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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Lee KJ, Tilling KM, Cornish RP, Little RJA, Bell ML, Goetghebeur E, Hogan JW, Carpenter JR. Framework for the treatment and reporting of missing data in observational studies: The Treatment And Reporting of Missing data in Observational Studies framework. J Clin Epidemiol 2021; 134:79-88. [PMID: 33539930 PMCID: PMC8168830 DOI: 10.1016/j.jclinepi.2021.01.008] [Citation(s) in RCA: 104] [Impact Index Per Article: 34.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 12/15/2020] [Accepted: 01/13/2021] [Indexed: 12/17/2022]
Abstract
Missing data are ubiquitous in medical research. Although there is increasing guidance on how to handle missing data, practice is changing slowly and misapprehensions abound, particularly in observational research. Importantly, the lack of transparency around methodological decisions is threatening the validity and reproducibility of modern research. We present a practical framework for handling and reporting the analysis of incomplete data in observational studies, which we illustrate using a case study from the Avon Longitudinal Study of Parents and Children. The framework consists of three steps: 1) Develop an analysis plan specifying the analysis model and how missing data are going to be addressed. An important consideration is whether a complete records' analysis is likely to be valid, whether multiple imputation or an alternative approach is likely to offer benefits and whether a sensitivity analysis regarding the missingness mechanism is required; 2) Examine the data, checking the methods outlined in the analysis plan are appropriate, and conduct the preplanned analysis; and 3) Report the results, including a description of the missing data, details on how the missing data were addressed, and the results from all analyses, interpreted in light of the missing data and the clinical relevance. This framework seeks to support researchers in thinking systematically about missing data and transparently reporting the potential effect on the study results, therefore increasing the confidence in and reproducibility of research findings.
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Affiliation(s)
- Katherine J Lee
- Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Melbourne, Australia; Department of Paediatrics, University of Melbourne, Melbourne, Australia.
| | - Kate M Tilling
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Rosie P Cornish
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | | | - Melanie L Bell
- Department of Epidemiology and Biostatistics, University of Arizona, AZ, USA
| | - Els Goetghebeur
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | | | - James R Carpenter
- MRC Clinical Trials Unit, London School of Hygiene and Tropical Medicine, London, UK
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Yarmolinsky J, Wade KH, Richmond RC, Langdon RJ, Bull CJ, Tilling KM, Relton CL, Lewis SJ, Davey Smith G, Martin RM. Causal Inference in Cancer Epidemiology: What Is the Role of Mendelian Randomization? Cancer Epidemiol Biomarkers Prev 2018; 27:995-1010. [PMID: 29941659 PMCID: PMC6522350 DOI: 10.1158/1055-9965.epi-17-1177] [Citation(s) in RCA: 85] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2017] [Revised: 02/15/2018] [Accepted: 06/05/2018] [Indexed: 02/07/2023] Open
Abstract
Observational epidemiologic studies are prone to confounding, measurement error, and reverse causation, undermining robust causal inference. Mendelian randomization (MR) uses genetic variants to proxy modifiable exposures to generate more reliable estimates of the causal effects of these exposures on diseases and their outcomes. MR has seen widespread adoption within cardio-metabolic epidemiology, but also holds much promise for identifying possible interventions for cancer prevention and treatment. However, some methodologic challenges in the implementation of MR are particularly pertinent when applying this method to cancer etiology and prognosis, including reverse causation arising from disease latency and selection bias in studies of cancer progression. These issues must be carefully considered to ensure appropriate design, analysis, and interpretation of such studies. In this review, we provide an overview of the key principles and assumptions of MR, focusing on applications of this method to the study of cancer etiology and prognosis. We summarize recent studies in the cancer literature that have adopted a MR framework to highlight strengths of this approach compared with conventional epidemiological studies. Finally, limitations of MR and recent methodologic developments to address them are discussed, along with the translational opportunities they present to inform public health and clinical interventions in cancer. Cancer Epidemiol Biomarkers Prev; 27(9); 995-1010. ©2018 AACR.
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Affiliation(s)
- James Yarmolinsky
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Kaitlin H Wade
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Rebecca C Richmond
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Ryan J Langdon
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Caroline J Bull
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Kate M Tilling
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Caroline L Relton
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Sarah J Lewis
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Richard M Martin
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
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Burton AJ, Tilling KM, Holly JM, Hamdy FC, Rowlands MAE, Donovan JL, Martin RM. Metabolic imbalance and prostate cancer progression. Int J Mol Epidemiol Genet 2010; 1:248-271. [PMID: 21532839 PMCID: PMC3076778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Received: 06/25/2010] [Accepted: 07/20/2010] [Indexed: 05/30/2023]
Abstract
There is substantial evidence implicating environmental factors in the progression of prostate cancer. The metabolic consequences of a western lifestyle, such as obesity, insulin resistance and abnormal hormone production have been linked to prostate carcinogenesis through multiple overlapping pathways. Insulin resistance results in raised levels of the mitogens insulin and insulin-like growth factor-1, both of which may affect prostate cancer directly, or through their effect on other metabolic regulators. Obesity is associated with abnormal levels of adipocyte-derived peptides (adipokines), sex hormones and inflammatory cytokines. Adipokines have been shown to influence prostate cancer in both cell culture studies and observational, population level studies. Testosterone appears to have a complex relationship with prostate carcinogenesis, and it has been suggested that the lower levels associated with obesity may select for more aggressive androgen independent prostate cancer cells. Prostatic inflammation, caused by infection, urinary reflux or dietary toxins, frequently occurs prior to cancer development and may influence progression to advanced disease. High levels of ω-6 fatty acids in the diet may lead to the production of further inflammatory molecules that may influence prostate cancer. Increased fatty acid metabolism occurs within tumour cells, providing a potential target for prostate cancer therapies. Aberrations in amino acid metabolism have also been identified in prostate cancer tissue, particularly in metastatic cancer. This evidence indicates lifestyle interventions may be effective in reducing the incidence of clinical disease. However, much more research is needed before recommendations are made.
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