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Zou J, Talluri R, Shete S. Approaches to estimate bidirectional causal effects using Mendelian randomization with application to body mass index and fasting glucose. PLoS One 2024; 19:e0293510. [PMID: 38457457 PMCID: PMC10923437 DOI: 10.1371/journal.pone.0293510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 10/16/2023] [Indexed: 03/10/2024] Open
Abstract
Mendelian randomization (MR) is an epidemiological framework using genetic variants as instrumental variables (IVs) to examine the causal effect of exposures on outcomes. Statistical methods based on unidirectional MR (UMR) are widely used to estimate the causal effects of exposures on outcomes in observational studies. To estimate the bidirectional causal effects between two phenotypes, investigators have naively applied UMR methods separately in each direction. However, bidirectional causal effects between two phenotypes create a feedback loop that biases the estimation when UMR methods are naively applied. To overcome this limitation, we proposed two novel approaches to estimate bidirectional causal effects using MR: BiRatio and BiLIML, which are extensions of the standard ratio, and limited information maximum likelihood (LIML) methods, respectively. We compared the performance of the two proposed methods with the naive application of UMR methods through extensive simulations of several scenarios involving varying numbers of strong and weak IVs. Our simulation results showed that when multiple strong IVs are used, the proposed methods provided accurate bidirectional causal effect estimation in terms of median absolute bias and relative median absolute bias. Furthermore, compared to the BiRatio method, the BiLIML method provided a more accurate estimation of causal effects when weak IVs were used. Therefore, based on our simulations, we concluded that the BiLIML should be used for bidirectional causal effect estimation. We applied the proposed methods to investigate the potential bidirectional relationship between obesity and diabetes using the data from the Multi-Ethnic Study of Atherosclerosis cohort. We used body mass index (BMI) and fasting glucose (FG) as measures of obesity and type 2 diabetes, respectively. Our results from the BiLIML method revealed the bidirectional causal relationship between BMI and FG in across all racial populations. Specifically, in the White/Caucasian population, a 1 kg/m2 increase in BMI increased FG by 0.70 mg/dL (95% confidence interval [CI]: 0.3517-1.0489; p = 8.43×10-5), and 1 mg/dL increase in FG increased BMI by 0.10 kg/m2 (95% CI: 0.0441-0.1640; p = 6.79×10-4). Our study provides novel findings and quantifies the effect sizes of the bidirectional causal relationship between BMI and FG. However, further studies are needed to understand the biological and functional mechanisms underlying the bidirectional pathway.
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Affiliation(s)
- Jinhao Zou
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Rajesh Talluri
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
- Department of Data Science, The University of Mississippi Medical Center, Jackson, Mississippi, United States of America
| | - Sanjay Shete
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center Houston, Texas, United States of America
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Power GM, Sanderson E, Pagoni P, Fraser A, Morris T, Prince C, Frayling TM, Heron J, Richardson TG, Richmond R, Tyrrell J, Warrington N, Davey Smith G, Howe LD, Tilling KM. Methodological approaches, challenges, and opportunities in the application of Mendelian randomisation to lifecourse epidemiology: A systematic literature review. Eur J Epidemiol 2023:10.1007/s10654-023-01032-1. [PMID: 37938447 DOI: 10.1007/s10654-023-01032-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 07/21/2023] [Indexed: 11/09/2023]
Abstract
Diseases diagnosed in adulthood may have antecedents throughout (including prenatal) life. Gaining a better understanding of how exposures at different stages in the lifecourse influence health outcomes is key to elucidating the potential benefits of disease prevention strategies. Mendelian randomisation (MR) is increasingly used to estimate causal effects of exposures across the lifecourse on later life outcomes. This systematic literature review explores MR methods used to perform lifecourse investigations and reviews previous work that has utilised MR to elucidate the effects of factors acting at different stages of the lifecourse. We conducted searches in PubMed, Embase, Medline and MedRXiv databases. Thirteen methodological studies were identified. Four studies focused on the impact of time-varying exposures in the interpretation of "standard" MR techniques, five presented methods for repeat measures of the same exposure, and four described methodological approaches to handling multigenerational exposures. A further 127 studies presented the results of an applied research question. Over half of these estimated effects in a single generation and were largely confined to the exploration of questions regarding body composition. The remaining mostly estimated maternal effects. There is a growing body of research focused on the development and application of MR methods to address lifecourse research questions. The underlying assumptions require careful consideration and the interpretation of results rely on select conditions. Whilst we do not advocate for a particular strategy, we encourage practitioners to make informed decisions on how to approach a research question in this field with a solid understanding of the limitations present and how these may be affected by the research question, modelling approach, instrument selection, and data availability.
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Affiliation(s)
- Grace M Power
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.
| | - Eleanor Sanderson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Panagiota Pagoni
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Abigail Fraser
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Tim Morris
- Centre for Longitudinal Studies, Social Research Institute, University College London, London, UK
| | - Claire Prince
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Timothy M Frayling
- Genetics of Complex Traits, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Jon Heron
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Tom G Richardson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Rebecca Richmond
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Jessica Tyrrell
- Genetics of Complex Traits, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Nicole Warrington
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Frazer Institute, University of Queensland, Woolloongabba, Queensland, Australia
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- NIHR Bristol Biomedical Research Centre Bristol, University Hospitals Bristol and Weston NHS Foundation Trust, University of Bristol, Bristol, UK
| | - Laura D Howe
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Kate M Tilling
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
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Zhou J, Jiang X, Xia HA, Hobbs BP, Wei P. Landmark mediation survival analysis using longitudinal surrogate. Front Oncol 2023; 12:999324. [PMID: 36733365 PMCID: PMC9887328 DOI: 10.3389/fonc.2022.999324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 12/14/2022] [Indexed: 01/18/2023] Open
Abstract
Clinical cancer trials are designed to collect radiographic measurements of each patient's baseline and residual tumor burden at regular intervals over the course of study. For solid tumors, the extent of reduction in tumor size following treatment is used as a measure of a drug's antitumor activity. Statistical estimation of treatment efficacy routinely reduce the longitudinal assessment of tumor burden to a binary outcome describing the presence versus absence of an objective tumor response as defined by RECIST criteria. The objective response rate (ORR) is the predominate method for evaluating an experimental therapy in a single-arm trial. Additionally, ORR is routinely compared against a control therapy in phase III randomized controlled trials. The longitudinal assessments of tumor burden are seldom integrated into a formal statistical model, nor integrated into mediation analysis to characterize the relationships among treatment, residual tumor burden, and survival. This article presents a frameworkfor landmark mediation survival analyses devised to incorporate longitudinal assessment of tumor burden. R 2 effect-size measures are developed to quantify the survival treatment mediation effects using longitudinal predictors. Analyses are demonstrated with applications to two colorectal cancer trials. Survival prediction is compared in the presence versus absence of longitudinal analysis. Simulation studies elucidate settings wherein patterns of tumor burden dynamics require longitudinal analysis.
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Affiliation(s)
- Jie Zhou
- Department of Biostatistics and Pharmacometrics, Neuroscience Global Drug Development, Novartis, East Hanover, NJ, United States
| | - Xun Jiang
- Center for Design and Analysis, Amgen, Thousand Oaks, CA, United States
| | - H. Amy Xia
- Center for Design and Analysis, Amgen, Thousand Oaks, CA, United States
| | - Brian P. Hobbs
- Department of Population Health, The University of Texas, Austin, TX, United States
| | - Peng Wei
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States,*Correspondence: Peng Wei,
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Morris TT, Heron J, Sanderson ECM, Davey Smith G, Didelez V, Tilling K. Interpretation of Mendelian randomization using a single measure of an exposure that varies over time. Int J Epidemiol 2022; 51:1899-1909. [PMID: 35848950 PMCID: PMC9749705 DOI: 10.1093/ije/dyac136] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 06/15/2022] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Mendelian randomization (MR) is a powerful tool through which the causal effects of modifiable exposures on outcomes can be estimated from observational data. Most exposures vary throughout the life course, but MR is commonly applied to one measurement of an exposure (e.g. weight measured once between ages 40 and 60 years). It has been argued that MR provides biased causal effect estimates when applied to one measure of an exposure that varies over time. METHODS We propose an approach that emphasizes the liability that causes the entire exposure trajectory. We demonstrate this approach using simulations and an applied example. RESULTS We show that rather than estimating the direct or total causal effect of changing the exposure value at a given time, MR estimates the causal effect of changing the underlying liability for the exposure, scaled to the effect of the liability on the exposure at that time. As such, results from MR conducted at different time points are expected to differ (unless the effect of the liability on exposure is constant over time), as we illustrate by estimating the effect of body mass index measured at different ages on systolic blood pressure. CONCLUSION Univariable MR results should not be interpreted as time-point-specific direct or total causal effects, but as the effect of changing the liability for the exposure. Estimates of how the effects of a genetic variant on an exposure vary over time, together with biological knowledge that provides evidence regarding likely effective exposure periods, are required to interpret time-point-specific causal effects.
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Affiliation(s)
- Tim T Morris
- Corresponding author. MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK. E-mail:
| | - Jon Heron
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK,Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Eleanor C M Sanderson
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK,Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK,Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Vanessa Didelez
- Leibniz Institute for Prevention Research and Epidemiology—BIPS, Bremen, Germany,Department of Mathematics and Computer Science, University of Bremen, Bremen, Germany
| | - Kate Tilling
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK,Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
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Avery CL, Howard AG, Ballou AF, Buchanan VL, Collins JM, Downie CG, Engel SM, Graff M, Highland HM, Lee MP, Lilly AG, Lu K, Rager JE, Staley BS, North KE, Gordon-Larsen P. Strengthening Causal Inference in Exposomics Research: Application of Genetic Data and Methods. ENVIRONMENTAL HEALTH PERSPECTIVES 2022; 130:55001. [PMID: 35533073 PMCID: PMC9084332 DOI: 10.1289/ehp9098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Advances in technologies to measure a broad set of exposures have led to a range of exposome research efforts. Yet, these efforts have insufficiently integrated methods that incorporate genetic data to strengthen causal inference, despite evidence that many exposome-associated phenotypes are heritable. Objective: We demonstrate how integration of methods and study designs that incorporate genetic data can strengthen causal inference in exposomics research by helping address six challenges: reverse causation and unmeasured confounding, comprehensive examination of phenotypic effects, low efficiency, replication, multilevel data integration, and characterization of tissue-specific effects. Examples are drawn from studies of biomarkers and health behaviors, exposure domains where the causal inference methods we describe are most often applied. Discussion: Technological, computational, and statistical advances in genotyping, imputation, and analysis, combined with broad data sharing and cross-study collaborations, offer multiple opportunities to strengthen causal inference in exposomics research. Full application of these opportunities will require an expanded understanding of genetic variants that predict exposome phenotypes as well as an appreciation that the utility of genetic variants for causal inference will vary by exposure and may depend on large sample sizes. However, several of these challenges can be addressed through international scientific collaborations that prioritize data sharing. Ultimately, we anticipate that efforts to better integrate methods that incorporate genetic data will extend the reach of exposomics research by helping address the challenges of comprehensively measuring the exposome and its health effects across studies, the life course, and in varied contexts and diverse populations. https://doi.org/10.1289/EHP9098.
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Affiliation(s)
- Christy L Avery
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Carolina Population Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Annie Green Howard
- Department of Biostatistics, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Carolina Population Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Anna F Ballou
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Victoria L Buchanan
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Jason M Collins
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Carolina G Downie
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Stephanie M Engel
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Mariaelisa Graff
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Heather M Highland
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Moa P Lee
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Adam G Lilly
- Carolina Population Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Department of Sociology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Kun Lu
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Julia E Rager
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Brooke S Staley
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Kari E North
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Penny Gordon-Larsen
- Department of Nutrition, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Carolina Population Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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Shi B, Wei P, Huang X. Functional principal component based landmark analysis for the effects of longitudinal cholesterol profiles on the risk of coronary heart disease. Stat Med 2020; 40:650-667. [PMID: 33155338 DOI: 10.1002/sim.8794] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 07/04/2020] [Accepted: 10/10/2020] [Indexed: 12/19/2022]
Abstract
Patients' longitudinal biomarker changing patterns are crucial factors for their disease progression. In this research, we apply functional principal component analysis techniques to extract these changing patterns and use them as predictors in landmark models for dynamic prediction. The time-varying effects of risk factors along a sequence of landmark times are smoothed by a supermodel to borrow information from neighbor time intervals. This results in more stable estimation and more clear demonstration of the time-varying effects. Compared with the traditional landmark analysis, simulation studies show our proposed approach results in lower prediction error rates and higher area under receiver operating characteristic curve (AUC) values, which indicate better ability to discriminate between subjects with different risk levels. We apply our method to data from the Framingham Heart Study, using longitudinal total cholesterol (TC) levels to predict future coronary heart disease (CHD) risk profiles. Our approach not only obtains the overall trend of biomarker-related risk profiles, but also reveals different risk patterns that are not available from the traditional landmark analyses. Our results show that high cholesterol levels during young ages are more harmful than those in old ages. This demonstrates the importance of analyzing the age-dependent effects of TC on CHD risk.
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Affiliation(s)
- Bin Shi
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA.,Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Peng Wei
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Xuelin Huang
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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7
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Vandebergh M, Goris A. Smoking and multiple sclerosis risk: a Mendelian randomization study. J Neurol 2020; 267:3083-3091. [PMID: 32529581 PMCID: PMC7501136 DOI: 10.1007/s00415-020-09980-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 06/03/2020] [Accepted: 06/05/2020] [Indexed: 11/02/2022]
Abstract
BACKGROUND Striking changes in the demographic pattern of multiple sclerosis (MS) strongly indicate an influence of modifiable exposures, which lend themselves well to intervention. It is important to pinpoint which of the many environmental, lifestyle, and sociodemographic changes that have occurred over the past decades, such as higher smoking and obesity rates, are responsible. Mendelian randomization (MR) is an elegant tool to overcome limitations inherent to observational studies and leverage human genetics to inform prevention strategies in MS. METHODS We use genetic variants from the largest genome-wide association study for smoking phenotypes (initiation: N = 378, heaviness: N = 55, lifetime smoking: N = 126) and body mass index (BMI, N = 656) and apply these as instrumental variables in a two-sample MR analysis to the most recent meta-analysis for MS. We adjust for the genetic correlation between smoking and BMI in a multivariable MR. RESULTS In univariable and multivariable MR, smoking does not have an effect on MS risk nor explains part of the association between BMI and MS risk. In contrast, in both analyses each standard deviation increase in BMI, corresponding to roughly 5 kg/m2 units, confers a 30% increase in MS risk. CONCLUSION Despite observational studies repeatedly reporting an association between smoking and increased risk for MS, MR analyses on smoking phenotypes and MS risk could not confirm a causal relationship. This is in contrast with BMI, where observational studies and MR agree on a causal contribution. The reasons for the discrepancy between observational studies and our MR study concerning smoking and MS require further investigation.
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Affiliation(s)
- Marijne Vandebergh
- Department of Neurosciences, Laboratory for Neuroimmunology, KU Leuven, Herestraat 49 bus 1022, 3000, Leuven, Belgium.,Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - An Goris
- Department of Neurosciences, Laboratory for Neuroimmunology, KU Leuven, Herestraat 49 bus 1022, 3000, Leuven, Belgium. .,Leuven Brain Institute, KU Leuven, Leuven, Belgium.
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Fan HY, Huang YT, Hsieh RH, Chao JCJ, Tung YC, Lee YL, Chen YC. Birthweight, time-varying adiposity growth and early menarche in girls: A Mendelian randomisation and mediation analysis. Obes Res Clin Pract 2018; 12:445-451. [PMID: 30082248 DOI: 10.1016/j.orcp.2018.07.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2018] [Revised: 06/21/2018] [Accepted: 07/13/2018] [Indexed: 10/28/2022]
Abstract
OBJECTIVE To explore the causal effect of time-varying z-BMI growth on early menarche using Mendelian randomisation (MR); to identify critical adiposity predictors of early menarche; to compare the effects of birthweight and time-varying z-BMI growth as mediators of the path from genes to early menarche using mediation analysis. METHODS We used data from the Taiwan Children Health Study with 21 obesity-related single-nucleotide polymorphisms (SNPs) to yield genetic (instrumental variable)IVs for adiposity. Children with available data on genotyping, birthweight, adiposity, and menarcheal age were included. RESULTS In MR analyses, results based on the time-varying z-BMI growth show more statistical power and capture more information of adiposity growth (p=0.01) than those based on single point z-BMI (p=0.02). Among adiposity measures, critical predictors of early menarche are fat free mass (RR=1.33, 95% CI 1.07-1.65) and waist/height ratio (RR=1.27, 95% CI 1.03-1.56). Other potential predictors of early menarche are sum of skinfold (RR=1.24, 95% CI 1.03-1.48) and total body fat (RR=1.20, 95% CI 1.05-1.38). In both one-mediation and multi-mediation analyses, time-varying z-BMI growth in the prepubertal years plays a crucial mediator in the pathway from the genes to early menarche. CONCLUSIONS This study discovered that greater prepubertal adiposity growth is a crucial mediator in the path from genes to early menarche. For girls with genes positively associated with obesity; and/or of lower birthweight, a strategy to prevent childhood adiposity should be implemented in order to avoid early menarche development.
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Affiliation(s)
- Hsien-Yu Fan
- Department of Family Medicine, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan; Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan
| | | | - Rong-Hong Hsieh
- School of Nutrition and Health Sciences, College of Nutrition, Taipei Medical University, Taipei, Taiwan
| | - Jane C-J Chao
- School of Nutrition and Health Sciences, College of Nutrition, Taipei Medical University, Taipei, Taiwan; Master Program in Global Health and Development, Taipei Medical University, Taipei, Taiwan; Nutrition Research Center, Taipei Medical University Hospital, Taipei, Taiwan
| | - Yi-Ching Tung
- Department of Pediatrics, National Taiwan University Hospital, Taipei, Taiwan
| | - Yungling L Lee
- Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan; Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan.
| | - Yang-Ching Chen
- Department of Family Medicine, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan; School of Nutrition and Health Sciences, College of Nutrition, Taipei Medical University, Taipei, Taiwan; Department of Family Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan; Graduate Institute of Metabolism and Obesity Sciences, Taipei Medical University, Taipei, Taiwan.
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Millard LAC, Davies NM, Gaunt TR, Davey Smith G, Tilling K. Software Application Profile: PHESANT: a tool for performing automated phenome scans in UK Biobank. Int J Epidemiol 2017; 47:29-35. [PMID: 29040602 PMCID: PMC5837456 DOI: 10.1093/ije/dyx204] [Citation(s) in RCA: 96] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/30/2017] [Indexed: 01/21/2023] Open
Abstract
Motivation Epidemiological cohorts typically contain a diverse set of phenotypes such that automation of phenome scans is non-trivial, because they require highly heterogeneous models. For this reason, phenome scans have to date tended to use a smaller homogeneous set of phenotypes that can be analysed in a consistent fashion. We present PHESANT (PHEnome Scan ANalysis Tool), a software package for performing comprehensive phenome scans in UK Biobank. General features PHESANT tests the association of a specified trait with all continuous, integer and categorical variables in UK Biobank, or a specified subset. PHESANT uses a novel rule-based algorithm to determine how to appropriately test each trait, then performs the analyses and produces plots and summary tables. Implementation The PHESANT phenome scan is implemented in R. PHESANT includes a novel Javascript D3.js visualization and accompanying Java code that converts the phenome scan results to the required JavaScript Object Notation (JSON) format. Availability PHESANT is available on GitHub at [https://github.com/MRCIEU/PHESANT]. Git tag v0.5 corresponds to the version presented here.
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Affiliation(s)
- Louise A C Millard
- MRC Integrative Epidemiology Unit (IEU), School of Social and Community Medicine.,Intelligent Systems Laboratory, Department of Computer Science, University of Bristol, Bristol, UK
| | - Neil M Davies
- MRC Integrative Epidemiology Unit (IEU), School of Social and Community Medicine
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit (IEU), School of Social and Community Medicine
| | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), School of Social and Community Medicine
| | - Kate Tilling
- MRC Integrative Epidemiology Unit (IEU), School of Social and Community Medicine
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