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Sanderson E, Tilling K. Estimation of time-varying causal effects with multivariable Mendelian randomization: the importance of model specification. Int J Epidemiol 2024; 53:dyae100. [PMID: 39067480 DOI: 10.1093/ije/dyae100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 07/26/2024] [Indexed: 07/30/2024] Open
Affiliation(s)
- Eleanor Sanderson
- MRC Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, UK
| | - Kate Tilling
- MRC Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, UK
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2
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Bergstedt J, Pasman JA, Ma Z, Harder A, Yao S, Parker N, Treur JL, Smit DJA, Frei O, Shadrin AA, Meijsen JJ, Shen Q, Hägg S, Tornvall P, Buil A, Werge T, Hjerling-Leffler J, Als TD, Børglum AD, Lewis CM, McIntosh AM, Valdimarsdóttir UA, Andreassen OA, Sullivan PF, Lu Y, Fang F. Distinct biological signature and modifiable risk factors underlie the comorbidity between major depressive disorder and cardiovascular disease. NATURE CARDIOVASCULAR RESEARCH 2024; 3:754-769. [PMID: 39215135 PMCID: PMC11182748 DOI: 10.1038/s44161-024-00488-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 05/08/2024] [Indexed: 06/21/2024]
Abstract
Major depressive disorder (MDD) and cardiovascular disease (CVD) are often comorbid, resulting in excess morbidity and mortality. Here we show that CVDs share most of their genetic risk factors with MDD. Multivariate genome-wide association analysis of shared genetic liability between MDD and atherosclerotic CVD revealed seven loci and distinct patterns of tissue and brain cell-type enrichments, suggesting the involvement of the thalamus. Part of the genetic overlap was explained by shared inflammatory, metabolic and psychosocial or lifestyle risk factors. Our data indicated causal effects of genetic liability to MDD on CVD risk, but not from most CVDs to MDD, and showed that the causal effects were partly explained by metabolic and psychosocial or lifestyle factors. The distinct signature of MDD-atherosclerotic CVD comorbidity suggests an immunometabolic subtype of MDD that is more strongly associated with CVD than overall MDD. In summary, we identified biological mechanisms underlying MDD-CVD comorbidity and modifiable risk factors for prevention of CVD in individuals with MDD.
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Affiliation(s)
- Jacob Bergstedt
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
| | - Joëlle A Pasman
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Ziyan Ma
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Arvid Harder
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Shuyang Yao
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Nadine Parker
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Jorien L Treur
- Genetic Epidemiology, Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Dirk J A Smit
- Genetic Epidemiology, Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Oleksandr Frei
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Alexey A Shadrin
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Joeri J Meijsen
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
| | - Qing Shen
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Clinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, Tongji University School of Medicine, Shanghai, China
- Institute for Advanced Study, Tongji University, Shanghai, China
| | - Sara Hägg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Per Tornvall
- Department of Clinical Science and Education Södersjukhuset, Karolinska Institutet, Stockholm, Sweden
| | - Alfonso Buil
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
| | - Thomas Werge
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jens Hjerling-Leffler
- Department Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Thomas D Als
- Department of Molecular Medicine (MOMA), Molecular Diagnostic Laboratory, Aarhus University Hospital, Aarhus, Denmark
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Anders D Børglum
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Cathryn M Lewis
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, UK
- Department of Medical and Molecular Genetics, King's College London, London, UK
| | - Andrew M McIntosh
- Centre for Clinical Brain Sciences, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
- Centre for Genomics and Experimental Medicine, University of Edinburgh, Edinburgh, UK
| | - Unnur A Valdimarsdóttir
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Centre of Public Health Sciences, Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
- Department of Epidemiology, Harvard TH Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Ole A Andreassen
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Patrick F Sullivan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yi Lu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Fang Fang
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
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Lawton M, Ben-Shlomo Y, Gkatzionis A, Hu MT, Grosset D, Tilling K. Two sample Mendelian Randomisation using an outcome from a multilevel model of disease progression. Eur J Epidemiol 2024; 39:521-533. [PMID: 38281297 PMCID: PMC11219432 DOI: 10.1007/s10654-023-01093-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 12/21/2023] [Indexed: 01/30/2024]
Abstract
Identifying factors that are causes of disease progression, especially in neurodegenerative diseases, is of considerable interest. Disease progression can be described as a trajectory of outcome over time-for example, a linear trajectory having both an intercept (severity at time zero) and a slope (rate of change). A technique for identifying causal relationships between one exposure and one outcome in observational data whilst avoiding bias due to confounding is two sample Mendelian Randomisation (2SMR). We consider a multivariate approach to 2SMR using a multilevel model for disease progression to estimate the causal effect an exposure has on the intercept and slope. We carry out a simulation study comparing a naïve univariate 2SMR approach to a multivariate 2SMR approach with one exposure that effects both the intercept and slope of an outcome that changes linearly with time since diagnosis. The simulation study results, across six different scenarios, for both approaches were similar with no evidence against a non-zero bias and appropriate coverage of the 95% confidence intervals (for intercept 93.4-96.2% and the slope 94.5-96.0%). The multivariate approach gives a better joint coverage of both the intercept and slope effects. We also apply our method to two Parkinson's cohorts to examine the effect body mass index has on disease progression. There was no strong evidence that BMI affects disease progression, however the confidence intervals for both intercept and slope were wide.
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Affiliation(s)
- Michael Lawton
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Yoav Ben-Shlomo
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Apostolos Gkatzionis
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Michele T Hu
- Nuffield Department of Clinical Neurosciences, Oxford University and Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Donald Grosset
- School of Neuroscience and Psychology, University of Glasgow, Glasgow, UK
| | - Kate Tilling
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
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4
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Zhu R, Zhang N, Zhu H, Li F, Xu H. Major depressive disorder and the risk of irritable bowel syndrome: A Mendelian randomization study. Mol Genet Genomic Med 2024; 12:e2413. [PMID: 38439604 PMCID: PMC10912794 DOI: 10.1002/mgg3.2413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 10/08/2023] [Accepted: 02/14/2024] [Indexed: 03/06/2024] Open
Abstract
BACKGROUND The association between major depressive disorder (MDD) and irritable bowel syndrome (IBS) has been found in observational research; however, the causative relationship between MDD and IBS remains uncertain. Using the two-sample Mendelian randomization (MR) approach, we attempted to examine the causal effect of MDD on IBS. METHODS Independent genetic variants for MDD identified by Howard et al. based on a genome-wide meta-analysis were selected for this study. Gene-Outcome associations for IBS were gathered from UK Biobank and FinnGen databases. The MR analysis included inverse variance weighted (IVW), MR-Egger regression, weighted median, weighted mode, and MR-PRESSO sensitivity analyses. RESULTS FinnGen database subjected to inverse variance weighted (IVW) analysis revealed that MDD may be a risk factor for the development of IBS (OR = 1.356, 95% CI: 1.125-1.632, p = 0.0013). The same finding was reached in UK Biobank for IVW (OR = 1.011, 95% CI: 1.006-1.015, p = 3.18 × 10-7 ), MR-Egger progression (OR = 1.030, 95% CI: 1.008-1.051, p = 0.007), and weighted median (OR = 1.011, 95% CI: 1.005-1.016, p = 0.0001). CONCLUSION Our findings supported a causal relationship between MDD and IBS, which may have implications for the clinical management of IBS in individuals with MDD.
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Affiliation(s)
- Ruiming Zhu
- Department of EndoscopyThe First Hospital of Jilin UniversityChangchunJilinChina
| | - Nan Zhang
- Department of EndoscopyThe First Hospital of Jilin UniversityChangchunJilinChina
| | - He Zhu
- Department of EndoscopyThe First Hospital of Jilin UniversityChangchunJilinChina
| | - Fudong Li
- Department of EndoscopyThe First Hospital of Jilin UniversityChangchunJilinChina
| | - Hong Xu
- Department of EndoscopyThe First Hospital of Jilin UniversityChangchunJilinChina
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Millard LAC, Davey Smith G, Tilling K. Using the global randomization test as a Mendelian randomization falsification test for the exclusion restriction assumption. Eur J Epidemiol 2024:10.1007/s10654-024-01097-6. [PMID: 38421485 DOI: 10.1007/s10654-024-01097-6] [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: 05/09/2022] [Accepted: 01/06/2024] [Indexed: 03/02/2024]
Abstract
Mendelian randomization may give biased causal estimates if the instrument affects the outcome not solely via the exposure of interest (violating the exclusion restriction assumption). We demonstrate use of a global randomization test as a falsification test for the exclusion restriction assumption. Using simulations, we explored the statistical power of the randomization test to detect an association between a genetic instrument and a covariate set due to (a) selection bias or (b) horizontal pleiotropy, compared to three approaches examining associations with individual covariates: (i) Bonferroni correction for the number of covariates, (ii) correction for the effective number of independent covariates, and (iii) an r2 permutation-based approach. We conducted proof-of-principle analyses in UK Biobank, using CRP as the exposure and coronary heart disease (CHD) as the outcome. In simulations, power of the randomization test was higher than the other approaches for detecting selection bias when the correlation between the covariates was low (r2 < 0.1), and at least as powerful as the other approaches across all simulated horizontal pleiotropy scenarios. In our applied example, we found strong evidence of selection bias using all approaches (e.g., global randomization test p < 0.002). We identified 51 of the 58 CRP genetic variants as horizontally pleiotropic, and estimated effects of CRP on CHD attenuated somewhat to the null when excluding these from the genetic risk score (OR = 0.96 [95% CI: 0.92, 1.00] versus 0.97 [95% CI: 0.90, 1.05] per 1-unit higher log CRP levels). The global randomization test can be a useful addition to the MR researcher's toolkit.
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Affiliation(s)
- Louise A C Millard
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Kate Tilling
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
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6
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Khouja JN, Sanderson E, Wootton RE, Taylor AE, Church BA, Richmond RC, Munafò MR. Estimating the health impact of nicotine exposure by dissecting the effects of nicotine versus non-nicotine constituents of tobacco smoke: A multivariable Mendelian randomisation study. PLoS Genet 2024; 20:e1011157. [PMID: 38335242 PMCID: PMC10883537 DOI: 10.1371/journal.pgen.1011157] [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: 08/09/2023] [Revised: 02/22/2024] [Accepted: 01/29/2024] [Indexed: 02/12/2024] Open
Abstract
The detrimental health effects of smoking are well-known, but the impact of regular nicotine use without exposure to the other constituents of tobacco is less clear. Given the increasing daily use of alternative nicotine delivery systems, such as e-cigarettes, it is increasingly important to understand and separate the effects of nicotine use from the impact of tobacco smoke exposure. Using a multivariable Mendelian randomisation framework, we explored the direct effects of nicotine compared with the non-nicotine constituents of tobacco smoke on health outcomes (lung cancer, chronic obstructive pulmonary disease [COPD], forced expiratory volume in one second [FEV-1], forced vital capacity [FVC], coronary heart disease [CHD], and heart rate [HR]). We used Genome-Wide Association Study (GWAS) summary statistics from Buchwald and colleagues, the GWAS and Sequencing Consortium of Alcohol and Nicotine, the International Lung Cancer Consortium, and UK Biobank. Increased nicotine metabolism increased the risk of COPD, lung cancer, and lung function in the univariable analysis. However, when accounting for smoking heaviness in the multivariable analysis, we found that increased nicotine metabolite ratio (indicative of decreased nicotine exposure per cigarette smoked) decreases heart rate (b = -0.30, 95% CI -0.50 to -0.10) and lung function (b = -33.33, 95% CI -41.76 to -24.90). There was no clear evidence of an effect on the remaining outcomes. The results suggest that these smoking-related outcomes are not due to nicotine exposure but are caused by the other components of tobacco smoke; however, there are multiple potential sources of bias, and the results should be triangulated using evidence from a range of methodologies.
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Affiliation(s)
- Jasmine N. Khouja
- School of Psychological Science, University of Bristol, Bristol, United Kingdom
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
| | - Eleanor Sanderson
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Robyn E. Wootton
- School of Psychological Science, University of Bristol, Bristol, United Kingdom
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Nic Waals Institute, Lovisenberg diakonale sykehus, Oslo, Norway
| | - Amy E. Taylor
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Billy A. Church
- School of Psychology and Vision Sciences, University of Leicester, United Kingdom
| | - Rebecca C. Richmond
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Marcus R. Munafò
- School of Psychological Science, University of Bristol, Bristol, United Kingdom
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- NIHR Bristol Biomedical Research Centre, Bristol, United Kingdom
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7
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Dobrijevic E, van Zwieten A, Kiryluk K, Grant AJ, Wong G, Teixeira-Pinto A. Mendelian randomization for nephrologists. Kidney Int 2023; 104:1113-1123. [PMID: 37783446 DOI: 10.1016/j.kint.2023.09.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 08/11/2023] [Accepted: 09/01/2023] [Indexed: 10/04/2023]
Abstract
Confounding is a major limitation of observational studies. Mendelian randomization (MR) is a powerful study design that uses genetic variants as instrumental variables to enable examination of the causal effect of an exposure on an outcome in observational data. With the emergence of large-scale genome-wide association studies in nephrology over the past decade, MR has become a popular method to establish causal inferences. However, MR is a complex and challenging methodology that requires careful consideration to ensure robust results. This review article aims to summarize the basic concepts of MR, its application and relevance in nephrology, and the methodological challenges and limitations as well as discuss the current guidelines for design and reporting. With reference to a clinically relevant example of examining the causal relationship between the estimated glomerular filtration rate and cancer, this review outlines the key steps to conducting an MR study, including the key considerations and potential pitfalls at each step. These include defining the clinical question, selecting the data sources, identifying and refining appropriate genetic variants by considering linkage disequilibrium and associations with potential confounders, harmonization of variants across data sets, validation of the genetic instrument by assessing its strength, estimation of the causal effects, confirming the validity of the findings, and interpreting and reporting results.
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Affiliation(s)
- Ellen Dobrijevic
- Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia; Centre for Kidney Research, Kids Research Institute, The Children's Hospital at Westmead, Westmead, New South Wales, Australia.
| | - Anita van Zwieten
- Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia; Centre for Kidney Research, Kids Research Institute, The Children's Hospital at Westmead, Westmead, New South Wales, Australia
| | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York, USA
| | - Andrew J Grant
- Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | - Germaine Wong
- Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia; Centre for Kidney Research, Kids Research Institute, The Children's Hospital at Westmead, Westmead, New South Wales, Australia; Centre for Transplant and Renal Research, Westmead Hospital, Westmead, New South Wales, Australia
| | - Armando Teixeira-Pinto
- Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia; Centre for Kidney Research, Kids Research Institute, The Children's Hospital at Westmead, Westmead, New South Wales, Australia
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8
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Havdahl A, Hughes AM, Sanderson E, Ask H, Cheesman R, Reichborn-Kjennerud T, Andreassen OA, Corfield EC, Hannigan L, Magnus P, Njølstad PR, Stoltenberg C, Torvik FA, Brandlistuen R, Smith GD, Ystrom E, Davies NM. Intergenerational effects of parental educational attainment on parenting and childhood educational outcomes: Evidence from MoBa using within-family Mendelian randomization. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.22.23285699. [PMID: 36865116 PMCID: PMC9980223 DOI: 10.1101/2023.02.22.23285699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
The intergenerational transmission of educational attainment from parents to their children is one of the most important and studied relationships in social science. Longitudinal studies have found strong associations between parents' and their children's educational outcomes, which could be due to the effects of parents. Here we provide new evidence about whether parents' educational attainment affects their parenting behaviours and children's early educational outcomes using within-family Mendelian randomization and data from 40,879 genotyped parent-child trios from the Norwegian Mother, Father and Child Cohort (MoBa) study. We found evidence suggesting that parents' educational attainment affects their children's educational outcomes from age 5 to 14. More studies are needed to provide more samples of parent-child trios and assess the potential consequences of selection bias and grandparental effects.
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Affiliation(s)
- Alexandra Havdahl
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, BS8 2BN, United Kingdom
- Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Amanda M Hughes
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, BS8 2BN, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, United Kingdom
| | - Eleanor Sanderson
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, BS8 2BN, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, United Kingdom
| | - Helga Ask
- Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Rosa Cheesman
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Ted Reichborn-Kjennerud
- Department of Child Health and Development, Norwegian Institute of Public Health, Oslo, Norway
| | - Ole A. Andreassen
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Elizabeth C. Corfield
- Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
| | - Laurie Hannigan
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, BS8 2BN, United Kingdom
- Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
| | - Per Magnus
- Department of Child Health and Development, Norwegian Institute of Public Health, Oslo, Norway
| | - Pål R. Njølstad
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
- Children and Youth Clinic, Haukeland University Hospital, Bergen, Norway
| | - Camilla Stoltenberg
- Department of Child Health and Development, Norwegian Institute of Public Health, Oslo, Norway
| | - Fartein Ask Torvik
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Ragnhild Brandlistuen
- Department of Child Health and Development, Norwegian Institute of Public Health, Oslo, Norway
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, BS8 2BN, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, United Kingdom
| | - Eivind Ystrom
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- Department of Child Health and Development, Norwegian Institute of Public Health, Oslo, Norway
| | - Neil M Davies
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, BS8 2BN, United Kingdom
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Norway
- Division of Psychiatry, University College London, Maple House, 149 Tottenham Court Rd, London W1T 7NF
- Department of Statistical Sciences, University College London, London WC1E 6BT, UK
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9
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Weintraub WS, Boden WE. Can we measurably improve the prediction of recurrent coronary artery disease events? Eur Heart J 2023; 44:3466-3468. [PMID: 37738645 DOI: 10.1093/eurheartj/ehad464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/24/2023] Open
Affiliation(s)
- William S Weintraub
- MedStar Health Research Institute and Department of Medicine, Georgetown University, Washington, DC 20057, USA
| | - William E Boden
- Department of Medicine, Veterans Affairs Boston Healthcare System and Department of Medicine, Boston University School of Medicine, Boston, MA, USA
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10
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Burgess S, Davey Smith G, Davies NM, Dudbridge F, Gill D, Glymour MM, Hartwig FP, Kutalik Z, Holmes MV, Minelli C, Morrison JV, Pan W, Relton CL, Theodoratou E. Guidelines for performing Mendelian randomization investigations: update for summer 2023. Wellcome Open Res 2023; 4:186. [PMID: 32760811 PMCID: PMC7384151 DOI: 10.12688/wellcomeopenres.15555.3] [Citation(s) in RCA: 180] [Impact Index Per Article: 180.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/19/2023] [Indexed: 08/08/2023] Open
Abstract
This paper provides guidelines for performing Mendelian randomization investigations. It is aimed at practitioners seeking to undertake analyses and write up their findings, and at journal editors and reviewers seeking to assess Mendelian randomization manuscripts. The guidelines are divided into ten sections: motivation and scope, data sources, choice of genetic variants, variant harmonization, primary analysis, supplementary and sensitivity analyses (one section on robust statistical methods and one on other approaches), extensions and additional analyses, data presentation, and interpretation. These guidelines will be updated based on feedback from the community and advances in the field. Updates will be made periodically as needed, and at least every 24 months.
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Affiliation(s)
- Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- BHF Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Neil M. Davies
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Division of Psychiatry, University College London, London, UK
- Department of Statistical Sciences, University College London, London, WC1E 6BT, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Frank Dudbridge
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - M. Maria Glymour
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Fernando P. Hartwig
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil
| | - Zoltán Kutalik
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- University Center for Primary Care and Public Health (Unisanté), Lausanne, Switzerland
| | - Michael V. Holmes
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Cosetta Minelli
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Jean V. Morrison
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Wei Pan
- Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA
| | - Caroline L. Relton
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Evropi Theodoratou
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
- Edinburgh Cancer Research Centre, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
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11
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Ng NYT, Zhao JV, Mak CCY, Lee SL, Chung BHY. Asthma subtypes and risk of cardiovascular disease: A Mendelian randomization study. Pediatr Allergy Immunol 2023; 34:e13987. [PMID: 37492908 DOI: 10.1111/pai.13987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 06/13/2023] [Accepted: 06/16/2023] [Indexed: 07/27/2023]
Affiliation(s)
- Nicole Y T Ng
- Department of Paediatrics and Adolescent Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Jie V Zhao
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Christopher C Y Mak
- Department of Paediatrics and Adolescent Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - So Lun Lee
- Department of Paediatrics and Adolescent Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Brian H Y Chung
- Department of Paediatrics and Adolescent Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
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12
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Baumeister SE, Reckelkamm SL, Grabe HJ, Nauck M, Klinger-König J, Völzke H, Kocher T, Friedrich N, Holtfreter B. Cortisol and periodontitis: Prospective observational and Mendelian randomization studies. Front Endocrinol (Lausanne) 2023; 14:1100985. [PMID: 37008927 PMCID: PMC10050732 DOI: 10.3389/fendo.2023.1100985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 03/01/2023] [Indexed: 03/17/2023] Open
Abstract
PURPOSE Cortisol has obesogenic, hyperglycemic and immunomodulating effects. Preclinical and observational research suggested that it is associated with periodontitis but the evidence for potential causality in humans is sparse. We triangulated results from prospective observational and Mendelian randomization (MR) analyses to further explore this. METHODS Using pooled data from 3,388 participants of two population cohort studies embedded in the Study of Health in Pomerania (SHIP) project, we associated serum cortisol levels with periodontal outcomes measured after a median follow-up time of 6.9 years, adjusting for confounding and selection bias using propensity score weighting and multiple imputation. We further examined the effect of genetically proxied plasma morning cortisol levels on periodontitis using two-sample MR of 17,353 cases and 28,210 controls. RESULTS In SHIP, we found that cortisol levels were positively associated with follow-up levels of mean clinical attachment level (CAL), deep interdental CAL and bleeding on probing but were unrelated to mean probing pocket depth and deep periodontal pockets. In MR analysis, cortisol was not associated with periodontitis. CONCLUSION The observational study revealed a prospective association of spot cortisol with makers of periodontitis. Contrary to observational studies, genetically instrumented, long-term cortisol was unrelated to periodontitis. Our results find no univocal evidence that cortisol plays a role in periodontitis pathology, casting doubt on cortisol-related pathways.
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Affiliation(s)
- Sebastian-Edgar Baumeister
- Institute of Health Services Research in Dentistry, University of Münster, Münster, Germany
- *Correspondence: Sebastian-Edgar Baumeister,
| | - Stefan Lars Reckelkamm
- Institute of Health Services Research in Dentistry, University of Münster, Münster, Germany
| | - Hans-Jörgen Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Matthias Nauck
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, University Medicine, Greifswald, Germany
| | - Johanna Klinger-König
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Henry Völzke
- DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, University Medicine, Greifswald, Germany
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Thomas Kocher
- Department of Restorative Dentistry, Periodontology, Endodontology, and Preventive and Pediatric Dentistry, University Medicine Greifswald, Greifswald, Germany
| | - Nele Friedrich
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, University Medicine, Greifswald, Germany
| | - Birte Holtfreter
- Department of Restorative Dentistry, Periodontology, Endodontology, and Preventive and Pediatric Dentistry, University Medicine Greifswald, Greifswald, Germany
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13
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Effects of Selenium on Chronic Kidney Disease: A Mendelian Randomization Study. Nutrients 2022; 14:nu14214458. [PMID: 36364721 PMCID: PMC9654848 DOI: 10.3390/nu14214458] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 10/18/2022] [Accepted: 10/21/2022] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND Previous observational studies have shown that there is a controversial association between selenium levels and chronic kidney disease (CKD). Our aim was to assess the causal relationship between selenium levels and CKD using Mendelian randomization (MR) analysis. METHODS We used the two-sample Mendelian randomization (MR) method to analyze the causal role of selenium levels on CKD risk. The variants associated with selenium levels were extracted from a large genome-wide association study (GWAS) meta-analysis of circulating selenium levels (n = 5477) and toenail selenium levels (n = 4162) in the European population. Outcome data were from the largest GWAS meta-analysis of European-ancestry participants for kidney function to date. Inverse variance weighted (IVW) method was used as the main analysis and a series of sensitivity analyses were carried out to detect potential violations of MR assumptions. RESULTS The MR analysis results indicate that the genetically predicted selenium levels were associated with decreased estimated glomerular filtration (eGFR) (effect = -0.0042, 95% confidence interval [CI]: -0.0053-0.0031, p = 2.186 × 10-13) and increased blood urea nitrogen (BUN) (effect = 0.0029, 95% confidence interval [CI]: 0.0006-0.0052, p = 0.0136) with no pleiotropy detected. CONCLUSIONS The MR study indicated that an increased level of selenium is a causative factor for kidney function impairment.
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Richmond RC, Sanderson E. Methods and practical considerations for performing Mendelian randomization. Int J Epidemiol 2022. [DOI: 10.1093/ije/dyac166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Rebecca C Richmond
- MRC Integrative Epidemiology Unit, Bristol University , Bristol, UK E-mail:
| | - Eleanor Sanderson
- MRC Integrative Epidemiology Unit, Bristol University , Bristol, UK E-mail:
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Sanderson E, Richardson TG, Morris TT, Tilling K, Davey Smith G. Estimation of causal effects of a time-varying exposure at multiple time points through multivariable mendelian randomization. PLoS Genet 2022; 18:e1010290. [PMID: 35849575 PMCID: PMC9348730 DOI: 10.1371/journal.pgen.1010290] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 08/03/2022] [Accepted: 06/09/2022] [Indexed: 12/15/2022] Open
Abstract
Mendelian Randomisation (MR) is a powerful tool in epidemiology that can be used to estimate the causal effect of an exposure on an outcome in the presence of unobserved confounding, by utilising genetic variants as instrumental variables (IVs) for the exposure. The effect estimates obtained from MR studies are often interpreted as the lifetime effect of the exposure in question. However, the causal effects of some exposures are thought to vary throughout an individual's lifetime with periods during which an exposure has a greater effect on a particular outcome. Multivariable MR (MVMR) is an extension of MR that allows for multiple, potentially highly related, exposures to be included in an MR estimation. MVMR estimates the direct effect of each exposure on the outcome conditional on all the other exposures included in the estimation. We explore the use of MVMR to estimate the direct effect of a single exposure at different time points in an individual's lifetime on an outcome. We use simulations to illustrate the interpretation of the results from such analyses and the key assumptions required. We show that causal effects at different time periods can be estimated through MVMR when the association between the genetic variants used as instruments and the exposure measured at those time periods varies. However, this estimation will not necessarily identify exact time periods over which an exposure has the most effect on the outcome. Prior knowledge regarding the biological basis of exposure trajectories can help interpretation. We illustrate the method through estimation of the causal effects of childhood and adult BMI on C-Reactive protein and smoking behaviour.
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Affiliation(s)
- Eleanor Sanderson
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Tom G. Richardson
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- Novo Nordisk Research Centre, Headington, Oxford, United Kingdom
| | - Tim T. Morris
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Kate Tilling
- MRC Integrative Epidemiology Unit at the 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 at the University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
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