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1. Cardiovascular risk factors are associated with venous thromboembolism

Author

Gregson, John, Kaptoge, Stephen, Bolton, Thomas, Pennells, Lisa, Willeit, Peter, Burgess, Stephen, Bell, Steven, Sweeting, Michael, Rimm, Eric B., Kabrhel, Christopher, Zoller, Bengt, Assmann, Gerd, Gudnason, Vilmundur, Folsom, Aaron R., Arndt, Volker, Fletcher, Astrid, Norman, Paul E., Nordestgaard, Borge G., Kitamura, Akihiko, Mahmoodi, Bakhtawar K., Whincup, Peter H., Knuiman, Matthew, Salomaa, Veikko, Meisinger, Christa, Koenig, Wolfgang, Kavousi, Maryam, Voelzke, Henry, Cooper, Jackie A., Ninomiya, Toshiharu, Casiglia, Edoardo, Rodriguez, Beatriz, Ben-Shlomo, Yoav, Despres, Jean-Pierre, Simons, Leon, Barrett-Connor, Elizabeth, Bjorkelund, Cecilia, Notdurfter, Marlene, Kromhout, Daan, Price, Jackie, Sutherland, Susan E., Sundstroem, Johan, Kauhanen, Jussi, Gallacher, John, Beulens, Joline W. J., Dankner, Rachel, Cooper, Cyrus, Giampaoli, Simona, Deen, Jason F., Gomez de la Camara, Agustin, Kuller, Lewis H., Rosengren, Annika, Svensson, Peter J., Nagel, Dorothea, Crespo, Carlos J., Brenner, Hermann, Albertorio-Diaz, Juan R., Atkins, Robert, Brunner, Eric J., Shipley, Martin, Njolstad, Inger, Lawlor, Deborah A., van der Schouw, Yvonne T., Selmer, Randi Marie, Trevisan, Maurizio, Verschuren, W. M. Monique, Greenland, Philip, Wassertheil-Smoller, Sylvia, Lowe, Gordon D. O., Wood, Angela M., Butterworth, Adam S., Thompson, Simon G., Danesh, John, Di Angelantonio, Emanuele, Meade, Tom, Rosamond, Wayne, Whitsel, Eric, Cushman, Mary, Barr, Elizabeth L. M., Shaw, Jonathan E., Zimmet, Paul Z., Kiechl, Stefan, Weger, Siegfried, Willeit, Johann, Amuzu, Antoinette, Dale, Caroline, Casas, Juan P., Tikhonoff, Valerie, Nietert, Paul, Tybjaerg-Hansen, Anne, Frikke-Schmidt, Ruth, Jensen, Gorm B., Lora Pablos, David, Cancelas Navia, Pilar, McLachlan, Stela, Schoettker, Ben, Saum, Kai-Uwe, Holleczek, Bernd, Ariansen, Inger, Meyer, Haakon E., Haheim, Lise Lund, Vartiainen, Erkki, Jousilahti, Pekka, Harald, Kennet, Wilhelmsen, Lars, Dennison, Elaine, Syddall, Holly, Westbury, Leo, Flicker, Leon, Hankey, Graeme J., Golledge, Jonathan, Doi, Yasufumi, Kiyohara, Yutaka, Elders, Petra, Stehouwer, Coen, Jensen, Majken, Iso, Hiroyasu, Yamagishi, Kazumasa, Sudhir, Kurl, Tuomainen, Tomi-Pekka, Salonen, Jukka T., Boer, Jolanda M. A., Blokstra, Anneke, Melander, Olle, Nilsson, Peter M., Engstrom, Gunnar, Palmieri, Luigi, Vanuzzo, Diego, Peters, Annette, Thorand, Barbara, Heier, Margit, Hu, Frank B., Manson, JoAnn E., Meijer, Karina, Gansevoort, Ron T., Schulte, Helmut, Sluijs, Ivonne, Cantin, Bernard, Lamarche, Benoit, Dagenais, Gilles R., McEvoy, Linda, Laughlin, Gail, Daniels, Lori B., Aspelund, Thor, Gudmundsson, Elias Freyr, Thorsson, Bolli, Leening, Maarten J. G., Ikram, M. Arfan, Franco, Oscar H., Tunstall-Pedoe, Hugh, Werner, Andre, Devereux, Richard, Jolly, Stacey, Smith, George Davey, Can, Gunay, Yuksel, Husniye, Altay, Servet, Ingelsson, Martin, Giedraitis, Vilmantas, Claessen, Heiner, Rothenbacher, Dietrich, Parikh, Nisha, I, Eaton, Charles, Kivimaki, Mika, Feskens, Edith, Geleijnse, Johanna M., Spackman, Sarah, Walker, Matthew, Cardiovascular Centre (CVC), Groningen Kidney Center (GKC), Real World Studies in PharmacoEpidemiology, -Genetics, -Economics and -Therapy (PEGET), Kaptoge, Stephen [0000-0002-1155-4872], Pennells, Lisa [0000-0002-8594-3061], Burgess, Stephen [0000-0001-5365-8760], Bell, Steven [0000-0001-6774-3149], Sweeting, Michael [0000-0003-0980-8965], Wood, Angela [0000-0002-7937-304X], Butterworth, Adam [0000-0002-6915-9015], Danesh, John [0000-0003-1158-6791], Di Angelantonio, Emanuele [0000-0001-8776-6719], Apollo - University of Cambridge Repository, Epidemiology, ACS - Diabetes & metabolism, APH - Health Behaviors & Chronic Diseases, Epidemiology and Data Science, and ACS - Heart failure & arrhythmias

Subjects
Male, Outcome Assessment, ALCOHOL-CONSUMPTION, Coronary Disease, 030204 cardiovascular system & hematology, Body Mass Index, 0302 clinical medicine, Risk Factors, Outcome Assessment, Health Care, Cardiac and Cardiovascular Systems, 030212 general & internal medicine, Prospective Studies, DEEP-VEIN THROMBOSIS, Prospective cohort study, Original Investigation, Venous Thrombosis, education.field_of_study, Kardiologi, Hazard ratio, INDIVIDUAL-PARTICIPANT DATA, Venous Thromboembolism, Middle Aged, Biobank, 3. Good health, Pulmonary embolism, ddc, Cardiovascular Diseases, OBESITY, Female, SMOKING, Cardiology and Cardiovascular Medicine, Cohort study, Adult, medicine.medical_specialty, PULMONARY-EMBOLISM, Population, 03 medical and health sciences, Internal medicine, medicine, Journal Article, Diabetes Mellitus, Online First, Life Science, Humans, CORONARY-HEART-DISEASE, ddc:610, Obesity, Pulmonary Embolism, Smoking, United Kingdom, Risk factor, education, METAANALYSIS, VLAG, Global Nutrition, Wereldvoeding, business.industry, Research, DIABETES-MELLITUS, medicine.disease, Health Care, LIPOPROTEIN, business, Body mass index
Abstract

Key Points Question To what extent are established cardiovascular risk factors associated with risk of venous thromboembolism (VTE)? Findings In this analysis of individual participant data from the Emerging Risk Factors Collaboration and the UK Biobank including 1.1 million participants, among a panel of several established cardiovascular risk factors, older age, smoking, and greater adiposity were consistently associated with higher VTE risk. Meaning There is overlap in at least some of the major population determinants of important venous and arterial thrombotic diseases., This analysis of data from the Emerging Risk Factors Collaboration and the UK Biobank estimates the associations of major cardiovascular risk factors with venous thromboembolism., 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.

Published
2019

2. The use of repeated blood pressure measures for cardiovascular risk prediction: a comparison of statistical models in the ARIC study

Author

Sweeting, Michael J., Barrett, Jessica K., Thompson, Simon G., Wood, Angela M., Sweeting, Michael J [0000-0003-0980-8965], Thompson, Simon G [0000-0002-5274-7814], and Apollo - University of Cambridge Repository

Subjects
Male, repeat measures, Models, Statistical, joint models, C-index, Blood Pressure, Blood Pressure Determination, regression calibration, Middle Aged, C‐index, Risk Assessment, Survival Analysis, Bias, Cardiovascular Diseases, Risk Factors, cardiovascular risk prediction, Humans, Regression Analysis, Computer Simulation, Female, Longitudinal Studies, Research Articles, Biomarkers, Research Article
Abstract

Many prediction models have been developed for the risk assessment and the prevention of cardiovascular disease in primary care. Recent efforts have focused on improving the accuracy of these prediction models by adding novel biomarkers to a common set of baseline risk predictors. Few have considered incorporating repeated measures of the common risk predictors. Through application to the Atherosclerosis Risk in Communities study and simulations, we compare models that use simple summary measures of the repeat information on systolic blood pressure, such as (i) baseline only; (ii) last observation carried forward; and (iii) cumulative mean, against more complex methods that model the repeat information using (iv) ordinary regression calibration; (v) risk-set regression calibration; and (vi) joint longitudinal and survival models. In comparison with the baseline-only model, we observed modest improvements in discrimination and calibration using the cumulative mean of systolic blood pressure, but little further improvement from any of the complex methods. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Published
2016
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3. Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors

Author

Burgess, Stephen, Scott, Robert A., Timpson, Nicholas J., Davey Smith, George, and Thompson, Simon G.

Subjects
Epidemiology, Published data, Genetic Variation, Mendelian Randomization Analysis, Sensitivity and Specificity, Two-sample Mendelian randomization, Instrumental variable, Random Allocation, Risk Factors, Data Interpretation, Statistical, Methods, Mendelian randomization, Summarized data, Humans, Genetic Predisposition to Disease, Causal inference
Abstract

Finding individual-level data for adequately-powered Mendelian randomization analyses may be problematic. As publicly-available summarized data on genetic associations with disease outcomes from large consortia are becoming more abundant, use of published data is an attractive analysis strategy for obtaining precise estimates of the causal effects of risk factors on outcomes. We detail the necessary steps for conducting Mendelian randomization investigations using published data, and present novel statistical methods for combining data on the associations of multiple (correlated or uncorrelated) genetic variants with the risk factor and outcome into a single causal effect estimate. A two-sample analysis strategy may be employed, in which evidence on the gene-risk factor and gene-outcome associations are taken from different data sources. These approaches allow the efficient identification of risk factors that are suitable targets for clinical intervention from published data, although the ability to assess the assumptions necessary for causal inference is diminished. Methods and guidance are illustrated using the example of the causal effect of serum calcium levels on fasting glucose concentrations. The estimated causal effect of a 1 standard deviation (0.13 mmol/L) increase in calcium levels on fasting glucose (mM) using a single lead variant from the CASR gene region is 0.044 (95 % credible interval −0.002, 0.100). In contrast, using our method to account for the correlation between variants, the corresponding estimate using 17 genetic variants is 0.022 (95 % credible interval 0.009, 0.035), a more clearly positive causal effect. Electronic supplementary material The online version of this article (doi:10.1007/s10654-015-0011-z) contains supplementary material, which is available to authorized users.

Published
2015

4. Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods

Author

Burgess, Stephen, Dudbridge, Frank, Thompson, Simon G, Burgess, Stephen [0000-0001-5365-8760], Thompson, Simon G [0000-0002-5274-7814], and Apollo - University of Cambridge Repository

Subjects
instrumental variables, Models, Statistical, Models, Genetic, genetic variants, Coronary Disease, summarized data, Cholesterol, LDL, Mendelian Randomization Analysis, genetic risk score, aggregated data, Causality, allele score, Risk Factors, Mendelian randomization, Humans, causal inference, weak instruments, Alleles
Abstract

Mendelian randomization is the use of genetic instrumental variables to obtain causal inferences from observational data. Two recent developments for combining information on multiple uncorrelated instrumental variables (IVs) into a single causal estimate are as follows: (i) allele scores, in which individual-level data on the IVs are aggregated into a univariate score, which is used as a single IV, and (ii) a summary statistic method, in which causal estimates calculated from each IV using summarized data are combined in an inverse-variance weighted meta-analysis. To avoid bias from weak instruments, unweighted and externally weighted allele scores have been recommended. Here, we propose equivalent approaches using summarized data and also provide extensions of the methods for use with correlated IVs. We investigate the impact of different choices of weights on the bias and precision of estimates in simulation studies. We show that allele score estimates can be reproduced using summarized data on genetic associations with the risk factor and the outcome. Estimates from the summary statistic method using external weights are biased towards the null when the weights are imprecisely estimated; in contrast, allele score estimates are unbiased. With equal or external weights, both methods provide appropriate tests of the null hypothesis of no causal effect even with large numbers of potentially weak instruments. We illustrate these methods using summarized data on the causal effect of low-density lipoprotein cholesterol on coronary heart disease risk. It is shown that a more precise causal estimate can be obtained using multiple genetic variants from a single gene region, even if the variants are correlated.

Published
2015

5. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction : An Individual-Participant-Data Meta-Analysis

Author

Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G, Psaty, Bruce M, Goldbourt, Uri, Best, Lyle G, Assmann, Gerd, Salonen, Jukka T, Nietert, Paul J, Verschuren, W M Monique, Brunner, Eric J, Kronmal, Richard A, Salomaa, Veikko, Bakker, Stephan J L, Dagenais, Gilles R, Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W, Meade, Tom W, Donfrancesco, Chiara, Kuller, Lewis H, Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R, Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G, Wood, Angela, Clinicum, Department of Public Health, University of Helsinki, Barrett, Jessica [0000-0003-1889-9803], Pennells, Lisa [0000-0002-8594-3061], Sweeting, Michael [0000-0003-0980-8965], Di Angelantonio, Emanuele [0000-0001-8776-6719], Danesh, John [0000-0003-1158-6791], Thompson, Simon [0000-0002-5274-7814], Wood, Angela [0000-0002-7937-304X], Apollo - University of Cambridge Repository, and Epidemiology

Subjects
Adult, MODELS, Blood Pressure, Cholesterol/blood, Risk Assessment, risk prediction, Systematic Reviews, Meta- and Pooled Analyses, SDG 3 - Good Health and Well-being, cardiovascular disease, MULTIPLE, Humans, risk factors, CORONARY-HEART-DISEASE, longitudinal measurements, Cardiac and Cardiovascular Systems, Risk Assessment/methods, repeated measurements, Human Nutrition & Health, Aged, Cardiovascular Disease, Longitudinal Measurements, Repeated Measurements, Risk Factors, Risk Prediction, repeated risk factors, Kardiologi, Cardiovascular Diseases/epidemiology, Humane Voeding & Gezondheid, Blood Pressure Determination, Public Health, Global Health, Social Medicine and Epidemiology, Middle Aged, 3142 Public health care science, environmental and occupational health, VARIABILITY, Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi, Cholesterol, Cardiovascular Diseases, Female
Abstract

The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.

Published
2017

6. Modeling the costs and long-term health benefits of screening the general population for risks of cardiovascular disease: a review of methods used in the literature

Author

Epstein, David, García-Mochón, Leticia, Kaptoge, Stephen, Thompson, Simon G, Kaptoge, Stephen [0000-0002-1155-4872], Thompson, Simon [0000-0002-5274-7814], and Apollo - University of Cambridge Repository

Subjects
Adult, Male, Cost-Benefit Analysis, Economics, Econometrics and Finance (miscellaneous), Health Promotion, Risk Assessment, Risk Factors, Humans, Aged, Literature review, Aged, 80 and over, Original Paper, Primary prevention, Health Policy, Cost-effectiveness analysis, H510, Statins, Reproducibility of Results, Middle Aged, Cardiovascular disease, Models, Economic, Cardiovascular Diseases, Screening, Female, I180, Quality-Adjusted Life Years, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Algorithms
Abstract

Background Strategies for screening and intervening to reduce the risk of cardiovascular disease (CVD) in primary care settings need to be assessed in terms of both their costs and long-term health effects. We undertook a literature review to investigate the methodologies used. Methods In a framework of developing a new health-economic model for evaluating different screening strategies for primary prevention of CVD in Europe (EPIC-CVD project), we identified seven key modeling issues and reviewed papers published between 2000 and 2013 to assess how they were addressed. Results We found 13 relevant health-economic modeling studies of screening to prevent CVD in primary care. The models varied in their degree of complexity, with between two and 33 health states. Programmes that screen the whole population by a fixed cut-off (e.g., predicted 10-year CVD risk >20 %) identify predominantly elderly people, who may not be those most likely to benefit from long-term treatment. Uncertainty and model validation were generally poorly addressed. Few studies considered the disutility of taking drugs in otherwise healthy individuals or the budget impact of the programme. Conclusions Model validation, incorporation of parameter uncertainty, and sensitivity analyses for assumptions made are all important components of model building and reporting, and deserve more attention. Complex models may not necessarily give more accurate predictions. Availability of a large enough source dataset to reliably estimate all relevant input parameters is crucial for achieving credible results. Decision criteria should consider budget impact and the medicalization of the population as well as cost-effectiveness thresholds. Electronic supplementary material The online version of this article (doi:10.1007/s10198-015-0753-2) contains supplementary material, which is available to authorized users.

Published
2016

7. Mendelian Randomization Analysis With Multiple Genetic Variants Using Summarized Data

Author

Burgess, Stephen, Butterworth, Adam, and Thompson, Simon G

Subjects
Likelihood Functions, instrumental variables, genome-wide association study, Models, Genetic, Genetic Variation, Coronary Disease, Cholesterol, LDL, Mendelian Randomization Analysis, Linkage Disequilibrium, Phenotype, Bias, Genes, Risk Factors, Mendelian randomization, Linear Models, Odds Ratio, Humans, causal inference, weak instruments, Least-Squares Analysis, Research Articles
Abstract

Genome-wide association studies, which typically report regression coefficients summarizing the associations of many genetic variants with various traits, are potentially a powerful source of data for Mendelian randomization investigations. We demonstrate how such coefficients from multiple variants can be combined in a Mendelian randomization analysis to estimate the causal effect of a risk factor on an outcome. The bias and efficiency of estimates based on summarized data are compared to those based on individual-level data in simulation studies. We investigate the impact of gene-gene interactions, linkage disequilibrium, and 'weak instruments' on these estimates. Both an inverse-variance weighted average of variant-specific associations and a likelihood-based approach for summarized data give similar estimates and precision to the two-stage least squares method for individual-level data, even when there are gene-gene interactions. However, these summarized data methods overstate precision when variants are in linkage disequilibrium. If the P-value in a linear regression of the risk factor for each variant is less than 1×10⁻⁵, then weak instrument bias will be small. We use these methods to estimate the causal association of low-density lipoprotein cholesterol (LDL-C) on coronary artery disease using published data on five genetic variants. A 30% reduction in LDL-C is estimated to reduce coronary artery disease risk by 67% (95% CI: 54% to 76%). We conclude that Mendelian randomization investigations using summarized data from uncorrelated variants are similarly efficient to those using individual-level data, although the necessary assumptions cannot be so fully assessed.

Published
2013

8. Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects

Author

Burgess, Stephen, Thompson, Simon G, Burgess, Stephen [0000-0001-5365-8760], Thompson, Simon [0000-0002-5274-7814], and Apollo - University of Cambridge Repository

Subjects
lipid fractions, instrumental variables, Models, Genetic, epidemiologic methods, Cholesterol, HDL, Genetic Variation, Coronary Disease, Genetic Pleiotropy, Cholesterol, LDL, Mendelian Randomization Analysis, Sensitivity and Specificity, United Kingdom, Causality, Predictive Value of Tests, Risk Factors, pleiotropy, Mendelian randomization, Humans, causal inference, Mathematical Computing, Biomarkers, Triglycerides
Abstract

A conventional Mendelian randomization analysis assesses the causal effect of a risk factor on an outcome by using genetic variants that are solely associated with the risk factor of interest as instrumental variables. However, in some cases, such as the case of triglyceride level as a risk factor for cardiovascular disease, it may be difficult to find a relevant genetic variant that is not also associated with related risk factors, such as other lipid fractions. Such a variant is known as pleiotropic. In this paper, we propose an extension of Mendelian randomization that uses multiple genetic variants associated with several measured risk factors to simultaneously estimate the causal effect of each of the risk factors on the outcome. This "multivariable Mendelian randomization" approach is similar to the simultaneous assessment of several treatments in a factorial randomized trial. In this paper, methods for estimating the causal effects are presented and compared using real and simulated data, and the assumptions necessary for a valid multivariable Mendelian randomization analysis are discussed. Subject to these assumptions, we demonstrate that triglyceride-related pathways have a causal effect on the risk of coronary heart disease independent of the effects of low-density lipoprotein cholesterol and high-density lipoprotein cholesterol.

Published
2015

9. Instrumental variable analysis with a nonlinear exposure-outcome relationship

Author

Burgess, Stephen, Davies, Neil M, Thompson, Simon G, EPIC-InterAct Consortium, Burgess, Stephen [0000-0001-5365-8760], Thompson, Simon [0000-0002-5274-7814], and Apollo - University of Cambridge Repository

Subjects
Causality, Europe, Cardiovascular Diseases, Risk Factors, Linear Models, Humans, Multicenter Studies as Topic, Prospective Studies, Mendelian Randomization Analysis, Epidemiologic Methods, Body Mass Index
Abstract

BACKGROUND: Instrumental variable methods can estimate the causal effect of an exposure on an outcome using observational data. Many instrumental variable methods assume that the exposure-outcome relation is linear, but in practice this assumption is often in doubt, or perhaps the shape of the relation is a target for investigation. We investigate this issue in the context of Mendelian randomization, the use of genetic variants as instrumental variables. METHODS: Using simulations, we demonstrate the performance of a simple linear instrumental variable method when the true shape of the exposure-outcome relation is not linear. We also present a novel method for estimating the effect of the exposure on the outcome within strata of the exposure distribution. This enables the estimation of localized average causal effects within quantile groups of the exposure or as a continuous function of the exposure using a sliding window approach. RESULTS: Our simulations suggest that linear instrumental variable estimates approximate a population-averaged causal effect. This is the average difference in the outcome if the exposure for every individual in the population is increased by a fixed amount. Estimates of localized average causal effects reveal the shape of the exposure-outcome relation for a variety of models. These methods are used to investigate the relations between body mass index and a range of cardiovascular risk factors. CONCLUSIONS: Nonlinear exposure-outcome relations should not be a barrier to instrumental variable analyses. When the exposure-outcome relation is not linear, either a population-averaged causal effect or the shape of the exposure-outcome relation can be estimated.

Published
2014

10. Assessing Risk Prediction Models Using Individual Participant Data From Multiple Studies

Author

Pennells, Lisa, Kaptoge, Stephen, White, Ian R., Thompson, Simon G., Wood, Angela M., Tipping, Robert W., Folsom, Aaron R., Couper, David J., Ballantyne, Christie M., Coresh, Josef, Goya Wannamethee, S., Morris, Richard W., Kiechl, Stefan, Willeit, Johann, Willeit, Peter, Schett, Georg, Ebrahim, Shah, Lawlor, Debbie A., Yarnell, John W., Gallacher, John, Cushman, Mary, Psaty, Bruce M., Tracy, Russ, Tybjærg-Hansen, Anne, Price, Jackie F., Lee, Amanda J., McLachlan, Stela, Khaw, Kay-Tee, Wareham, Nicholas J., Brenner, Hermann, Schöttker, Ben, Müller, Heiko, Jansson, Jan-Håkan, Wennberg, Patrik, Salomaa, Veikko, Harald, Kennet, Jousilahti, Pekka, Vartiainen, Erkki, Woodward, Mark, D'Agostino, Ralph B., Bladbjerg, Else-Marie, Jørgensen, Torben, Kiyohara, Yutaka, Arima, Hisatomi, Doi, Yasufumi, Ninomiya, Toshiharu, Dekker, Jacqueline M., Nijpels, Giel, Stehouwer, Coen D. A., Kauhanen, Jussi, Salonen, Jukka T., Meade, Tom W., Cooper, Jackie A., Shea, Steven, Döring, Angela, Kuller, Lewis H., Grandits, Greg, Gillum, Richard F., Mussolino, Michael, Rimm, Eric B., Hankinson, Sue E., Manson, JoAnn E., Pai, Jennifer K., Kirkland, Susan, Shaffer, Jonathan A., Shimbo, Daichi, Bakker, Stephan J. L., Gansevoort, Ron T., Hillege, Hans L., Amouyel, Philippe, Arveiler, Dominique, Evans, Alun, Ferrières, Jean, Sattar, Naveed, Westendorp, Rudi G., Buckley, Brendan M., Cantin, Bernard, Lamarche, Benoît, Barrett-Connor, Elizabeth, Wingard, Deborah L., Bettencourt, Richele, Gudnason, Vilmundur, Aspelund, Thor, Sigurdsson, Gunnar, Thorsson, Bolli, Kavousi, Maryam, Witteman, Jacqueline C., Hofman, Albert, Franco, Oscar H., Howard, Barbara V., Zhang, Ying, Best, Lyle, Umans, Jason G., Onat, Altan, Sundström, Johan, Michael Gaziano, J., Stampfer, Meir, Ridker, Paul M., Marmot, Michael, Clarke, Robert, Collins, Rory, Fletcher, Astrid, Brunner, Eric, Shipley, Martin, Kivimäki, Mika, Buring, Julie, Cook, Nancy, Ford, Ian, Shepherd, James, Cobbe, Stuart M., Robertson, Michele, Walker, Matthew, Watson, Sarah, Alexander, Myriam, Butterworth, Adam S., Angelantonio, Emanuele Di, Gao, Pei, Haycock, Philip, Wormser, David, Danesh, John, MUMC+: HVC Pieken Maastricht Studie (9), Interne Geneeskunde, MUMC+: MA Interne Geneeskunde (3), and RS: CARIM - R3 - Vascular biology

Subjects
Male, Epidemiology, Practice of Epidemiology, Poison control, Coronary Disease, Weighting, Inverse variance, Risk Assessment, risk prediction, Meta-Analysis as Topic, Risk Factors, Statistics, Medicine, Humans, Prospective Studies, coronary heart disease, 10. No inequality, Prospective cohort study, Survival analysis, Proportional Hazards Models, Models, Statistical, business.industry, Proportional hazards model, C index, Individual participant data, inverse variance, individual participant data, Middle Aged, D measure, Risk prediction, Coronary heart disease, meta-analysis, Meta-analysis, C-Reactive Protein, Data Interpretation, Statistical, weighting, Female, business, Risk assessment
Abstract

Individual participant time-to-event data from multiple prospective epidemiologic studies enable detailed investigation into the predictive ability of risk models. Here we address the challenges in appropriately combining such information across studies. Methods are exemplified by analyses of log C-reactive protein and conventional risk factors for coronary heart disease in the Emerging Risk Factors Collaboration, a collation of individual data from multiple prospective studies with an average follow-up duration of 9.8 years (dates varied). We derive risk prediction models using Cox proportional hazards regression analysis stratified by study and obtain estimates of risk discrimination, Harrell's concordance index, and Royston's discrimination measure within each study; we then combine the estimates across studies using a weighted meta-analysis. Various weighting approaches are compared and lead us to recommend using the number of events in each study. We also discuss the calculation of measures of reclassification for multiple studies. We further show that comparison of differences in predictive ability across subgroups should be based only on within-study information and that combining measures of risk discrimination from case-control studies and prospective studies is problematic. The concordance index and discrimination measure gave qualitatively similar results throughout. While the concordance index was very heterogeneous between studies, principally because of differing age ranges, the increments in the concordance index from adding log C-reactive protein to conventional risk factors were more homogeneous.

Published
2014

11. Use of allele scores as instrumental variables for Mendelian randomization.

Author

Burgess, Stephen and Thompson, Simon G

Subjects
*ALLELES, *INSTRUMENTAL variables (Statistics), *RANDOMIZATION (Statistics), *HUMAN genetic variation, *HEALTH outcome assessment, *GENETICS, *EPIDEMIOLOGY, RISK factors
Abstract

Background An allele score is a single variable summarizing multiple genetic variants associated with a risk factor. It is calculated as the total number of risk factor-increasing alleles for an individual (unweighted score), or the sum of weights for each allele corresponding to estimated genetic effect sizes (weighted score). An allele score can be used in a Mendelian randomization analysis to estimate the causal effect of the risk factor on an outcome.Methods Data were simulated to investigate the use of allele scores in Mendelian randomization where conventional instrumental variable techniques using multiple genetic variants demonstrate ‘weak instrument’ bias. The robustness of estimates using the allele score to misspecification (for example non-linearity, effect modification) and to violations of the instrumental variable assumptions was assessed.Results Causal estimates using a correctly specified allele score were unbiased with appropriate coverage levels. The estimates were generally robust to misspecification of the allele score, but not to instrumental variable violations, even if the majority of variants in the allele score were valid instruments. Using a weighted rather than an unweighted allele score increased power, but the increase was small when genetic variants had similar effect sizes. Naive use of the data under analysis to choose which variants to include in an allele score, or for deriving weights, resulted in substantial biases.Conclusions Allele scores enable valid causal estimates with large numbers of genetic variants. The stringency of criteria for genetic variants in Mendelian randomization should be maintained for all variants in an allele score. [ABSTRACT FROM AUTHOR]

Published
2013
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12. Somatic mosaicism and common genetic variation contribute to the risk of very-early-onset inflammatory bowel disease

Author

Serra, Eva Gonçalves, Schwerd, Tobias, Moutsianas, Loukas, Cavounidis, Athena, Fachal, Laura, Pandey, Sumeet, Kammermeier, Jochen, Croft, Nicholas M, Posovszky, Carsten, Rodrigues, Astor, Russell, Richard K, Barakat, Farah, Auth, Marcus K H, Heuschkel, Robert, Zilbauer, Matthias, Fyderek, Krzysztof, Braegger, Christian, Travis, Simon P, Satsangi, Jack, Parkes, Miles, Thapar, Nikhil, Ferry, Helen, Matte, Julie C, Gilmour, Kimberly C, Wedrychowicz, Andrzej, Sullivan, Peter, Moore, Carmel, Sambrook, Jennifer, Ouwehand, Willem, Roberts, David, Danesh, John, Baeumler, Toni A, Fulga, Tudor A, Karaminejadranjbar, Mohammad, Ahmed, Ahmed, Wilson, Rachel, Barrett, Jeffrey C, Elkadri, Abdul, Griffiths, Anne M, Snapper, Scott B, Shah, Neil, Muise, Aleixo M, Wilson, David C, Uhlig, Holm H, Anderson, Carl A, Marlen, Zurek, Caterina, Strisciuglio, Mamoun, Elawad, Bernice, Lo, Carolina, Arancibia-Carcamo, Adam, Bailey, Ellie, Barnes, Elizabeth Louise, Bird-Lieberman, Oliver, Brain, Barbara, Braden, Jane, Collier, James, East, Lucy, Howarth, Satish, Keshav, Paul, Klenerman, Simon, Leedham, Rebecca, Palmer, Fiona, Powrie, Alison, Simmons, Matthew, Walker, Zoe, Tolkien, Stephen, Kaptoge, David, Allen, Susan, Mehenny, Jonathan, Mant, Emanuele, Di Angelantonio, Simon G, Thompson, Bahtiyar, Yilmaz, Pascal, Juillerat, Markus, Geuking, Reiner, Wiest, Andrew J, Macpherson, Francisco Damian, Bravo, Lukas, Brügger, Ove, Carstens, Ulrike Graf, Bigler, Benjamin, Heimgartner, Monica, Rusticeanu, Sybille, Schmid-Uebelhart, Bruno, Strebel, Aurora, Tatu, Radu, Tutuian, Ove, Øyås, Charlotte, Ramon, Jörg, Stelling, Yannick, Franc, Nicolas, Fournier, Valerie E H, Pittet, Bernard, Burnand, Mara, Egger, Delphine, Golay, Astrid, Marot, Leilla, Musso, Valérie, Pittet, Jean-Benoît, Rossel, Vivianne, Seematter, Joachim, Sommer, Rachel, Vulliamy, Pierre, Michetti, Michel H, Maillard, Céline, Keller, Andreas, Nydegger, Alain, Schoepfe, Eva, Archanioti, Jessica, Ezri, Montserrat, Fraga, Alain, Schoepfer, Christoph, Müller, Gerhard, Rogler, Luc, Biedermann, Mirjam, Blattmann, Sabine, Burk, Barbara, Dora, Michael, Fried, Benjamin, Misselwitz, Beat, Müllhaupt, Nicole, Obialo, Daniel, Pohl, Nadia, Raschle, Michael, Scharl, Stephan, Vavricka, Roland, Von Känel, Jonas, Zeitz, Karim, Abdelrahman, Gentiana, Ademi, Jan, Borovicka, Stephan, Brand, Remus, Frei, Johannes, Haarer, Christina, Knellwolf-Grieger, Claudia, Krieger-Grübel, Patrizia, Künzler, Christa, Meyenberger, Pamela, Meyer, Nina, Röhrich, Mikael, Sawatzki, Martin, Schelling, Gian-Marco, Semadeni, Michael, Sulz, Dorothee, Zimmermann, Patrick, Aepli, Dominique H, Criblez, Cyrill, Hess, Jean-Pierre, Richterich, Johannes, Spalinger, Dominic, Staudenmann, Andreas, Stulz, Stefanie, Wöhrle, Amman, Thomas, Claudia, Anderegg, Henrik, Köhler, Rachel, Kusche, Anca-Teodora, Antonino, Eviano, Arrigoni, José M, Bengoa, Sophie, Cunningham, Philippe, de Saussure, Laurent, Girard, Diana Bakker, de Jong, Polat, Bastürk, Simon, Brunner, Lukas, Degen, Petr, Hruz, Carolina, Khalid-de Bakker, Jan, Niess, Bruno, Balsiger, Janine, Haldemann, Gaby, Saner, Frank, Seibold, Peter, Bauerfeind, Andrea, Becocci, Dominique, Belli, Janek, Binek, Peter, Hengstler, Stephan, Boehm, Tujana, Boldanov, Patrick, Bühr, Rebekka, Koller, Vanessa, Rueger, Arne, Senning, Emanuel, Burri, Sophie, Buyse, Dahlia-Thao, Cao, Fabrizia, D'Angelo, Joakim, Delarive, Christopher, Doerig, Roxane, Hessler, Claudia, Preissler, Ronald, Rentsch, Branislav, Risti, Marc Alain, Ritz, Michael, Steuerwald, Jürg, Vögtlin, Markus, Sagmeister, Bernhard, Sauter, Susanne, Schibli, Christiane, Sokollik, Hugo, Schlauri, Jean-François, Schnegg, Mariam, Seirafi, Holger, Spangenberger, Philippe, Stadler, Peter, Staub, Volker, Stenz, Michela, Tempia-Caliera, Joël, Thorens, Kaspar, Truninger, Patrick, Urfer, Francesco, Viani, Dominique, Vouillamoz, Silvan, Zander, Tina, Wyli, L, Jostins, N A, Kennedy, T, Ahmad, C A, Lamb, C, Edwards, A, Hart, C, Hawkey, J C, Mansfield, C, Mowat, W G, Newman, A, Simmons, M, Tremelling, J C, Lee, N J, Prescott, C G, Mathew, C W, Lees, D P B, McGovern, S R, Targan, G, Botwin, E, Mengesha, P, Fleshner, C, Landers, D, Li, J D, Rioux, A, Bitton, J, Côté-Daigneault, M J, Daly, R, Xavier, K, Morris, G, Boucher, J H, Cho, C, Abraham, M, Merad, B, Sands, I, Peter, K, Hao, Y, Itan, R H, Duerr, L, Konnikova, M B, Schwartz, S, Proksell, E, Johnston, V, Miladinova, W, Chen, S R, Brant, L, Datta, M S, Silverberg, L P, Schumm, S, Birch, M, Giri, K, Gettler, Y, Sharma, C, Stevens, M, Lazarev, T, Haritunians, Fachal, Laura [0000-0002-7256-9752], Croft, Nicholas M [0000-0002-1519-6435], Posovszky, Carsten [0000-0002-9487-8812], Russell, Richard K [0000-0001-7398-4926], Zilbauer, Matthias [0000-0002-7272-0547], Travis, Simon P [0000-0002-2690-4361], Matte, Julie C [0000-0001-5642-648X], Wedrychowicz, Andrzej [0000-0003-1448-167X], Fulga, Tudor A [0000-0002-1056-0082], Karaminejadranjbar, Mohammad [0000-0002-7770-2065], Ahmed, Ahmed [0000-0001-6509-2581], Muise, Aleixo M [0000-0001-9624-3346], Wilson, David C [0000-0003-0879-1129], Apollo - University of Cambridge Repository, UCL - SSS/IREC/MONT - Pôle Mont Godinne, UCL - (MGD) Service de gastro-entérologie, Croft, Nicholas M. [0000-0002-1519-6435], Russell, Richard K. [0000-0001-7398-4926], Travis, Simon P. [0000-0002-2690-4361], Matte, Julie C. [0000-0001-5642-648X], Fulga, Tudor A. [0000-0002-1056-0082], Muise, Aleixo M. [0000-0001-9624-3346], Wilson, David C. [0000-0003-0879-1129], Eva Gonçalves, Serra, Tobias, Schwerd, Loukas, Moutsiana, Athena, Cavounidi, Laura, Fachal, Sumeet, Pandey, Jochen, Kammermeier, Nicholas M, Croft, Carsten, Posovszky, Astor, Rodrigue, Richard K, Russell, Farah, Barakat, Marcus K H, Auth, Robert, Heuschkel, Matthias, Zilbauer, Krzysztof, Fyderek, Christian, Braegger, Simon P, Travi, Jack, Satsangi, Miles, Parke, Nikhil, Thapar, Helen, Ferry, Julie C, Matte, Kimberly C, Gilmour, Andrzej, Wedrychowicz, Peter, Sullivan, Carmel, Moore, Jennifer, Sambrook, Willem, Ouwehand, David, Robert, John, Danesh, Toni A, Baeumler, Tudor A, Fulga, Mohammad, Karaminejadranjbar, Ahmed, Ahmed, Rachel, Wilson, Jeffrey C, Barrett, Abdul, Elkadri, Anne M, Griffith, Scott B, Snapper, Neil, Shah, Aleixo M, Muise, David C, Wilson, Holm H, Uhlig, Carl A, Anderson, Zurek, Marlen, Strisciuglio, Caterina, Elawad, Mamoun, Lo, Bernice, Arancibia-Carcamo, Carolina, Bailey, Adam, Barnes, Ellie, Bird-Lieberman, Elizabeth Louise, Brain, Oliver, Braden, Barbara, Collier, Jane, East, Jame, Howarth, Lucy, Keshav, Satish, Klenerman, Paul, Leedham, Simon, Palmer, Rebecca, Powrie, Fiona, Simmons, Alison, Walker, Matthew, Tolkien, Zoe, Kaptoge, Stephen, Allen, David, Mehenny, Susan, Mant, Jonathan, Di Angelantonio, Emanuele, Thompson, Simon G, Yilmaz, Bahtiyar, Juillerat, Pascal, Geuking, Marku, Wiest, Reiner, Macpherson, Andrew J, Bravo, Francisco Damian, Brügger, Luka, Carstens, Ove, Bigler, Ulrike Graf, Heimgartner, Benjamin, Rusticeanu, Monica, Schmid-Uebelhart, Sybille, Strebel, Bruno, Tatu, Aurora, Tutuian, Radu, Øyås, Ove, Ramon, Charlotte, Stelling, Jörg, Franc, Yannick, Fournier, Nicola, Pittet, Valerie E H, Burnand, Bernard, Egger, Mara, Golay, Delphine, Marot, Astrid, Musso, Leilla, Pittet, Valérie, Rossel, Jean-Benoît, Seematter, Vivianne, Sommer, Joachim, Vulliamy, Rachel, Michetti, Pierre, Maillard, Michel H, Keller, Céline, Nydegger, Andrea, Schoepfe, Alain, Archanioti, Eva, Ezri, Jessica, Fraga, Montserrat, Schoepfer, Alain, Müller, Christoph, Rogler, Gerhard, Biedermann, Luc, Blattmann, Mirjam, Burk, Sabine, Dora, Barbara, Fried, Michael, Misselwitz, Benjamin, Müllhaupt, Beat, Obialo, Nicole, Pohl, Daniel, Raschle, Nadia, Scharl, Michael, Vavricka, Stephan, Von Känel, Roland, Zeitz, Jona, Abdelrahman, Karim, Ademi, Gentiana, Borovicka, Jan, Brand, Stephan, Frei, Remu, Haarer, Johanne, Knellwolf-Grieger, Christina, Krieger-Grübel, Claudia, Künzler, Patrizia, Meyenberger, Christa, Meyer, Pamela, Röhrich, Nina, Sawatzki, Mikael, Schelling, Martin, Semadeni, Gian-Marco, Sulz, Michael, Zimmermann, Dorothee, Aepli, Patrick, Criblez, Dominique H, Hess, Cyrill, Richterich, Jean-Pierre, Spalinger, Johanne, Staudenmann, Dominic, Stulz, Andrea, Wöhrle, Stefanie, Thomas, Amman, Anderegg, Claudia, Köhler, Henrik, Kusche, Rachel, Antonino, Anca-Teodora, Arrigoni, Eviano, Bengoa, José M, Cunningham, Sophie, de Saussure, Philippe, Girard, Laurent, de Jong, Diana Bakker, Bastürk, Polat, Brunner, Simon, Degen, Luka, Hruz, Petr, Khalid-de Bakker, Carolina, Niess, Jan, Balsiger, Bruno, Haldemann, Janine, Saner, Gaby, Seibold, Frank, Bauerfeind, Peter, Becocci, Andrea, Belli, Dominique, Binek, Janek, Hengstler, Peter, Boehm, Stephan, Boldanov, Tujana, Bühr, Patrick, Koller, Rebekka, Rueger, Vanessa, Senning, Arne, Burri, Emanuel, Buyse, Sophie, Cao, Dahlia-Thao, D'Angelo, Fabrizia, Delarive, Joakim, Doerig, Christopher, Hessler, Roxane, Preissler, Claudia, Rentsch, Ronald, Risti, Branislav, Ritz, Marc Alain, Steuerwald, Michael, Vögtlin, Jürg, Sagmeister, Marku, Sauter, Bernhard, Schibli, Susanne, Sokollik, Christiane, Schlauri, Hugo, Schnegg, Jean-Françoi, Seirafi, Mariam, Spangenberger, Holger, Stadler, Philippe, Staub, Peter, Stenz, Volker, Tempia-Caliera, Michela, Thorens, Joël, Truninger, Kaspar, Urfer, Patrick, Viani, Francesco, Vouillamoz, Dominique, Zander, Silvan, Wyli, Tina, Jostins, L, Kennedy, N A, Ahmad, T, Lamb, C A, Edwards, C, Hart, A, Hawkey, C, Mansfield, J C, Mowat, C, Newman, W G, Simmons, A, Tremelling, M, Lee, J C, Prescott, N J, Mathew, C G, Lees, C W, Mcgovern, D P B, Targan, S R, Botwin, G, Mengesha, E, Fleshner, P, Landers, C, Li, D, Rioux, J D, Bitton, A, Côté-Daigneault, J, Daly, M J, Xavier, R, Morris, K, Boucher, G, Cho, J H, Abraham, C, Merad, M, Sands, B, Peter, I, Hao, K, Itan, Y, Duerr, R H, Konnikova, L, Schwartz, M B, Proksell, S, Johnston, E, Miladinova, V, Chen, W, Brant, S R, Datta, L, Silverberg, M S, Schumm, L P, Birch, S, Giri, M, Gettler, K, Sharma, Y, Stevens, C, Lazarev, M, Haritunians, T, Carrami, Eli M [0000-0002-7770-2065], COLORS in IBD group investigators, Oxford IBD cohort study investigators, INTERVAL Study, Swiss IBD cohort investigators, UK IBD Genetics Consortium, NIDDK IBD Genetics Consortium, Zurek, M., Strisciuglio, C., Elawad, M., Lo, B., Arancibia-Carcamo, C., Bailey, A., Barnes, E., Bird-Lieberman, E.L., Brain, O., Braden, B., Collier, J., East, J., Howarth, L., Keshav, S., Klenerman, P., Leedham, S., Palmer, R., Powrie, F., Simmons, A., Walker, M., Tolkien, Z., Kaptoge, S., Allen, D., Mehenny, S., Mant, J., Di Angelantonio, E., Thompson, S.G., Yilmaz, B., Juillerat, P., Geuking, M., Wiest, R., Macpherson, A.J., Bravo, F.D., Brügger, L., Carstens, O., Bigler, U.G., Heimgartner, B., Rusticeanu, M., Schmid-Uebelhart, S., Strebel, B., Tatu, A., Tutuian, R., Øyås, O., Ramon, C., Stelling, J., Franc, Y., Fournier, N., Pittet, VEH, Burnand, B., Egger, M., Golay, D., Marot, A., Musso, L., Pittet, V., Rossel, J.B., Seematter, V., Sommer, J., Vulliamy, R., Michetti, P., Maillard, M.H., Keller, C., Nydegger, A., Schoepfe, A., Archanioti, E., Ezri, J., Fraga, M., Schoepfer, A., Müller, C., Rogler, G., Biedermann, L., Blattmann, M., Burk, S., Dora, B., Fried, M., Misselwitz, B., Müllhaupt, B., Obialo, N., Pohl, D., Raschle, N., Scharl, M., Vavricka, S., Von Känel, R., Zeitz, J., Abdelrahman, K., Ademi, G., Borovicka, J., Brand, S., Frei, R., Haarer, J., Knellwolf-Grieger, C., Krieger-Grübel, C., Künzler, P., Meyenberger, C., Meyer, P., Röhrich, N., Sawatzki, M., Schelling, M., Semadeni, G.M., Sulz, M., Zimmermann, D., Aepli, P., Criblez, D.H., Hess, C., Richterich, J.P., Spalinger, J., Staudenmann, D., Stulz, A., Wöhrle, S., Thomas, A., Anderegg, C., Köhler, H., Kusche, R., Antonino, A.T., Arrigoni, E., Bengoa, J.M., Cunningham, S., de Saussure, P., Girard, L., de Jong, D.B., Bastürk, P., Brunner, S., Degen, L., Hruz, P., Bakker, C.K., Niess, J., Balsiger, B., Haldemann, J., Saner, G., Seibold, F., Bauerfeind, P., Becocci, A., Belli, D., Binek, J., Hengstler, P., Boehm, S., Boldanov, T., Bühr, P., Koller, R., Rueger, V., Senning, A., Burri, E., Buyse, S., Cao, D.T., D'Angelo, F., Delarive, J., Doerig, C., Hessler, R., Preissler, C., Rentsch, R., Risti, B., Ritz, M.A., Steuerwald, M., Vögtlin, J., Sagmeister, M., Sauter, B., Schibli, S., Sokollik, C., Schlauri, H., Schnegg, J.F., Seirafi, M., Spangenberger, H., Stadler, P., Staub, P., Stenz, V., Tempia-Caliera, M., Thorens, J., Truninger, K., Urfer, P., Viani, F., Vouillamoz, D., Zander, S., Wyli, T., Jostins, L., Kennedy, N.A., Ahmad, T., Lamb, C.A., Edwards, C., Hart, A., Hawkey, C., Mansfield, J.C., Mowat, C., Newman, W.G., Tremelling, M., Lee, J.C., Prescott, N.J., Mathew, C.G., Lees, C.W., McGovern, DPB, Targan, S.R., Botwin, G., Mengesha, E., Fleshner, P., Landers, C., Li, D., Rioux, J.D., Bitton, A., Côté-Daigneault, J., Daly, M.J., Xavier, R., Morris, K., Boucher, G., Cho, J.H., Abraham, C., Merad, M., Sands, B., Peter, I., Hao, K., Itan, Y., Duerr, R.H., Konnikova, L., Schwartz, M.B., Proksell, S., Johnston, E., Miladinova, V., Chen, W., Brant, S.R., Datta, L., Silverberg, M.S., Schumm, L.P., Birch, S., Giri, M., Gettler, K., Sharma, Y., Stevens, C., Lazarev, M., and Haritunians, T.

Subjects
0301 basic medicine, Male, Multifactorial Inheritance, General Physics and Astronomy, 631/208/1516, 13, Inflammatory bowel disease, Whole Exome Sequencing, Adult, Age of Onset, Case-Control Studies, Child, Child, Preschool, Cohort Studies, Female, Genes, Recessive, Genetic Predisposition to Disease, Genetic Variation, Humans, Infant, Infant, Newborn, Inflammatory Bowel Diseases/etiology, Inflammatory Bowel Diseases/genetics, Loss of Function Mutation, Mosaicism, Mutation, NADPH Oxidase 2/genetics, Pedigree, Primary Immunodeficiency Diseases/complications, Primary Immunodeficiency Diseases/genetics, Risk Factors, 0302 clinical medicine, Primary Immunodeficiency Disease, Medicine, lcsh:Science, Exome sequencing, 49/31, education.field_of_study, Multidisciplinary, Medical genetics, article, 692/699/249/1570, 631/250/249/2510/257, 631/208/248, 3. Good health, NADPH Oxidase 2, 030211 gastroenterology & hepatology, Case-Control Studie, Human, medicine.medical_specialty, Science, Primary Immunodeficiency Diseases, Population, 45/22, 45/23, digestive system, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, Immunological deficiency syndromes, Exome Sequencing, Immunogenetics, Allele, education, 45, business.industry, Risk Factor, Inflammatory Bowel Disease, Case-control study, General Chemistry, medicine.disease, Inflammatory Bowel Diseases, digestive system diseases, 49, 030104 developmental biology, Immunology, Primary immunodeficiency, lcsh:Q, Age of onset, Cohort Studie, business
Abstract

Very-early-onset inflammatory bowel disease (VEO-IBD) is a heterogeneous phenotype associated with a spectrum of rare Mendelian disorders. Here, we perform whole-exome-sequencing and genome-wide genotyping in 145 patients (median age-at-diagnosis of 3.5 years), in whom no Mendelian disorders were clinically suspected. In five patients we detect a primary immunodeficiency or enteropathy, with clinical consequences (XIAP, CYBA, SH2D1A, PCSK1). We also present a case study of a VEO-IBD patient with a mosaic de novo, pathogenic allele in CYBB. The mutation is present in ~70% of phagocytes and sufficient to result in defective bacterial handling but not life-threatening infections. Finally, we show that VEO-IBD patients have, on average, higher IBD polygenic risk scores than population controls (99 patients and 18,780 controls; P, Adult forms of inflammatory bowel disease (IBD) are of a polygenic nature, but paediatric and very early onset (VEO) IBD also occur as monogenic forms. Here, using whole exome sequencing, the authors explore both the monogenic and polygenic contribution to VEO-IBD and characterize a rare somatic mosaic VEO-IBD patient.

Published
2020

13. Predictive value for cardiovascular events of common carotid intima media thickness and its rate of change in individuals at high cardiovascular risk – Results from the PROG-IMT collaboration

Author

Sathanur R. Srinivasan, Christine Espinola-Klein, Albert Hofman, Jing Liu, Eric de Groot, Tomi-Pekka Tuomainen, Helmuth Steinmetz, Ta-Chen Su, Carmen Suárez, Maria Rosvall, Stela McLachlan, Raffaele Izzo, Marcus Dörr, Liliana Grigore, Gunnar Engström, Stein Harald Johnsen, David Yanez, Giel Nijpels, Giuseppe Danilo Norata, Fabrizio Veglia, Matthias W. Lorenz, Dong Zhao, Lu Gao, Wuxiang Xie, Damiano Baldassarre, Mauro Amato, Ellisiv B. Mathiesen, Michiel L. Bots, Dirk Sander, Joseph F. Polak, Matthieu Plichart, Holger Poppert, Matthias Sitzer, Ralph L. Sacco, Irene Schmidtmann, Heiko Uthoff, Manuel F. Landecho, Horst Bickel, Gerald S. Berenson, Oscar H. Franco, Bo Hedblad, Kazuo Kitagawa, Daniel Staub, Jackie F. Price, Caroline Schmidt, Alberico L. Catapano, Tatjana Rundek, Thapat Wannarong, M. Arfan Ikram, Kathrin Ziegelbauer, Alfonso Friera, Francesco Rozza, Kimmo Ronkainen, Jacqueline M. Dekker, Peter Willeit, Samuela Castelnuovo, Coen D.A. Stehouwer, Shuhei Okazaki, Pierre Ducimetière, Stefan Blankenberg, Johann Willeit, Oscar Beloqui, Maryam Kavousi, Henry Völzke, Kuo-Liong Chien, Grace Parraga, Bernhard Iglseder, Rafael Gabriel, Lena Bokemark, Stefan Kiechl, Cesare R. Sirtori, Göran Bergström, Jussi Kauhanen, Simon G. Thompson, Nicola De Luca, Lars Lind, Jean Philippe Empana, Moïse Desvarieux, Ulf Schminke, Hung-Ju Lin, Elena Tremoli, Lorenz, Matthias W, Gao, Lu, Ziegelbauer, Kathrin, Norata, Giuseppe Danilo, Empana, Jean Philippe, Schmidtmann, Irene, Lin, Hung-Ju, Mclachlan, Stela, Bokemark, Lena, Ronkainen, Kimmo, Amato, Mauro, Schminke, Ulf, Srinivasan, Sathanur R, Lind, Lar, Okazaki, Shuhei, Stehouwer, Coen D A, Willeit, Peter, Polak, Joseph F, Steinmetz, Helmuth, Sander, Dirk, Poppert, Holger, Desvarieux, Moise, Ikram, M Arfan, Johnsen, Stein Harald, Staub, Daniel, Sirtori, Cesare R, Iglseder, Bernhard, Beloqui, Oscar, Engström, Gunnar, Friera, Alfonso, Rozza, Francesco, Xie, Wuxiang, Parraga, Grace, Grigore, Liliana, Plichart, Matthieu, Blankenberg, Stefan, Su, Ta-Chen, Schmidt, Caroline, Tuomainen, Tomi-Pekka, Veglia, Fabrizio, Völzke, Henry, Nijpels, Giel, Willeit, Johann, Sacco, Ralph L, Franco, Oscar H, Uthoff, Heiko, Hedblad, Bo, Suarez, Carmen, Izzo, Raffaele, Zhao, Dong, Wannarong, Thapat, Catapano, Alberico, Ducimetiere, Pierre, Espinola-Klein, Christine, Chien, Kuo-Liong, Price, Jackie F, Bergström, Göran, Kauhanen, Jussi, Tremoli, Elena, Dörr, Marcu, Berenson, Gerald, Kitagawa, Kazuo, Dekker, Jacqueline M, Kiechl, Stefan, Sitzer, Matthia, Bickel, Horst, Rundek, Tatjana, Hofman, Albert, Mathiesen, Ellisiv B, Castelnuovo, Samuela, Landecho, Manuel F, Rosvall, Maria, Gabriel, Rafael, de Luca, Nicola, Liu, Jing, Baldassarre, Damiano, Kavousi, Maryam, de Groot, Eric, Bots, Michiel L, Yanez, David N, Thompson, Simon G, Lorenz, Matthias W [0000-0002-7565-1751], Apollo - University of Cambridge Repository, General practice, APH - Health Behaviors & Chronic Diseases, Epidemiology and Data Science, Interne Geneeskunde, RS: CARIM - R3.01 - Vascular complications of diabetes and the metabolic syndrome, MUMC+: HVC Pieken Maastricht Studie (9), MUMC+: MA Interne Geneeskunde (3), Epidemiology, Neurology, Radiology & Nuclear Medicine, Pirro, Matteo, and PROG-IMT study group

Subjects
Male, Myocardial Infarction, lcsh:Medicine, PROGRESSION, Cardiovascular Medicine, 030204 cardiovascular system & hematology, Vascular Medicine, Biochemistry, Carotid Intima-Media Thickness, Geographical locations, DISEASE, 0302 clinical medicine, Risk Factors, Germany, Medicine and Health Sciences, Medicine, Cardiac and Cardiovascular Systems, Myocardial infarction, skin and connective tissue diseases, lcsh:Science, ARTERY INTIMA, Stroke, Intersectoral Collaboration, POPULATION, Cardiovascular Diseases/diagnosis, METABOLIC SYNDROME, education.field_of_study, Kardiologi, Multidisciplinary, Agricultural and Biological Sciences(all), VDP::Medical disciplines: 700::Clinical medical disciplines: 750, Hazard ratio, VDP::Medisinske Fag: 700::Klinisk medisinske fag: 750, Middle Aged, Prognosis, Predictive value, 3. Good health, Europe, Neurology, Italy, Cardiovascular Diseases, HYPERTENSIVE MEN, Cardiology, cardiovascular system, Female, Research Article, medicine.medical_specialty, High cardiovascular risk, Cerebrovascular Diseases, Science, Population, 030209 endocrinology & metabolism, ATHEROSCLEROSIS RISK, Arbetsmedicin och miljömedicin, 03 medical and health sciences, Carotid intima media thickness (CIMT), Internal medicine, Humans, European Union, ddc:610, cardiovascular diseases, education, Aged, Sweden, Biochemistry, Genetics and Molecular Biology(all), Proportional hazards model, business.industry, lcsh:R, Health Risk Analysis, Correction, Occupational Health and Environmental Health, Atherosclerosis, medicine.disease, Confidence interval, Health Care, Intima-media thickness, MYOCARDIAL-INFARCTION, Medical Biophysics, lcsh:Q, VASCULAR RISK, sense organs, Carotid intima media thickness , Cardiovascular risk, People and places, Metabolic syndrome, business, FOLLOW-UP, 030217 neurology & neurosurgery, Genetics and Molecular Biology(all)
Abstract

AIMS: Carotid intima media thickness (CIMT) predicts cardiovascular (CVD) events, but the predictive value of CIMT change is debated. We assessed the relation between CIMT change and events in individuals at high cardiovascular risk. METHODS AND RESULTS: From 31 cohorts with two CIMT scans (total n = 89070) on average 3.6 years apart and clinical follow-up, subcohorts were drawn: (A) individuals with at least 3 cardiovascular risk factors without previous CVD events, (B) individuals with carotid plaques without previous CVD events, and (C) individuals with previous CVD events. Cox regression models were fit to estimate the hazard ratio (HR) of the combined endpoint (myocardial infarction, stroke or vascular death) per standard deviation (SD) of CIMT change, adjusted for CVD risk factors. These HRs were pooled across studies. In groups A, B and C we observed 3483, 2845 and 1165 endpoint events, respectively. Average common CIMT was 0.79mm (SD 0.16mm), and annual common CIMT change was 0.01mm (SD 0.07mm), both in group A. The pooled HR per SD of annual common CIMT change (0.02 to 0.43mm) was 0.99 (95% confidence interval: 0.95-1.02) in group A, 0.98 (0.93-1.04) in group B, and 0.95 (0.89-1.04) in group C. The HR per SD of common CIMT (average of the first and the second CIMT scan, 0.09 to 0.75mm) was 1.15 (1.07-1.23) in group A, 1.13 (1.05-1.22) in group B, and 1.12 (1.05-1.20) in group C. CONCLUSIONS: We confirm that common CIMT is associated with future CVD events in individuals at high risk. CIMT change does not relate to future event risk in high-risk individuals. The PROG-IMT project, which includes this publication, has been funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, www.dfg.de) under the grants DFG Lo 1569/2-1 and DFG Lo 1569/2-3, received by MWL. The DFG had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Simon Thompson is supported by the British heart Foundation (CH/12/2/29428). Some of the contributing studies were funded by different parties, as listed in the acknowledgement section. Here, too, the funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Sí

Published
2018

14. Parity, breastfeeding and risk of coronary heart disease:A pan-European case-cohort study

Author

Kim Overvad, Eleni Klinaki, Elisabete Weiderpass, Emanuele Di Angelantonio, Giuseppe Matullo, Giovanna Masala, Anna Winkvist, Simon G. Thompson, M. Luisa Redondo, Françoise Clavel-Chapelon, Isabel Drake, Elena Molina-Portillo, N. Charlotte Onland-Moret, Therese Haugdahl Nøst, Adam S. Butterworth, Salma Butt, Diana Gavrila, Conchi Moreno-Iribas, Vassiliki Benetou, Antonia Trichopoulou, Tilman Kühn, Anja Olsen, Melissa A. Merritt, Ioanna Tzoulaki, Anne Tjønneland, Angela M. Wood, Larraitz Arriola, Renée Turzanski-Fortner, Fabrice Bonnet, Elio Riboli, Rosario Tumino, Karel G.M. Moons, Sanne A.E. Peters, Patrik Wennberg, Heiner Boeing, Timothy J. Key, Yvonne T. van der Schouw, Camille Lassale, Nicholas J. Wareham, Michael J. Sweeting, Salvatore Panico, Vittorio Krogh, John Danesh, Department of Epidemiology, German Institute of Human Nutrition (DIfE) Potsdam-Rehbrücke, University Medical Center [Utrecht], Department of Public Health & Primary Care, University of Cambridge [UK] (CAM), Department of Community Medicine, The Arctic University of Norway [Tromsø, Norway] (UiT), Department of Research, Cancer Registry of Norway, Department of Medical Epidemiology and Biostatistics (MEB), Karolinska Institutet [Stockholm], Genetic Epidemiology Group [Helsinki], Folkhälsan Research Center, Faculty of Medecine [Helsinki], Helsingin yliopisto = Helsingfors universitet = University of Helsinki-Helsingin yliopisto = Helsingfors universitet = University of Helsinki-Faculty of Medecine [Helsinki], Helsingin yliopisto = Helsingfors universitet = University of Helsinki-Helsingin yliopisto = Helsingfors universitet = University of Helsinki, Instituto de Investigación Sanitaria Biodonostia - San Sebastián, WHO Collaborating Center for Food and Nutrition Policies, Department of Hygiene, Epidemiology and Medical Statistics, University of Athens Medical School, Hellenic Health Foundation, Centre de recherche en épidémiologie et santé des populations (CESP), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Université Paris-Sud - Paris 11 (UP11)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM), CHU Pontchaillou [Rennes], Foie, métabolismes et cancer, Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Department of Clinical Sciences, Lund University [Lund], Mode de vie, génétique et santé : études intégratives et transgénérationnelles (U1018 (Équipe 9)), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Université Paris-Sud - Paris 11 (UP11)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Université Paris-Sud - Paris 11 (UP11)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Gustave Roussy (IGR), Nutrition, hormones et cancer: épidémiologie et prévention (E3N), Epidémiologie, sciences sociales, santé publique (IFR 69), Université Paris 1 Panthéon-Sorbonne (UP1)-Université Paris-Sud - Paris 11 (UP11)-École des hautes études en sciences sociales (EHESS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Paris 1 Panthéon-Sorbonne (UP1)-Université Paris-Sud - Paris 11 (UP11)-École des hautes études en sciences sociales (EHESS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-Institut Gustave Roussy (IGR)-Institut National de la Santé et de la Recherche Médicale (INSERM), CIBER de Epidemiología y Salud Pública (CIBERESP), Cancer Epidemiology Unit, University of Oxford-Cancer Epidemiology Unit, Nutritional Epidemiology Unit, Fondazione IRCCS Istituto Nazionale Tumori - National Cancer Institute [Milan], German Cancer Research Center - Deutsches Krebsforschungszentrum [Heidelberg] (DKFZ), Biomarkers Research, Imperial College School of Public Health, Molecular and Nutritional Epidemiology Unit, Cancer Research and Prevention Institute-ISPO, Department of Genetics, Biology and Biochemistry, Università degli studi di Torino = University of Turin (UNITO), Institute for Scientific Interchange Foundation, Universidad de Granada = University of Granada (UGR), Public Health Institute of Navarra, Pamplona, Spain, and CIBER Epidemiología y Salud Pública (CIBERESP), Danish Cancer Society Research Center, Department of Clinical Epidemiology, Aarhus University Hospital, Dipartimento di Medicina Clinica e Chirurgia, University of Naples Federico II = Università degli studi di Napoli Federico II, Public Health Directorate Asturias, Bureau of Epidemiologic Research, Academy of Athens, Cancer Registry and Histopathology Unit, Department of Oncology-Civile - M.P.Arezzo Hospital, Imperial College London, Umeå University, Medical Research Council Epidemiology Unit, European Union [HEALTH-F2-2012-279233], European Research Council [268834], Swedish Scientific Council, Regional Government of Skane and Vasterbotten (Sweden), Compagnia di San Paolo, Human Genetics Foundation-Torino (HuGeF), UK Medical Research Council [G0800270, MR/L003120/1, MR/M012190/1], British Heart Foundation [SP/09/002, RG/08/014, RG13/13/30194], UK National Institute of Health Research, Regional Government of Asturias, Cancer Research UK [570/A16491], Sicilian Government, AIRE ONLUS Ragusa, AVIS Ragusa, Swedish Cancer Society, The Arctic University of Norway, University of Helsinki-University of Helsinki-Faculty of Medecine [Helsinki], University of Helsinki-University of Helsinki, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Paris-Sud - Paris 11 (UP11)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Paris-Sud - Paris 11 (UP11)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Paris-Sud - Paris 11 (UP11)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut Gustave Roussy (IGR), University of Oxford [Oxford]-Cancer Epidemiology Unit, University of Turin, Universidad de Granada (UGR), University of Naples Federico II, Wood, Angela [0000-0002-7937-304X], Sweeting, Michael [0000-0003-0980-8965], Thompson, Simon [0000-0002-5274-7814], Di Angelantonio, Emanuele [0000-0001-8776-6719], Wareham, Nicholas [0000-0003-1422-2993], Danesh, John [0000-0003-1158-6791], Butterworth, Adam [0000-0002-6915-9015], Apollo - University of Cambridge Repository, Peters, Sanne Ae, van der Schouw, Yvonne T, Wood, Angela M, Sweeting, Michael J, Moons, Karel Gm, Weiderpass, Elisabete, Arriola, Larraitz, Benetou, Vassiliki, Boeing, Heiner, Bonnet, Fabrice, Butt, Salma T, Clavel Chapelon, Françoise, Drake, Isabel, Gavrila, Diana, Key, Timothy J, Klinaki, Eleni, Krogh, Vittorio, Kühn, Tilman, Lassale, Camille, Masala, Giovanna, Matullo, Giuseppe, Merritt, Melissa, Molina Portillo, Elena, Moreno Iribas, Conchi, Nøst, Therese H, Olsen, Anja, Onland Moret, N. Charlotte, Overvad, Kim, Panico, Salvatore, Redondo, M. Luisa, Tjønneland, Anne, Trichopoulou, Antonia, Tumino, Rosario, Turzanski Fortner, Renée, Tzoulaki, Ioanna, Wennberg, Patrik, Winkvist, Anna, Thompson, Simon G, Di Angelantonio, Emanuele, Riboli, Elio, Wareham, Nicholas J, Danesh, John, Butterworth, Adam S., Commission of the European Communities, and Jonchère, Laurent

Subjects
Pediatrics, breastfeeding, coronary heart disease, Parity, Women, Epidemiology, Cardiology and Cardiovascular Medicine, Breastfeeding, Coronary Disease, 030204 cardiovascular system & hematology, 0302 clinical medicine, Pregnancy, Risk Factors, Medicine, 030212 general & internal medicine, Prospective Studies, Age of Onset, Prospective cohort study, number, Incidence, Hazard ratio, health, Middle Aged, 3. Good health, European Prospective Investigation into Cancer and Nutrition, [SDV.MHEP.CSC] Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, Europe, Breast Feeding, Population Surveillance, Female, pregnancy, women, Cohort study, Adult, medicine.medical_specialty, life-style, lactation, Risk Assessment, Article, 03 medical and health sciences, [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, Journal Article, Humans, cancer, maternal cardiovascular-disease, Proportional hazards model, business.industry, duration, mortality, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, business, Breast feeding, Demography, Follow-Up Studies, Forecasting
Abstract

Objective: There is uncertainty about the direction and magnitude of the associations between parity, breastfeeding and the risk of coronary heart disease (CHD). We examined the separate and combined associations of parity and breastfeeding practices with the incidence of CHD later in life among women in a large, pan-European cohort study. Methods: Data were used from European Prospective Investigation into Cancer and Nutrition (EPIC)-CVD, a case–cohort study nested within the EPIC prospective study of 520,000 participants from 10 countries. Information on reproductive history was available for 14,917 women, including 5138 incident cases of CHD. Using Prentice-weighted Cox regression separately for each country followed by a random-effects meta-analysis, we calculated hazard ratios (HRs) and 95% confidence intervals (CIs) for CHD, after adjustment for age, study centre and several socioeconomic and biological risk factors. Results: Compared with nulliparous women, the adjusted HR was 1.19 (95% CI: 1.01–1.41) among parous women; HRs were higher among women with more children (e.g., adjusted HR: 1.95 (95% CI: 1.19–3.20) for women with five or more children). Compared with women who did not breastfeed, the adjusted HR was 0.71 (95% CI: 0.52–0.98) among women who breastfed. For childbearing women who never breastfed, the adjusted HR was 1.58 (95% CI: 1.09–2.30) compared with nulliparous women, whereas for childbearing women who breastfed, the adjusted HR was 1.19 (95% CI: 0.99–1.43). Conclusion: Having more children was associated with a higher risk of CHD later in life, whereas breastfeeding was associated with a lower CHD risk. Women who both had children and breastfed did have a non-significantly higher risk of CHD.

Published
2016

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