson, Patrik [0000-0002-7315-7899], Mahajan, Anubha [0000-0001-5585-3420], Mordi, Ify R. [0000-0002-2686-729X], Morris, Andrew D. [0000-0002-1766-0473], Nagle, Michael W. [0000-0002-4677-7582], Nelson, Christopher P. [0000-0001-8025-2897], Palmer, Colin N. A. [0000-0002-6415-6560], Rice, Kenneth M. [0000-0002-3071-7278], Rotter, Jerome I. [0000-0001-7191-1723], Salomaa, Veikko [0000-0001-7563-5324], van Setten, Jessica [0000-0002-4934-7510], Svensson, Per [0000-0003-0372-6272], Taylor, Kent D. [0000-0002-2756-4370], Teder-Laving, Maris [0000-0002-5872-1850], Teumer, Alexander [0000-0002-8309-094X], Tyl, Benoit [0000-0001-5297-8412], Uitterlinden, Andre G. [0000-0002-7276-3387], Völker, Uwe [0000-0002-5689-3448], Wiggins, Kerri L. [0000-0003-2749-1279], Hemingway, Harry [0000-0003-2279-0624], Yang, Jian [0000-0003-2001-2474], Visscher, Peter M. [0000-0002-2143-8760], Lubitz, Steven A. [0000-0002-9599-4866], Sattar, Naveed [0000-0002-1604-2593], Cappola, Thomas P. [0000-0002-9630-7204], Asselbergs, Folkert W. [0000-0002-1692-8669], Kuchenbaecker, Karoline [0000-0001-9726-603X], Ellinor, Patrick T. [0000-0002-2067-0533], Vasan, Ramachandran S. [0000-0001-7357-5970], Lumbers, R. Thomas [0000-0002-9077-4741], Apollo - University of Cambridge Repository, Chaffin, Mark D [0000-0002-1234-5562], Guðbjartsson, Daníel F [0000-0002-5222-9857], Hyde, Craig L [0000-0002-6939-287X], Jukema, J Wouter [0000-0002-3246-8359], Kleber, Marcus E [0000-0003-0663-7275], Lindgren, Cecilia M [0000-0002-4903-9374], Lovering, Ruth C [0000-0002-9791-0064], Luan, Jian'an [0000-0003-3137-6337], Mordi, Ify R [0000-0002-2686-729X], Morris, Andrew D [0000-0002-1766-0473], Nagle, Michael W [0000-0002-4677-7582], Nelson, Christopher P [0000-0001-8025-2897], Palmer, Colin NA [0000-0002-6415-6560], Rice, Kenneth M [0000-0002-3071-7278], Rotter, Jerome I [0000-0001-7191-1723], Taylor, Kent D [0000-0002-2756-4370], Uitterlinden, Andre G [0000-0002-7276-3387], Wiggins, Kerri L [0000-0003-2749-1279], Visscher, Peter M [0000-0002-2143-8760], Lubitz, Steven A [0000-0002-9599-4866], Cappola, Thomas P [0000-0002-9630-7204], Asselbergs, Folkert W [0000-0002-1692-8669], Ellinor, Patrick T [0000-0002-2067-0533], Vasan, Ramachandran S [0000-0001-7357-5970], Lumbers, R Thomas [0000-0002-9077-4741], Palmer, Colin N A [0000-0002-6415-6560], Cardiovascular Centre (CVC), University of Queensland [Brisbane], University College of London [London] (UCL), Broad Institute of MIT and Harvard (BROAD INSTITUTE), Harvard Medical School [Boston] (HMS)-Massachusetts Institute of Technology (MIT)-Massachusetts General Hospital [Boston], University Medical Center Groningen [Groningen] (UMCG), Boston University School of Medicine (BUSM), Boston University [Boston] (BU), Framingham Heart Study, National Heart, Lung, and Blood Institute [Bethesda] (NHLBI)-Boston University [Boston] (BU), deCODE genetics [Reykjavik], Karolinska Institutet [Stockholm], Pfizer, University of Pennsylvania [Philadelphia], University of Groningen [Groningen], Imperial College London, Lund University [Lund], Herlev and Gentofte Hospital, Massachusetts General Hospital [Boston], Department of Neurobiology, Care Sciences and Society [Stockholm, Sweden] (Division of Family Medicine), Dalarna University, Regeneron Genetics Center, 777 Old Saw Mill River Road, Tarrytown, Department of Biostatistics, University of Washington [Seattle], Emory University School of Medicine, Emory University [Atlanta, GA], The University of Texas Medical School at Houston, Department of Molecular and Functional Genomics, Geisinger, Danville, PA, Brigham and Women's Hospital [Boston], Harvard Medical School [Boston] (HMS), Regeneron Genetics Center, 777 Old Saw Mill River Road, Tarrytown, NY, Novartis Institutes for BioMedical Research (NIBR), University of Liverpool, Universität Heidelberg [Heidelberg], Medizinische Fakultät Mannheim, The Alan Turing Institute, Ninewells Hospital and Medical School [Dundee], Universität Greifswald - University of Greifswald, German Center for Cardiovascular Research (DZHK), Berlin Institute of Health (BIH), University of Minnesota System, Regeneron Pharmaceuticals [Tarrytown], Department of Clinical Sciences, Cardiovascular Epidemiology, Skane University Hospital [Lund], Institute of Genomics [Tartu, Estonia], University of Tartu, Robertson Centre for Biostatistics, University of Glasgow, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, Erasmus University Medical Center [Rotterdam] (Erasmus MC), Uppsala University, Brigham & Women’s Hospital [Boston] (BWH), University of Maryland School of Medicine, University of Maryland System, School of Science and Engineering (Reykjavik University), Carver College of Medicine, University of Iowa, Geisinger Health System [Danville, PA, USA], Durrer Center for Cardiogenetic Research, ICIN-Netherlands Heart Institute, Utrecht, Stanford School of Medicine [Stanford], Stanford Medicine, Stanford University-Stanford University, Stanford Cardiovascular Institute, Uppsala Universitet [Uppsala], Leiden University Medical Center (LUMC), Einthoven Laboratory for Experimental Vascular Medicine (ELEVM - LEIDEN), Department of Public Health and Primary Care, University of Cambridge [UK] (CAM), Rigshospitalet [Copenhagen], Copenhagen University Hospital, Glenfield Hospital, University Hospitals Leicester, MRC Epidemiology Unit, University of Cambridge [UK] (CAM)-Institute of Metabolic Science, Big Data Institute, University of Oxford [Oxford], University of Iowa [Iowa City], The Wellcome Trust Centre for Human Genetics [Oxford], Synlab Academy, Synlab Holding Deutschland GmbH, Mannheim, Medical University Graz, Skane University Hospital [Malmo], Vanderbilt University School of Medicine [Nashville], University of Edinburgh, Université médicale de Vienne, Autriche, National Institute for Health and Welfare [Helsinki], University of Turku, Birmingham Women's and Children's NHS Foundation Trust, Kaiser Permanente, Harbor UCLA Medical Center [Torrance, Ca.], Los Angeles Biomedical Research Institute (LA BioMed), University Medical Center [Utrecht], University of Pittsburgh (PITT), Pennsylvania Commonwealth System of Higher Education (PCSHE), Seattle Epidemiologic Research and Information Center [Seattle], Institute of Cardiovascular and Medical Sciences [Glasgow], University of Glasgow, Department of Cardiology, Södersjukhuset, Stockholm, Estonian Genome and Medicine, Landspitali National University Hospital of Iceland, University of Iceland [Reykjavik], Aalborg University [Denmark] (AAU), Institut de Recherches SERVIER (IRS), Interfaculty Institute for Genetics and Functional Genomics, Ernst-Moritz-Arndt-Universität Greifswald, GlaxoSmithKline, Glaxo Smith Kline, Northeastern Ohio Medical University (NEOMED), Centre d'investigation clinique plurithématique Pierre Drouin [Nancy] (CIC-P), Centre d'investigation clinique [Nancy] (CIC), Université de Lorraine (UL)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM), Défaillance Cardiovasculaire Aiguë et Chronique (DCAC), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Cardiovascular and Renal Clinical Trialists [Vandoeuvre-les-Nancy] (INI-CRCT), Institut Lorrain du Coeur et des Vaisseaux Louis Mathieu [Nancy], French-Clinical Research Infrastructure Network - F-CRIN [Paris] (Cardiovascular & Renal Clinical Trialists - CRCT ), MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, British Heart Foundation Glasgow Cardiovascular Research Centre (BHF GCRC), University of Glasgow-NHS Greater Glasgow and Clyde, Department of Cardiovascular Sciences [Leicester], University of Leicester, Queensland Brain Institute, Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, University of Dundee, National Heart and Lung Institute [London] (NHLI), Imperial College London-Royal Brompton and Harefield NHS Foundation Trust, Atherosclerosis Risk in Communities Study (ARIC)The Atherosclerosis Risk in Communities Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts N01-HC-55015, N01-HC- 55016, N01-HC-55018, N01-HC-55019, N01-HC-55020, N01-HC55021, N01-HC-55022, R01HL087641, R01HL59367, R01HL086694 and RC2 HL102419, National Human Genome Research Institute contract U01HG004402, and National Institutes of Health contract HHSN268200625226C. The authors thank the staff and participants of the ARIC study for their important contributions. Infrastructure was partly supported by Grant Number UL1RR025005, a component of the National Institutes of Health and NIH Roadmap for Medical Research. A systems BIOlogy Study to TAilored Treatment in Chronic Heart Failure (BIOSTAT- CHF)This project was funded by a grant from the European Commission (FP7‐242209‐ BIOSTAT‐CHF, EudraCT 2010–020808–29). Cardiovascular Health Study (CHS) This CHS research was supported by NHLBI contracts HHSN268201200036C, HHSN268200800007C, HHSN268201800001C, HHSN268200960009C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, and NHLBI grants U01HL080295, R01HL087652, R01HL105756, R01HL103612, R01HL120393, and U01HL130114 with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided through R01AG023629 from the National Institute on Aging (NIA). A full list of principal CHS investigators and institutions can be found at CHS-NHLBI.org. The provision of genotyping data was supported in part by the National Center for Advancing Translational Sciences, CTSI grant UL1TR001881, and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center (DRC) grant DK063491 to the Southern California Diabetes Endocrinology Research Center. deCODE Heart Failure Study (deCODE) We at deCODE thank the women and men of Iceland that have participated in our studies and our colleagues that contributed to data collection and processing. DiscovEHR We acknowledge and thank all participants in Geisinger’s MyCode Community Health Initiative for their support and permission to use their health and genomic information in the DiscovEHR collaboration. This work was supported by the Regeneron Genetics Center and Geisinger. Estonian Genome Center at the University of Tartu (EGCUT) This study was supported by Estonian Research Council Grant IUT20-60, EU, H2020 grant 692145, European Union through the European Regional Development Fund (Project No. 2014-2020.4.01.15-0012) GENTRANSMED. Eplerenone Post-Acute Myocardial Infarction Heart Failure Efficacy and Survival Study (EPHESUS) The EPHESUS was supported by Pfizer, Inc. The European Prospective Investigation of Cancer, Norfolk study (EPIC-Norfolk) The EPIC-Norfolk Study is supported by programme grants from the Medical Research Council UK (G1000143) and Cancer Research UK (C864/A14136) and with additional support from the European Union, Stroke Association, British Heart Foundation, Research into Ageing, Department of Health, The Wellcome Trust and the Food Standards Agency. NJW and CL also acknowledge support from the Medical Research Council, UK (MC_UU_12015/1, MC_PC_13048). We thank all EPIC participants and staff for their contribution to the study, and thank staff from the Technical, Field Epidemiology and Data Functional Group Teams of the Medical Research Council Epidemiology Unit in Cambridge, UK, for carrying out sample preparation, DNA provision and quality control, genotyping and data handling work. Framingham Heart Study (FHS) This work was conducted using data and resources from the Framingham Heart Study (FHS) of the National Heart Lung and Blood Institute and Boston University School of Medicine. The study was supported by the National Heart, Lung and Blood Institute’s Framingham Heart Study (Contract No. N01-HC-25195 and HHSN268201500001I) and its contract with Affymetrix, Inc for genotyping services (Contract No.N02-HL-6-4278). The work was also supported by R01 HL093328, R01 HL105993, and R01 HL71039 (PI: Ramachandran). FINRISK V.S. has been supported by the Finnish Foundation for Cardiovascular Research. Genetics of Diabetes Audit and Research Tayside Scotland GoDARTS) The Wellcome Trust United Kingdom Type 2 Diabetes Case Control Collection (supporting GoDARTS) was funded by the Wellcome Trust (072960/Z/03/Z, 084726/Z/08/Z, 084727/Z/08/Z, 085475/Z/08/Z, 085475/B/08/Z) and as part of the EU IMI-SUMMIT programme. We acknowledge the support of the Health Informatics Centre, University of Dundee, for managing and supplying the anonymized data and NHS Tayside, the original data owner. The Genetic Risk Assessment of Defibrillator Events (GRADE) NIH-NHLBI R01 HL77398 (Genetic Modulators of Sudden Death). S.L. is supported by NIH grant 1R01HL139731 and American Heart Association 18SFRN34250007. The LUdwigshafen RIsk and Cardiovascular Health (LURIC) study We extend our appreciation to the participants of the LURIC study, without their collaboration, this article would not have been written. We thank the LURIC study team who were either temporarily or permanently involved in patient recruitment as well as sample and data handling, in addition to the laboratory staff at the Ludwigshafen General Hospital and the Universities of Freiburg and Ulm, Germany. LURIC has received funding from the 7th Framework Program (RiskyCAD, grant agreement number 305739 and Atheroremo, grant agreement number 201668) of the European Union. Malmö Diet and Cancer Study (MDCS) J. Gustav Smith was supported by grants from the Swedish Heart-Lung Foundation (2016- 0134 and 2016-0315), the Swedish Research Council (2017-02554), the European Research Council (ERC-STG-2015-679242), the Crafoord Foundation, Skåne University Hospital, the Scania county, governmental funding of clinical research within the Swedish National Health Service, a generous donation from the Knut and Alice Wallenberg foundation to the Wallenberg Center for Molecular Medicine in Lund, and funding from the Swedish Research Council (Linnaeus grant Dnr 349-2006-237, Strategic Research Area Exodiab Dnr 2009-1039) and Swedish Foundation for Strategic Research (Dnr IRC15- 0067) to the Lund University Diabetes Center. The Malmo Diet and Cancer Study was made possible by grants from the Swedish Cancer Society, the Swedish Medical Research Council, the Swedish Dairy Association, and the Malmo city council. Penn Heart Failure Study (PHFS) The study was supported by NIH grants (NIH R01L088577 and NIH R01H105993). Prevention of REnal and Vascular ENd-stage Disease (PREVEND) The Prevention of Renal and Vascular Endstage Disease Study (PREVEND) genetics is supported by the Dutch Kidney Foundation (Grant E033), the EU project grant GENECURE (FP-6 LSHM CT 2006 037697), the National Institutes of Health (grant LM010098), the Netherlands organisation for health research and development (NWO VENI grant 916.761.70), and the Dutch Inter University Cardiology Institute Netherlands (ICIN). Niek Verweij was supported by NWO VENI grant 016.186.125. PROspective Study of Pravastatin in the Elderly at Risk for vascular disease (PROSPER)The PROSPER study was supported by an investigator-initiated grant obtained from Bristol- Myers Squibb. Support for genotyping was provided by the seventh framework program of the European commission (grant 223004) and by the Netherlands Genomics Initiative (Netherlands Consortium for Healthy Aging grant 050-060-810). J.W.J. is an Established Clinical Investigator of the Netherlands Heart Foundation (grant 2001 D 032). Study of Health in Pomerania (SHIP) SHIP is part of the Community Medicine Research net of the University of Greifswald, Germany, which is funded by the Federal Ministry of Education and Research (grants no. 01ZZ9603, 01ZZ0103, and 01ZZ0403), the Ministry of Cultural Affairs as well as the Social Ministry of the Federal State of Mecklenburg-West Pomerania, and the network ‘Greifswald Approach to Individualized Medicine (GANI_MED)’ funded by the Federal Ministry of Education and Research (grant 03IS2061A). Genome-wide data have been supported by the Federal Ministry of Education and Research (grant no. 03ZIK012) and a joint grant from Siemens Healthineers, Erlangen, Germany and the Federal State of Mecklenburg- West Pomerania. The University of Greifswald is a member of the Caché Campus program of the InterSystems GmbH. Stabilization of Plaque using Darapladib-Thrombolysis in Myocardial Infarction 52 (SOLID)SOLID-TIMI 52 was funded by GlaxoSmithKline. TwinGene (TwinGene) TwinGene received funding from the Swedish Research Council (M-2005-1112), GenomEUtwin (EU/QLRT-2001-01254, QLG2-CT-2002-01254), NIH DK U01-066134, The Swedish Foundation for Strategic Research (SSF) and the Heart and Lung foundation no. 20070481. TwinGene is part of the Swedish Twin Registry which is managed by Karolinska Institutet and receives funding through the Swedish Research Council (2017–00641). UK Biobank (UKBiobank) This research has been conducted using the UK Biobank Resource under Application Number 15422. This work was supported in part by grants to R.T.L. from the EU/EFPIA Innovative Medicines Initiative 2 Joint Undertaking BigData@Heart grant no. 116074, MRC Proximity to Discovery Award Scheme, the American Heart Association Institute for Precision Mecidine, Pfizer Ltd, the University College London British Heart Foundation Research Accelerator (AA/18/6/34223), and was supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre. A. H. is supported by the British Heart Foundation Cardiovascular Biomedicine PhD studentship. R.T.L is supported by a UK Research and Innovation Rutherford Fellowship and was previously supported by a National Institutes of Health Research Clinical Lectureship. Uppsala Longitudinal Study of Adult Men (ULSAM) J.Ä. is supported by the Swedish Research Council and the Swedish Heart Lung foundation. C.M.L is supported by the Li Ka Shing Foundation, WT-SSI/John Fell funds and by the NIHR Biomedical Research Centre, Oxford, by Widenlife and NIH (5P50HD028138- 27). Women’s Genome Health Study (WGHS) The WGHS is supported by the National Heart, Lung, and Blood Institute (HL043851 and HL080467, HL099355) and the National Cancer Institute (CA047988 and UM1CA182913), with collaborative scientific support and funding for genotyping provided by Amgen., Epidemiology, Internal Medicine, Læknadeild (HÍ), Faculty of Medicine (UI), Verkfræði- og náttúruvísindasvið (HÍ), School of Engineering and Natural Sciences (UI), Heilbrigðisvísindasvið (HÍ), School of Health Sciences (UI), Háskóli Íslands, and University of Iceland

Subjects
0301 basic medicine, Dánartíðni, Epidemiology, LOCI, 45/43, General Physics and Astronomy, Muscle Proteins, Genome-wide association study, Disease, Coronary Artery Disease, 030204 cardiovascular system & hematology, Bioinformatics, Genome-wide association studies, DISEASE, Ventricular Function, Left, Coronary artery disease, 0302 clinical medicine, Risk Factors, Atrial Fibrillation, IMPUTATION, Medicine, Blóðrásarsjúkdómar, 692/308/174, lcsh:Science, 2. Zero hunger, RISK, Multidisciplinary, Microfilament Proteins, article, Atrial fibrillation, Mendelian Randomization Analysis, CATALOG, 3. Good health, OBESITY, Erfðarannsóknir, Cardiomyopathies, Medical Genetics, Cyclin-Dependent Kinase Inhibitor p21, Science, 631/208/205/2138, Heart failure, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, 631/443/592/2727, [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, RESOURCE, Humans, Mortality, METAANALYSIS, Genetic association, Medicinsk genetik, Adaptor Proteins, Signal Transducing, Heart Failure, HYPERTENSION, business.industry, Case-control study, Klinisk medicin, 692/699/75/230, General Chemistry, Cardiovascular genetics, medicine.disease, R1, 030104 developmental biology, Case-Control Studies, lcsh:Q, Morbidity, Clinical Medicine, business, Apoptosis Regulatory Proteins, Carrier Proteins, Genome-Wide Association Study
Abstract

Publisher's version (útgefin grein), Heart failure (HF) is a leading cause of morbidity and mortality worldwide. A small proportion of HF cases are attributable to monogenic cardiomyopathies and existing genome-wide association studies (GWAS) have yielded only limited insights, leaving the observed heritability of HF largely unexplained. We report results from a GWAS meta-analysis of HF comprising 47,309 cases and 930,014 controls. Twelve independent variants at 11 genomic loci are associated with HF, all of which demonstrate one or more associations with coronary artery disease (CAD), atrial fibrillation, or reduced left ventricular function, suggesting shared genetic aetiology. Functional analysis of non-CAD-associated loci implicate genes involved in cardiac development (MYOZ1, SYNPO2L), protein homoeostasis (BAG3), and cellular senescence (CDKN1A). Mendelian randomisation analysis supports causal roles for several HF risk factors, and demonstrates CAD-independent effects for atrial fibrillation, body mass index, and hypertension. These findings extend our knowledge of the pathways underlying HF and may inform new therapeutic strategies., We acknowledge the contribution from the EchoGen Consortium.

Published
2020
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13. Spatio-temporal dynamics of Plasmodium falciparum transmission within a spatial unit on the Colombian Pacific Coast

Author

Carlos Fernandez-del Castillo, Samanda Aponte, Julian C. Rayner, Felipe González-Casabianca, Alejandro Feged-Rivadeneira, Vladimir Corredor, Giovanni Apráez, Angélica Knudson, Myriam Salazar-Terreros, Christopher G Jacob, Manuela Carrasquilla, Venkatachalam Udhayakumar, Ricardo Sánchez-Pedraza, Maria Fernanda Pedreros, Alena Pance, Naomi W. Lucchi, Elvira Mancilla, Jairo Andrés Angel, Anderson Piamba-Dorado, Adriana Olaya, Feged-Rivadeneira, Alejandro [0000-0001-8649-4213], Pance, Alena [0000-0002-9017-2644], Carrasquilla, Manuela [0000-0002-7953-4376], Rayner, Julian C [0000-0002-9835-1014], Corredor, Vladimir [0000-0002-8597-8125], Apollo - University of Cambridge Repository, and Rayner, Julian C. [0000-0002-9835-1014]

Subjects
0301 basic medicine, Male, Plasmodium, Epidemiology, Drug Resistance, Protozoan Proteins, lcsh:Medicine, law.invention, within, falciparum, 0302 clinical medicine, law, 692/308/174, 129, Malaria, Falciparum, lcsh:Science, Child, Coast, Aged, 80 and over, Multidisciplinary, Network topology, biology, 631/326/417/2550, transmission, Diagnostic test, dynamics, Middle Aged, 3. Good health, Colombian, Transmission (mechanics), Geography, Child, Preschool, 631/208/727/728, Female, Genetic and epidemiological data, Malaria control, Cartography, Adult, Adolescent, unit, 030231 tropical medicine, Plasmodium falciparum, 45/23, Antigens, Protozoan, Colombia, 631/114/2408, Article, Unit (housing), 03 medical and health sciences, Malaria transmission, Malaria elimination, Transmission heterogeneity, 631/158/1469, parasitic diseases, medicine, Parasite genetics, Humans, Understanding of malaria, Aged, Ecological epidemiology, lcsh:R, Infant, biology.organism_classification, medicine.disease, Pacific, spatial, 030104 developmental biology, Haplotypes, Spatio-temporal, lcsh:Q, 119, Malaria, Gene Deletion
Abstract

As malaria control programmes concentrate their efforts towards malaria elimination a better understanding of malaria transmission patterns at fine spatial resolution units becomes necessary. Defining spatial units that consider transmission heterogeneity, human movement and migration will help to set up achievable malaria elimination milestones and guide the creation of efficient operational administrative control units. Using a combination of genetic and epidemiological data we defined a malaria transmission unit as the area contributing 95% of malaria cases diagnosed at the catchment facility located in the town of Guapi in the South Pacific Coast of Colombia. We provide data showing that P. falciparum malaria transmission is heterogeneous in time and space and analysed, using topological data analysis, the spatial connectivity, at the micro epidemiological level, between parasite populations circulating within the unit. To illustrate the necessity to evaluate the efficacy of malaria control measures within the transmission unit in order to increase the efficiency of the malaria control effort, we provide information on the size of the asymptomatic reservoir, the nature of parasite genotypes associated with drug resistance as well as the frequency of the Pfhrp2/3 deletion associated with false negatives when using Rapid Diagnostic Tests.

Published
2020

14. Interleukin-18 as a drug repositioning opportunity for inflammatory bowel disease: A Mendelian randomization study

Author

Luke Devey, Lon R. Cardon, Sirui Zhou, Dawn Waterworth, Cong Guo, George Davey Smith, Robert A. Scott, Claudia Langenberg, Lauren E. Mokry, J. Brent Richards, Philippe Sanseau, Nicholas J. Wareham, Richards, J. Brent [0000-0002-3746-9086], Apollo - University of Cambridge Repository, and Richards, J Brent [0000-0002-3746-9086]

Subjects
0301 basic medicine, Oncology, Epidemiology, 45/43, lcsh:Medicine, Type 2 diabetes, Severity of Illness Index, Inflammatory bowel disease, law.invention, 631/208, 0302 clinical medicine, Randomized controlled trial, law, Odds Ratio, 692/308/174, lcsh:Science, Enterocolitis, Receptors, Interleukin-18, Multidisciplinary, Drug discovery, Anti-Inflammatory Agents, Non-Steroidal, Confounding, Interleukin-18, article, 3. Good health, Drug repositioning, Treatment Outcome, 692/4020/1503/257, medicine.symptom, medicine.medical_specialty, Single-nucleotide polymorphism, Polymorphism, Single Nucleotide, 03 medical and health sciences, Internal medicine, Mendelian randomization, Genetics, medicine, Humans, Genetic Predisposition to Disease, Alleles, business.industry, lcsh:R, Drug Repositioning, 631/154, Mendelian Randomization Analysis, Inflammatory Bowel Diseases, medicine.disease, 030104 developmental biology, lcsh:Q, business, Biomarkers, 030217 neurology & neurosurgery
Abstract

Support from human genetics increases the probability of success in drug development. However, few examples exist of successful genomically-driven drug repositioning. Given that a Mendelian form of severe enterocolitis is due to up-regulation of the interleukin-18 (IL18) signaling pathway, and pharmacologic inhibition of IL18 has been shown to reverse this enterocolitis, we undertook a Mendelian randomization study to test the causal effect of elevated IL18 levels on inflammatory bowel disease susceptibility (IBD) in 12,882 cases and 21,770 controls. Mendelian randomization is an established method to assess the role of biomarkers in disease etiology in a manner that minimizes confounding and prevents reverse causation. Using three SNPs that explained almost 7% of the variance in IL18 level, we found that each genetically predicted standard deviation increase in IL18 was associated with an increase in IBD susceptibility (odds ratio = 1.22, 95% CI = 1.11–1.34, P-value = 6 × 10−5). This association was further validated in 25,042 IBD cases and 34,915 controls (odds ratio = 1.13, 95% CI = 1.05–1.20). Recently, an anti-IL18 monoclonal antibody, which decreased free IL18 levels, was found to be safe, yet ineffective in a phase II trial for type 2 diabetes. Taken together, these genomic findings implicated IBD as an alternative indication for anti-IL18 therapy, which should be tested in randomized controlled trials.

Published
2019
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15. Evaluation of (−)-epicatechin metabolites as recovery biomarker of dietary flavan-3-ol intake

Author

Anna Vogiatzoglou, Gunter G. C. Kuhnle, Deborah H. Mawson, Reedmond Fong, Debora Lucarelli, Nicholas J. Wareham, Amy Tym, Javier I. Ottaviani, Antonia Wierzbicki, Philip B. Grace, Hagen Schroeter, Nita G. Forouhi, Jodi L. Ensunsa, Nicola Gray, Abigail Britten, Kay-Tee Khaw, Robert Luben, A. David Smith, Jennifer Kimball, Luben, Robert [0000-0002-5088-6343], Forouhi, Nita G [0000-0002-5041-248X], Kuhnle, Gunter GC [0000-0002-8081-8931], Apollo - University of Cambridge Repository, Forouhi, Nita G. [0000-0002-5041-248X], and Kuhnle, Gunter G. C. [0000-0002-8081-8931]

Subjects
141, 0301 basic medicine, Adult, Male, Epidemiology, Science, Population, Flavan-3-ol, Urine, Institute of medicine, EPIC, Catechin, Article, Correlation, Cohort Studies, 03 medical and health sciences, 0302 clinical medicine, Humans, 692/308/174, Food science, education, chemistry.chemical_classification, Flavonoids, Nutritional biomarkers, education.field_of_study, Multidisciplinary, Middle Aged, Healthy Volunteers, 3. Good health, Diet, 030104 developmental biology, chemistry, Risk factors, Medicine, Biomarker (medicine), Female, 692/499, 030217 neurology & neurosurgery, Biomarkers
Abstract

Data from dietary intervention studies suggest that intake of (−)-epicatechin mediates beneficial vascular effects in humans. However, population-based investigations are required to evaluate associations between habitual intake and health and these studies rely on accurate estimates of intake, which nutritional biomarkers can provide. Here, we evaluate a series of structurally related (−)-epicatechin metabolites (SREM), particularly (−)-epicatechin-3′-glucuronide, (−)-epicatechin-3′-sulfate and 3′-O-methyl-(−)-epicatechin-5-sulfate (SREMB), as flavan-3-ol and (−)-epicatechin intake. SREMB in urine proved to be a specific indicator of (−)-epicatechin intake, showing also a strong correlation with the amount of (−)-epicatechin ingested (R2: 0.86 (95% CI 0.8l; 0.92). The median recovery of (−)-epicatechin as SREMB in 24 h urine was 10% (IQR 7–13%) and we found SREMB in the majority of participants of EPIC Norfolk (83% of 24,341) with a mean concentration of 2.4 ± 3.2 µmol/L. Our results show that SREMB are suitable as biomarker of (−)-epicatechin intake. According to evaluation criteria from IARC and the Institute of Medicine, the results obtained support use of SREMB as a recovery biomarker to estimate actual intake of (−)-epicatechin.

Published
2019

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