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Elmore A, Adhikari N, Hartley AE, Javier Aparicio H, Posner DC, Hemani G, Tilling K, Gaunt TR, Wilson P, Casas JP, Michael Gaziano J, Smith GD, Paternoster L, Cho K, Peloso GM. Protein identification for stroke progression via Mendelian Randomization in Million Veteran Program and UK Biobank. medRxiv 2024:2024.01.31.24302111. [PMID: 38352469 PMCID: PMC10863017 DOI: 10.1101/2024.01.31.24302111] [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] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/19/2024]
Abstract
Background Individuals who have experienced a stroke, or transient ischemic attack, face a heightened risk of future cardiovascular events. Identification of genetic and molecular risk factors for subsequent cardiovascular outcomes may identify effective therapeutic targets to improve prognosis after an incident stroke. Methods We performed genome-wide association studies (GWAS) for subsequent major adverse cardiovascular events (MACE) (Ncases=51,929, Ncntrl=39,980) and subsequent arterial ischemic stroke (AIS) Ncases=45,120, Ncntrl=46,789) after first incident stroke within the Million Veteran Program and UK Biobank. We then used genetic variants associated with proteins (pQTLs) to determine the effect of 1,463 plasma protein abundances on subsequent MACE using Mendelian randomization (MR). Results Two variants were significantly associated with subsequent cardiovascular events: rs76472767 (OR=0.75, 95% CI = 0.64-0.85, p= 3.69×10-08) with subsequent AIS and rs13294166 (OR=1.52, 95% CI = 1.37-1.67, p=3.77×10-08) with subsequent MACE. Using MR, we identified 2 proteins with an effect on subsequent MACE after a stroke: CCL27 (effect OR= 0.77, 95% CI = 0.66-0.88, adj. p=0.05), and TNFRSF14 (effect OR=1.42, 95% CI = 1.24-1.60, adj. p=0.006). These proteins are not associated with incident AIS and are implicated to have a role in inflammation. Conclusions We found evidence that two proteins with little effect on incident stroke appear to influence subsequent MACE after incident AIS. These associations suggest that inflammation is a contributing factor to subsequent MACE outcomes after incident AIS and highlights potential novel targets.
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Affiliation(s)
- Andrew Elmore
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and University of Bristol
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol
| | - Nimish Adhikari
- Veteran’s Affairs Healthcare System, Boston, MA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - April E Hartley
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and University of Bristol
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol
| | - Hugo Javier Aparicio
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA
- Boston Medical Center, Boston, MA
| | | | - Gibran Hemani
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and University of Bristol
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol
| | - Kate Tilling
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and University of Bristol
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol
| | - Tom R Gaunt
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and University of Bristol
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol
| | | | - JP Casas
- Veteran’s Affairs Healthcare System, Boston, MA
- Division of Aging, Brigham and Women’s Hospital, Harvard Medical School
| | - John Michael Gaziano
- Veteran’s Affairs Healthcare System, Boston, MA
- Division of Aging, Brigham and Women’s Hospital, Harvard Medical School
| | - George Davey Smith
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and University of Bristol
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol
| | - Lavinia Paternoster
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and University of Bristol
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol
| | - Kelly Cho
- Veteran’s Affairs Healthcare System, Boston, MA
- Division of Aging, Brigham and Women’s Hospital, Harvard Medical School
| | - Gina M Peloso
- Veteran’s Affairs Healthcare System, Boston, MA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
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2
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Zheng J, Wheeler E, Pietzner M, Andlauer TFM, Yau MS, Hartley AE, Brumpton BM, Rasheed H, Kemp JP, Frysz M, Robinson J, Reppe S, Prijatelj V, Gautvik KM, Falk L, Maerz W, Gergei I, Peyser PA, Kavousi M, de Vries PS, Miller CL, Bos M, van der Laan SW, Malhotra R, Herrmann M, Scharnagl H, Kleber M, Dedoussis G, Zeggini E, Nethander M, Ohlsson C, Lorentzon M, Wareham N, Langenberg C, Holmes MV, Davey Smith G, Tobias JH. Lowering of Circulating Sclerostin May Increase Risk of Atherosclerosis and Its Risk Factors: Evidence From a Genome-Wide Association Meta-Analysis Followed by Mendelian Randomization. Arthritis Rheumatol 2023; 75:1781-1792. [PMID: 37096546 PMCID: PMC10586470 DOI: 10.1002/art.42538] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 03/22/2023] [Accepted: 04/18/2023] [Indexed: 04/26/2023]
Abstract
OBJECTIVE In this study, we aimed to establish the causal effects of lowering sclerostin, target of the antiosteoporosis drug romosozumab, on atherosclerosis and its risk factors. METHODS A genome-wide association study meta-analysis was performed of circulating sclerostin levels in 33,961 European individuals. Mendelian randomization (MR) was used to predict the causal effects of sclerostin lowering on 15 atherosclerosis-related diseases and risk factors. RESULTS We found that 18 conditionally independent variants were associated with circulating sclerostin. Of these, 1 cis signal in SOST and 3 trans signals in B4GALNT3, RIN3, and SERPINA1 regions showed directionally opposite signals for sclerostin levels and estimated bone mineral density. Variants with these 4 regions were selected as genetic instruments. MR using 5 correlated cis-SNPs suggested that lower sclerostin increased the risk of type 2 diabetes mellitus (DM) (odds ratio [OR] 1.32 [95% confidence interval (95% CI) 1.03-1.69]) and myocardial infarction (MI) (OR 1.35 [95% CI 1.01-1.79]); sclerostin lowering was also suggested to increase the extent of coronary artery calcification (CAC) (β = 0.24 [95% CI 0.02-0.45]). MR using both cis and trans instruments suggested that lower sclerostin increased hypertension risk (OR 1.09 [95% CI 1.04-1.15]), but otherwise had attenuated effects. CONCLUSION This study provides genetic evidence to suggest that lower levels of sclerostin may increase the risk of hypertension, type 2 DM, MI, and the extent of CAC. Taken together, these findings underscore the requirement for strategies to mitigate potential adverse effects of romosozumab treatment on atherosclerosis and its related risk factors.
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Affiliation(s)
- Jie Zheng
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, and Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the People's Republic of China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, and MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of BristolBristolUK
| | - Eleanor Wheeler
- MRC Epidemiology Unit, Institute of Metabolic ScienceUniversity of Cambridge School of Clinical MedicineCambridgeUK
| | - Maik Pietzner
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK, and Computational Medicine, Berlin Institute of Health at Charité–Universitätsmedizin BerlinBerlinGermany
| | - Till F. M. Andlauer
- Department of Neurology, Klinikum rechts der Isar, School of MedicineTechnical University of MunichMunichGermany
| | - Michelle S. Yau
- Marcus Institute for Aging Research, Hebrew SeniorLifeHarvard Medical SchoolBostonMassachusetts
| | | | - Ben Michael Brumpton
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, and HUNT Research Centre, Department of Public Health and Nursing, NTNUNorwegian University of Science and TechnologyLevangerNorway
| | - Humaira Rasheed
- MRC IEU, Bristol Medical School, University of Bristol, Bristol, UK, and HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway, and Division of Medicine and Laboratory Sciences, Faculty of MedicineUniversity of OsloOsloNorway
| | - John P. Kemp
- MRC IEU, Bristol Medical School, University of Bristol, Bristol, UK, and Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia, and The University of Queensland Diamantina InstituteThe University of QueenslandBrisbaneQueenslandAustralia
| | - Monika Frysz
- MRC IEU, Bristol Medical School, University of Bristol, and Musculoskeletal Research UnitUniversity of BristolBristolUK
| | - Jamie Robinson
- MRC IEU, Bristol Medical SchoolUniversity of BristolBristolUK
| | - Sjur Reppe
- Unger‐Vetlesen Institute, Lovisenberg Diaconal Hospital and Department of Plastic and Reconstructive Surgery, Oslo University Hospital and Department of Medical BiochemistryOslo University HospitalOsloNorway
| | - Vid Prijatelj
- Department of Internal MedicineErasmus MC University Medical CenterRotterdamThe Netherlands
| | | | - Louise Falk
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK, and Computational Medicine, Berlin Institute of Health at Charité–Universitätsmedizin BerlinBerlinGermany
| | - Winfried Maerz
- Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Austria, and SYNLAB Academy, SYNLAB Holding Deutschland GmbH and Vth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty MannheimUniversity of HeidelbergMannheimGermany
| | - Ingrid Gergei
- Vth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Mannheim, and Therapeutic Area Cardiovascular MedicineBoehringer Ingelheim International GmbHIngelheimGermany
| | - Patricia A. Peyser
- Department of Epidemiology, School of Public HealthUniversity of MichiganAnn Arbor
| | - Maryam Kavousi
- Department of Epidemiology, Erasmus MCUniversity Medical CenterRotterdamThe Netherlands
| | - Paul S. de Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public HealthThe University of Texas Health Science Center at Houston
| | - Clint L. Miller
- Center for Public Health Genomics, Department of Public Health SciencesUniversity of VirginiaCharlottesville
| | - Maxime Bos
- Department of Epidemiology, Erasmus MCUniversity Medical CenterRotterdamThe Netherlands
| | - Sander W. van der Laan
- Central Diagnostics Laboratory, Division of Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center UtrechtUtrecht UniversityUtrechtthe Netherlands
| | - Rajeev Malhotra
- Cardiology Division, Department of MedicineMassachusetts General HospitalBoston
| | - Markus Herrmann
- Clinical Institute of Medical and Chemical Laboratory DiagnosticsMedical University of GrazGrazAustria
| | - Hubert Scharnagl
- Clinical Institute of Medical and Chemical Laboratory DiagnosticsMedical University of GrazGrazAustria
| | - Marcus Kleber
- SYNLAB Academy, SYNLAB Holding Deutschland GmbHMannheimGermany
| | - George Dedoussis
- Department of Nutrition and Dietetics, School of Health Science and EducationHarokopio UniversityAthensGreece
| | - Eleftheria Zeggini
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, and Technical University of Munich (TUM) and Klinikum Rechts der IsarTUM School of MedicineMunichGermany
| | - Maria Nethander
- Sahlgrenska Osteoporosis Centre, Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, University of Gothenburg and Bioinformatics and Data Centre, Sahlgrenska AcademyUniversity of GothenburgGothenburgSweden
| | - Claes Ohlsson
- Sahlgrenska Osteoporosis Centre, Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of MedicineUniversity of GothenburgGothenburgSweden
| | - Mattias Lorentzon
- Sahlgrenska Osteoporosis Centre, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden, and Region Västra Götaland, Geriatric Medicine, Sahlgrenska University Hospital, Mölndal, Sweden, and Mary McKillop Institute for Health ResearchAustralian Catholic UniversityMelbourneVictoriaAustralia
| | - Nick Wareham
- MRC Epidemiology Unit, Institute of Metabolic ScienceUniversity of Cambridge School of Clinical MedicineCambridgeUK
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK, and Computational Medicine, Berlin Institute of Health at Charité–Universitätsmedizin BerlinBerlinGermany
| | - Michael V. Holmes
- MRC IEU, Bristol Medical School, University of Bristol, and Medical Research Council Population Health Research Unit, University of Oxford, and Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population HealthUniversity of Oxford, and National Institute for Health Research, Oxford Biomedical Research Centre, Oxford University HospitalOxfordUK
| | | | - Jonathan H. Tobias
- MRC IEU, Bristol Medical School, University of Bristol, and Musculoskeletal Research UnitUniversity of BristolBristolUK
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3
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Power GM, Tobias JH, Frayling TM, Tyrrell J, Hartley AE, Heron JE, Davey Smith G, Richardson TG. Age-specific effects of weight-based body size on fracture risk in later life: a lifecourse Mendelian randomisation study. Eur J Epidemiol 2023; 38:795-807. [PMID: 37133737 PMCID: PMC10276076 DOI: 10.1007/s10654-023-00986-6] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 03/02/2023] [Indexed: 05/04/2023]
Abstract
Musculoskeletal conditions, including fractures, can have severe and long-lasting consequences. Higher body mass index in adulthood is widely acknowledged to be protective for most fracture sites. However, sources of bias induced by confounding factors may have distorted previous findings. Employing a lifecourse Mendelian randomisation (MR) approach by using genetic instruments to separate effects at different life stages, this investigation aims to explore how prepubertal and adult body size independently influence fracture risk in later life.Using data from a large prospective cohort, univariable and multivariable MR were conducted to simultaneously estimate the effects of age-specific genetic proxies for body size (n = 453,169) on fracture risk (n = 416,795). A two-step MR framework was additionally applied to elucidate potential mediators. Univariable and multivariable MR indicated strong evidence that higher body size in childhood reduced fracture risk (OR, 95% CI: 0.89, 0.82 to 0.96, P = 0.005 and 0.76, 0.69 to 0.85, P = 1 × 10- 6, respectively). Conversely, higher body size in adulthood increased fracture risk (OR, 95% CI: 1.08, 1.01 to 1.16, P = 0.023 and 1.26, 1.14 to 1.38, P = 2 × 10- 6, respectively). Two-step MR analyses suggested that the effect of higher body size in childhood on reduced fracture risk was mediated by its influence on higher estimated bone mineral density (eBMD) in adulthood.This investigation provides novel evidence that higher body size in childhood reduces fracture risk in later life through its influence on increased eBMD. From a public health perspective, this relationship is complex since obesity in adulthood remains a major risk factor for co-morbidities. Results additionally indicate that higher body size in adulthood is a risk factor for fractures. Protective effect estimates previously observed are likely attributed to childhood effects.
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Affiliation(s)
- Grace Marion 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.
| | - Jonathan H Tobias
- 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
- Musculoskeletal Research Unit, Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Timothy M Frayling
- Genetics of Complex Traits, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Jessica Tyrrell
- Genetics of Complex Traits, College of Medicine and Health, University of Exeter, Exeter, UK
| | - April E Hartley
- 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
- Musculoskeletal Research Unit, Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jon E 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
| | - 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
- NIHR Bristol Biomedical Research Centre Bristol, University Hospitals Bristol and Weston NHS Foundation Trust, University of Bristol, Bristol, 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
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4
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Faber BG, Frysz M, Hartley AE, Ebsim R, Boer CG, Saunders FR, Gregory JS, Aspden RM, Harvey NC, Southam L, Giles W, Le Maitre CL, Wilkinson JM, van Meurs JBJ, Zeggini E, Cootes T, Lindner C, Kemp JP, Davey Smith G, Tobias JH. A Genome-Wide Association Study Meta-Analysis of Alpha Angle Suggests Cam-Type Morphology May Be a Specific Feature of Hip Osteoarthritis in Older Adults. Arthritis Rheumatol 2023; 75:900-909. [PMID: 36662418 PMCID: PMC10374163 DOI: 10.1002/art.42451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 11/08/2022] [Accepted: 01/17/2023] [Indexed: 01/21/2023]
Abstract
OBJECTIVE To examine the genetic architecture of cam morphology using alpha angle (AA) as a proxy measure and conduct an AA genome-wide association study (GWAS) followed by Mendelian randomization (MR) to evaluate its causal relationship with hip osteoarthritis (OA). METHODS Observational analyses examined associations between AA measurements derived from hip dual x-ray absorptiometry (DXA) scans from the UK Biobank study and radiographic hip OA outcomes and subsequent total hip replacement. Following these analyses, an AA GWAS meta-analysis was performed (N = 44,214) using AA measurements previously derived in the Rotterdam Study. Linkage disequilibrium score regression assessed the genetic correlation between AA and hip OA. Genetic associations considered significant (P < 5 × 10-8 ) were used as AA genetic instrument for 2-sample MR analysis. RESULTS DXA-derived AA showed expected associations between AA and radiographic hip OA (adjusted odds ratio [OR] 1.63 [95% confidence interval (95% CI) 1.58, 1.67]) and between AA and total hip replacement (adjusted hazard ratio 1.45 [95% CI 1.33, 1.59]) in the UK Biobank study cohort. The heritability of AA was 10%, and AA had a moderate genetic correlation with hip OA (rg = 0.26 [95% CI 0.10, 0.43]). Eight independent genetic signals were associated with AA. Two-sample MR provided weak evidence of causal effects of AA on hip OA risk (inverse variance weighted OR 1.84 [95% CI 1.14, 2.96], P = 0.01). In contrast, genetic predisposition for hip OA had stronger evidence of a causal effect on increased AA (inverse variance weighted β = 0.09 [95% CI 0.04, 0.13], P = 4.58 × 10-5 ). CONCLUSION Expected observational associations between AA and related clinical outcomes provided face validity for the DXA-derived AA measurements. Evidence of bidirectional associations between AA and hip OA, particularly for risk of hip OA on AA, suggests that hip shape modeling secondary to a genetic predisposition to hip OA contributes to the well-established relationship between hip OA and cam morphology in older adults.
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Affiliation(s)
- Benjamin G. Faber
- Musculoskeletal Research Unit, Translational Health Sciences, and Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolUK
| | - Monika Frysz
- Musculoskeletal Research Unit, Translational Health Sciences, and Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolUK
| | - April E. Hartley
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolUK
| | - Raja Ebsim
- Division of Informatics, Imaging and Data ScienceThe University of ManchesterUK
| | - Cindy G. Boer
- Department of Internal Medicine, Erasmus MCUniversity Medical CenterRotterdamThe Netherlands
| | - Fiona R. Saunders
- Centre for Arthritis and Musculoskeletal HealthUniversity of AberdeenUK
| | | | - Richard M. Aspden
- Centre for Arthritis and Musculoskeletal HealthUniversity of AberdeenUK
| | - Nicholas C. Harvey
- Medical Research Council Lifecourse Epidemiology Centre, University of Southampton, UK, and NIHR Southampton Biomedical Research CentreUniversity of Southampton and University Hospital Southampton NHS Foundation TrustUK
| | - Lorraine Southam
- Institute of Translational Genomics, Helmholtz Zentrum München–German Research Center for Environmental HealthNeuherbergGermany
| | - William Giles
- Department of Oncology and MetabolismThe University of SheffieldUK
| | | | | | - Joyce B. J. van Meurs
- Department of Internal Medicine and Department of Orthopaedics & Sports Medicine, Erasmus MCRotterdamThe Netherlands
| | - Eleftheria Zeggini
- Institute of Translational Genomics, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany, and TUM School of MedicineTechnical University of Munich and Klinikum Rechts der IsarGermany
| | - Timothy Cootes
- Division of Informatics, Imaging and Data ScienceThe University of ManchesterUK
| | - Claudia Lindner
- Division of Informatics, Imaging and Data ScienceThe University of ManchesterUK
| | - John P. Kemp
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, UK, and The University of Queensland Diamantina Institute and Institute for Molecular Bioscience, The University of QueenslandQueenslandAustralia
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolUK
| | - Jonathan H. Tobias
- Musculoskeletal Research Unit, Translational Health Sciences, and Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolUK
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5
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Mitchell RE, Hartley AE, Walker VM, Gkatzionis A, Yarmolinsky J, Bell JA, Chong AHW, Paternoster L, Tilling K, Smith GD. Strategies to investigate and mitigate collider bias in genetic and Mendelian randomisation studies of disease progression. PLoS Genet 2023; 19:e1010596. [PMID: 36821633 PMCID: PMC9949638 DOI: 10.1371/journal.pgen.1010596] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023] Open
Abstract
Genetic studies of disease progression can be used to identify factors that may influence survival or prognosis, which may differ from factors that influence on disease susceptibility. Studies of disease progression feed directly into therapeutics for disease, whereas studies of incidence inform prevention strategies. However, studies of disease progression are known to be affected by collider (also known as "index event") bias since the disease progression phenotype can only be observed for individuals who have the disease. This applies equally to observational and genetic studies, including genome-wide association studies and Mendelian randomisation (MR) analyses. In this paper, our aim is to review several statistical methods that can be used to detect and adjust for index event bias in studies of disease progression, and how they apply to genetic and MR studies using both individual- and summary-level data. Methods to detect the presence of index event bias include the use of negative controls, a comparison of associations between risk factors for incidence in individuals with and without the disease, and an inspection of Miami plots. Methods to adjust for the bias include inverse probability weighting (with individual-level data), or Slope-Hunter and Dudbridge et al.'s index event bias adjustment (when only summary-level data are available). We also outline two approaches for sensitivity analysis. We then illustrate how three methods to minimise bias can be used in practice with two applied examples. Our first example investigates the effects of blood lipid traits on mortality from coronary heart disease, while our second example investigates genetic associations with breast cancer mortality.
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Affiliation(s)
- Ruth E. Mitchell
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - April E. Hartley
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Venexia M. Walker
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
| | - Apostolos Gkatzionis
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - James Yarmolinsky
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Joshua A. Bell
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Amanda H. W. Chong
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Lavinia Paternoster
- 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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Hartley AE, Power GM, Sanderson E, Smith GD. A Guide for Understanding and Designing Mendelian Randomization Studies in the Musculoskeletal Field. JBMR Plus 2022; 6:e10675. [PMID: 36248277 PMCID: PMC9549705 DOI: 10.1002/jbm4.10675] [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] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 08/08/2022] [Indexed: 11/10/2022] Open
Abstract
Mendelian randomization (MR) is an increasingly popular component of an epidemiologist's toolkit, used to provide evidence of a causal effect of one trait (an exposure, eg, body mass index [BMI]) on an outcome trait or disease (eg, osteoarthritis). Identifying these effects is important for understanding disease etiology and potentially identifying targets for therapeutic intervention. MR uses genetic variants as instrumental variables for the exposure, which should not be influenced by the outcome or confounding variables, overcoming key limitations of traditional epidemiological analyses. For MR to generate a valid estimate of effect, key assumptions must be met. In recent years, there has been a rapid rise in MR methods that aim to test, or are robust to violations of, these assumptions. In this review, we provide an overview of MR for a non-expert audience, including an explanation of these key assumptions and how they are often tested, to aid a better reading and understanding of the MR literature. We highlight some of these new methods and how they can be useful for specific methodological challenges in the musculoskeletal field, including for conditions or traits that share underlying biological pathways, such as bone and joint disease. © 2022 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.
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Affiliation(s)
- April E Hartley
- MRC-Integrative Epidemiology Unit Population Health Sciences, Bristol Medical School Bristol UK
| | - Grace M Power
- MRC-Integrative Epidemiology Unit Population Health Sciences, Bristol Medical School Bristol UK
| | - Eleanor Sanderson
- MRC-Integrative Epidemiology Unit Population Health Sciences, Bristol Medical School Bristol UK
| | - George Davey Smith
- MRC-Integrative Epidemiology Unit Population Health Sciences, Bristol Medical School Bristol UK
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7
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Abstract
The evaluation of a radioimmunoassay for erythropoietin developed using recombinant material as immunogen and radiotracer is presented. A series of serum samples prepared and stored under varying conditions showed that immunoreactive erythropoietin levels were stable at room temperature for at least 10 days and at -20 degrees C for 5 months. The optimum time for separating sera from samples was between 6 and 24 h after venepuncture. Serum EPO values were significantly higher than those measured in heparin or potassium EDTA plasma.
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Affiliation(s)
- R G Kendall
- Department of Haematology, Leeds General Infirmary
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Abstract
Serial serum erythropoietin levels were measured in 10 consecutive patients undergoing allogeneic bone marrow transplantation. Observed erythropoietin levels are compared with those predicted from a large control population of anaemic patients not receiving chemotherapy. There was an initial acute rise in serum erythropoietin, peaking between days 1 and 4 after marrow transfusion, which was unrelated to changes in haemoglobin concentration. Patients maintained serum erythropoietin concentrations at around twice the predicted level for the first 2 weeks following transplantation, with a gradual fall into the expected range by wk 3. Erythropoietin levels did not change with episodes of bacterial infection or acute graft-versus-host disease. A patient with severe aplastic anaemia had initial successful engraftment with normalisation of erythropoietin levels, but showed a marked and amplified rise in erythropoietin 2 wk before falling peripheral blood counts indicated failure of the bone marrow graft.
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Affiliation(s)
- R J Grace
- Department of Haematology, General Infirmary, Leeds, U.K
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