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Souza-Teodoro LH, Davies NM, Warren HR, Andrade LHSG, Carvalho LA. DHEA and response to antidepressant treatment: A Mendelian Randomization analysis. J Psychiatr Res 2024; 173:151-156. [PMID: 38531145 DOI: 10.1016/j.jpsychires.2024.02.049] [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] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 02/02/2024] [Accepted: 02/21/2024] [Indexed: 03/28/2024]
Abstract
Treatment response is hard to predict and detailed mechanisms unknown. Lower levels of the dehydroepiandrosterone sulphate (DHEA(S)) - a precursor to testosterone and estrogen - have been associated to depression and to response to antidepressant treatment. Previous studies however may have been ridden by confounding and reverse causation. The aim of this study is to evaluate whether higher levels of DHEA(S) are causally linked to response to antidepressants using mendelian randomization (MR). We performed a Two-sample MR analysis using data the largest publicly available GWAS of DHEA(S) levels (n = 14,846) using eight common genetic variants associated to DHEA(S) (seven single nucleotide polymorphisms and one variant rs2497306) and the largest GWAS of antidepressant response (n = 5218) using various MR methods (IVW, MR Egger, Weighted mean, weighted mode, MR-PRESSO) and single SNP analysis. We further investigated for pleiotropy conducting a look up on PhenoScanner and GWAS Catalog. Results show no evidence for DHEA(S) gene risk score from any of MR methods, however, we found a significant association on individual variant analysis for rs11761538, rs17277546, and rs2497306. There was some evidence for heterogeneity and pleiotropy. This is the first paper to show some evidence for a causal association of genetically-predicted DHEA and improvement of depressive symptoms. The effect is not a simple linear effect, and we were unable to dissect whether the effect was direct effect of DHEA(S), mediated by DHEA(S) or on the pathway is not yet clear. Further studies using more refined instrumental variables will help clarify this association.
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Affiliation(s)
- L H Souza-Teodoro
- William Harvey Research Institute, Charterhouse Square, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, EC1M 6BQ, UK; Núcleo de Epidemiologia Psiquiatrica, Instituto de Psiquiatria, Hospital das Clínicas da Faculdade de Medicina da Universidade de Sao Paulo, Brazil
| | - N M Davies
- Division of Psychiatry, University College London, UK; K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Norway; Department of Statistical Sciences, University College London, London, UK
| | - H R Warren
- William Harvey Research Institute, Charterhouse Square, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, EC1M 6BQ, UK; NIHR Cardiovascular Biomedical Research Centre, Barts and the London Faculty of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - L H S G Andrade
- Núcleo de Epidemiologia Psiquiatrica, Instituto de Psiquiatria, Hospital das Clínicas da Faculdade de Medicina da Universidade de Sao Paulo, Brazil
| | - L A Carvalho
- William Harvey Research Institute, Charterhouse Square, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, EC1M 6BQ, UK.
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Askelund AD, Wootton RE, Torvik FA, Lawn RB, Ask H, Corfield EC, Magnus MC, Reichborn-Kjennerud T, Magnus PM, Andreassen OA, Stoltenberg C, Davey Smith G, Davies NM, Havdahl A, Hannigan LJ. Assessing causal links between age at menarche and adolescent mental health: a Mendelian randomisation study. BMC Med 2024; 22:155. [PMID: 38609914 PMCID: PMC11015655 DOI: 10.1186/s12916-024-03361-8] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 03/18/2024] [Indexed: 04/14/2024] Open
Abstract
BACKGROUND The timing of puberty may have an important impact on adolescent mental health. In particular, earlier age at menarche has been associated with elevated rates of depression in adolescents. Previous research suggests that this relationship may be causal, but replication and an investigation of whether this effect extends to other mental health domains is warranted. METHODS In this Registered Report, we triangulated evidence from different causal inference methods using a new wave of data (N = 13,398) from the Norwegian Mother, Father, and Child Cohort Study. We combined multiple regression, one- and two-sample Mendelian randomisation (MR), and negative control analyses (using pre-pubertal symptoms as outcomes) to assess the causal links between age at menarche and different domains of adolescent mental health. RESULTS Our results supported the hypothesis that earlier age at menarche is associated with elevated depressive symptoms in early adolescence based on multiple regression (β = - 0.11, 95% CI [- 0.12, - 0.09], pone-tailed < 0.01). One-sample MR analyses suggested that this relationship may be causal (β = - 0.07, 95% CI [- 0.13, 0.00], pone-tailed = 0.03), but the effect was small, corresponding to just a 0.06 standard deviation increase in depressive symptoms with each earlier year of menarche. There was also some evidence of a causal relationship with depression diagnoses during adolescence based on one-sample MR (OR = 0.74, 95% CI [0.54, 1.01], pone-tailed = 0.03), corresponding to a 29% increase in the odds of receiving a depression diagnosis with each earlier year of menarche. Negative control and two-sample MR sensitivity analyses were broadly consistent with this pattern of results. Multivariable MR analyses accounting for the genetic overlap between age at menarche and childhood body size provided some evidence of confounding. Meanwhile, we found little consistent evidence of effects on other domains of mental health after accounting for co-occurring depression and other confounding. CONCLUSIONS We found evidence that age at menarche affected diagnoses of adolescent depression, but not other domains of mental health. Our findings suggest that earlier age at menarche is linked to problems in specific domains rather than adolescent mental health in general.
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Affiliation(s)
- Adrian Dahl Askelund
- Department of Psychology, University of Oslo, Oslo, Norway.
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway.
- PsychGen Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway.
| | - Robyn E Wootton
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
- PsychGen Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
- School of Psychological Science, University of Bristol, Bristol, UK
| | - Fartein A Torvik
- Department of Psychology, University of Oslo, Oslo, Norway
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Rebecca B Lawn
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, USA
| | - Helga Ask
- PsychGen 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
| | - Elizabeth C Corfield
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
- PsychGen Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Maria C Magnus
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Ted Reichborn-Kjennerud
- PsychGen Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Per M Magnus
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Ole A Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Camilla Stoltenberg
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway
- NORCE Norwegian Research Centre, Bergen, Norway
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
| | - Neil M Davies
- Division of Psychiatry, University College London, London, UK
- Department of Statistical Sciences, University College London, London, UK
- KG Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
| | - Alexandra Havdahl
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
- PsychGen Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
- Promenta Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Laurie J Hannigan
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway.
- PsychGen Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway.
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK.
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3
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Arellano Spano M, Morris TT, Davies NM, Hughes A. Genetic associations of risk behaviours and educational achievement. Commun Biol 2024; 7:435. [PMID: 38600303 PMCID: PMC11006670 DOI: 10.1038/s42003-024-06091-y] [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: 12/04/2023] [Accepted: 03/22/2024] [Indexed: 04/12/2024] Open
Abstract
Risk behaviours are common in adolescent and persist into adulthood, people who engage in more risk behaviours are more likely to have lower educational attainment. We applied genetic causal inference methods to explore the causal relationship between adolescent risk behaviours and educational achievement. Risk behaviours were phenotypically associated with educational achievement at age 16 after adjusting for confounders (-0.11, 95%CI: -0.11, -0.09). Genomic-based restricted maximum likelihood (GREML) results indicated that both traits were heritable and have a shared genetic architecture (Riskh 2 = 0.18, 95% CI: -0.11,0.47; educationh 2 = 0.60, 95%CI: 0.50,0.70). Consistent with the phenotypic results, genetic variation associated with risk behaviour was negatively associated with education (r g = -0.51, 95%CI: -1.04,0.02). Lastly, the bidirectional MR results indicate that educational achievement or a closely related trait is likely to affect risk behaviours PGI (β=-1.04, 95% CI: -1.41, -0.67), but we found little evidence that the genetic variation associated with risk behaviours affected educational achievement (β=0.00, 95% CI: -0.24,0.24). The results suggest engagement in risk behaviour may be partly driven by educational achievement or a closely related trait.
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Affiliation(s)
- Michelle Arellano Spano
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, BS8 2BN, United Kingdom.
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, United Kingdom.
| | - Tim T Morris
- Centre for Longitudinal Studies, Social Research Institute, University College London, London, United Kingdom
| | - Neil M Davies
- Division of Psychiatry, University College London, Maple House, 149 Tottenham Court Rd, London, W1T 7NF, United Kingdom
- Department of Statistical Sciences, University College London, London, WC1E 6BT, United Kingdom
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, 7491, Trondheim, Norway
| | - Amanda Hughes
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, BS8 2BN, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, United Kingdom
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4
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Anderson EL, Davies NM, Korologou-Linden R, Kivimäki M. Dementia prevention: the Mendelian randomisation perspective. J Neurol Neurosurg Psychiatry 2024; 95:384-390. [PMID: 37967935 DOI: 10.1136/jnnp-2023-332293] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 10/25/2023] [Indexed: 11/17/2023]
Abstract
Understanding the causes of Alzheimer's disease and related dementias remains a challenge. Observational studies investigating dementia risk factors are limited by the pervasive issues of confounding, reverse causation and selection biases. Conducting randomised controlled trials for dementia prevention is often impractical due to the long prodromal phase and the inability to randomise many potential risk factors. In this essay, we introduce Mendelian randomisation as an alternative approach to examine factors that may prevent or delay Alzheimer's disease. Mendelian randomisation is a causal inference method that has successfully identified risk factors and treatments in various other fields. However, applying this method to dementia risk factors has yielded unexpected findings. Here, we consider five potential explanations and provide recommendations to enhance causal inference from Mendelian randomisation studies on dementia. By employing these strategies, we can better understand factors affecting dementia risk.
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Affiliation(s)
- Emma Louise Anderson
- Mental Health of Older People, Division of Psychiatry, University College London, London, UK
| | - Neil M Davies
- Epidemiology & Applied Clinical Research, Division of Psychiatry, University College London, London, UK
- Department of Statistical Sciences, University College London, London, UK
| | | | - Mika Kivimäki
- Mental Health of Older People, Division of Psychiatry, University College London, London, UK
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5
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Bann D, Courtin E, Davies NM, Wright L. Dialling back 'impact' claims: researchers should not be compelled to make policy claims based on single studies. Int J Epidemiol 2024; 53:dyad181. [PMID: 38124510 DOI: 10.1093/ije/dyad181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2023] Open
Affiliation(s)
- David Bann
- Centre for Longitudinal Studies, Social Research Institute, University College London, London, UK
| | - Emilie Courtin
- Department of Health Policy, London School of Economics, London, UK
| | - Neil M Davies
- Division of Psychiatry and Department of Statistical Science, University College London, London, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
| | - Liam Wright
- Centre for Longitudinal Studies, Social Research Institute, University College London, London, UK
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6
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Bradfield JP, Kember RL, Ulrich A, Balkiyarova Z, Alyass A, Aris IM, Bell JA, Broadaway KA, Chen Z, Chai JF, Davies NM, Fernandez-Orth D, Bustamante M, Fore R, Ganguli A, Heiskala A, Hottenga JJ, Íñiguez C, Kobes S, Leinonen J, Lowry E, Lyytikainen LP, Mahajan A, Pitkänen N, Schnurr TM, Have CT, Strachan DP, Thiering E, Vogelezang S, Wade KH, Wang CA, Wong A, Holm LA, Chesi A, Choong C, Cruz M, Elliott P, Franks S, Frithioff-Bøjsøe C, Gauderman WJ, Glessner JT, Gilsanz V, Griesman K, Hanson RL, Kaakinen M, Kalkwarf H, Kelly A, Kindler J, Kähönen M, Lanca C, Lappe J, Lee NR, McCormack S, Mentch FD, Mitchell JA, Mononen N, Niinikoski H, Oken E, Pahkala K, Sim X, Teo YY, Baier LJ, van Beijsterveldt T, Adair LS, Boomsma DI, de Geus E, Guxens M, Eriksson JG, Felix JF, Gilliland FD, Biobank PM, Hansen T, Hardy R, Hivert MF, Holm JC, Jaddoe VWV, Järvelin MR, Lehtimäki T, Mackey DA, Meyre D, Mohlke KL, Mykkänen J, Oberfield S, Pennell CE, Perry JRB, Raitakari O, Rivadeneira F, Saw SM, Sebert S, Shepherd JA, Standl M, Sørensen TIA, Timpson NJ, Torrent M, Willemsen G, Hypponen E, Power C, McCarthy MI, Freathy RM, Widén E, Hakonarson H, Prokopenko I, Voight BF, Zemel BS, Grant SFA, Cousminer DL. Trans-ancestral genome-wide association study of longitudinal pubertal height growth and shared heritability with adult health outcomes. Genome Biol 2024; 25:22. [PMID: 38229171 PMCID: PMC10790528 DOI: 10.1186/s13059-023-03136-z] [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: 02/19/2023] [Accepted: 11/30/2023] [Indexed: 01/18/2024] Open
Abstract
BACKGROUND Pubertal growth patterns correlate with future health outcomes. However, the genetic mechanisms mediating growth trajectories remain largely unknown. Here, we modeled longitudinal height growth with Super-Imposition by Translation And Rotation (SITAR) growth curve analysis on ~ 56,000 trans-ancestry samples with repeated height measurements from age 5 years to adulthood. We performed genetic analysis on six phenotypes representing the magnitude, timing, and intensity of the pubertal growth spurt. To investigate the lifelong impact of genetic variants associated with pubertal growth trajectories, we performed genetic correlation analyses and phenome-wide association studies in the Penn Medicine BioBank and the UK Biobank. RESULTS Large-scale growth modeling enables an unprecedented view of adolescent growth across contemporary and 20th-century pediatric cohorts. We identify 26 genome-wide significant loci and leverage trans-ancestry data to perform fine-mapping. Our data reveals genetic relationships between pediatric height growth and health across the life course, with different growth trajectories correlated with different outcomes. For instance, a faster tempo of pubertal growth correlates with higher bone mineral density, HOMA-IR, fasting insulin, type 2 diabetes, and lung cancer, whereas being taller at early puberty, taller across puberty, and having quicker pubertal growth were associated with higher risk for atrial fibrillation. CONCLUSION We report novel genetic associations with the tempo of pubertal growth and find that genetic determinants of growth are correlated with reproductive, glycemic, respiratory, and cardiac traits in adulthood. These results aid in identifying specific growth trajectories impacting lifelong health and show that there may not be a single "optimal" pubertal growth pattern.
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Affiliation(s)
- Jonathan P Bradfield
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Rachel L Kember
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Anna Ulrich
- Department of Clinical & Experimental Medicine, University of Surrey, Guildford, UK
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Zhanna Balkiyarova
- Department of Clinical & Experimental Medicine, University of Surrey, Guildford, UK
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- People-Centred Artificial Intelligence Institute, University of Surrey, Guildford, UK
| | - Akram Alyass
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
| | - Izzuddin M Aris
- Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, 02215, USA
| | - Joshua A Bell
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
| | - K Alaine Broadaway
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Zhanghua Chen
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, 90032, USA
| | - Jin-Fang Chai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Neil M Davies
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | | | | | - Ruby Fore
- Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, 02215, USA
| | - Amitavo Ganguli
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Anni Heiskala
- Center for Life Course Health Research, University of Oulu, Oulu, Finland
| | - Jouke-Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Carmen Íñiguez
- Department of Statistics and Computational Research, Universitat de València, Valencia, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Epidemiology and Environmental Health Joint Research Unit, FISABIO-Universitat Jaume I-Universitat de València, Valencia, Spain
| | - Sayuko Kobes
- Phoenix Epidemiology and Clinical Research Center, NIDDK, NIH, Bethesda, USA
| | - Jaakko Leinonen
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Estelle Lowry
- Center for Life Course Health Research, University of Oulu, Oulu, Finland
| | - Leo-Pekka Lyytikainen
- Department of Clinical Physiology, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, 33014, Tampere, Finland
- Department of Clinical Physiology, Tampere University Hospital, 33521, Tampere, Finland
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
| | - Niina Pitkänen
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
| | - Theresia M Schnurr
- Faculty of Health and Medical Sciences, Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Christian Theil Have
- Faculty of Health and Medical Sciences, Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - David P Strachan
- Population Health Research Institute, St George's, University of London, Cranmer Terrace, London, SW17 0RE, UK
| | - Elisabeth Thiering
- Institute of Epidemiology, Helmholtz Zentrum München- German Research Center for Environmental Health, Neuherberg, Germany
- Division of Metabolic and Nutritional Medicine, Dr. Von Hauner Children's Hospital, University of Munich Medical Center, Munich, Germany
| | - Suzanne Vogelezang
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Kaitlin H Wade
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
| | - Carol A Wang
- School of Medicine and Public Health, Faculty of Medicine and Health, University of Newcastle, Callaghan, NSW, 2308, Australia
- Hunter Medical Research Institute, Newcastle, NSW, 2305, Australia
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, London, UK
| | - Louise Aas Holm
- Faculty of Health and Medical Sciences, Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
- Department of Pediatrics, The Children's Obesity Clinic, Copenhagen University Hospital Holbæk, Holbæk, Denmark
| | - Alessandra Chesi
- Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Catherine Choong
- Faculty of Health and Medical Sciences, University of Western Australia, Perth, WA, Australia
| | - Miguel Cruz
- Unidad de Investigación Médica en Bioquímica, Hospital de Especialidades, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Paul Elliott
- MRC Centre for Environment and Health, School of Public Health, Faculty of Medicine, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK
| | - Steve Franks
- Institute of Reproductive & Developmental Biology, Imperial College London, London, UK
| | - Christine Frithioff-Bøjsøe
- Faculty of Health and Medical Sciences, Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
- Department of Pediatrics, The Children's Obesity Clinic, Copenhagen University Hospital Holbæk, Holbæk, Denmark
| | - W James Gauderman
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, 90032, USA
| | - Joseph T Glessner
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Vicente Gilsanz
- Center for Endocrinology, Diabetes & Metabolism, Children's Hospital Los Angeles, Los Angeles, CA, USA
| | | | - Robert L Hanson
- Phoenix Epidemiology and Clinical Research Center, NIDDK, NIH, Bethesda, USA
| | - Marika Kaakinen
- Department of Clinical & Experimental Medicine, University of Surrey, Guildford, UK
- Institute of Reproductive & Developmental Biology, Imperial College London, London, UK
| | - Heidi Kalkwarf
- Department of Pediatrics, Cincinnati Children's Hospital, University of Cincinnati, Cincinnati, OH, USA
| | - Andrea Kelly
- Department of Pediatrics, The University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Division of Endocrinology & Diabetes, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Joseph Kindler
- College of Family and Consumer Sciences, University of Georgia, Athens, GA, USA
| | - Mika Kähönen
- Department of Clinical Physiology, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, 33014, Tampere, Finland
- Department of Clinical Physiology, Tampere University Hospital, 33521, Tampere, Finland
| | - Carla Lanca
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Joan Lappe
- Department of Medicine and College of Nursing, Creighton University School of Medicine, Omaha, NB, USA
| | - Nanette R Lee
- USC-Office of Population Studies Foundation, Inc, University of San Carlos, Cebu, Philippines
| | - Shana McCormack
- Department of Pediatrics, The University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Division of Endocrinology & Diabetes, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Frank D Mentch
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Jonathan A Mitchell
- Department of Pediatrics, The University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Division of Gastroenterology, Hepatology and Nutrition, The Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Nina Mononen
- Department of Clinical Chemistry, Faculty of Medicine and Health Technology, Finnish Cardiovascular Research Center - Tampere, Tampere University, 33014, Tampere, Finland
- Department of Clinical Chemistry, Fimlab Laboratories, 33520, Tampere, Finland
| | - Harri Niinikoski
- Department of Pediatrics and Adolescent Medicine, Turku University Hospital and University of Turku, Turku, Finland
- Department of Physiology, University of Turku, Turku, Finland
| | - Emily Oken
- Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, 02215, USA
- Department of Nutrition, Harvard T.H Chan School of Public Health, Boston, MA, 02115, USA
| | - Katja Pahkala
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
- Paavo Nurmi Centre, Unit for Health and Physical Activity, University of Turku, Turku, Finland
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Yik-Ying Teo
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Leslie J Baier
- Phoenix Epidemiology and Clinical Research Center, NIDDK, NIH, Bethesda, USA
| | - Toos van Beijsterveldt
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Linda S Adair
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, the Netherlands
| | - Eco de Geus
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Mònica Guxens
- ISGlobal, Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Johan G Eriksson
- Institute of Clinical Medicine Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland
- Folkhälsan Research Center, Helsinki, Finland
- Department of Obstetrics & Gynecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Janine F Felix
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Frank D Gilliland
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, 90032, USA
| | | | - Torben Hansen
- Faculty of Health and Medical Sciences, Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Rebecca Hardy
- Cohort and Longitudinal Studies Enhancement Resources (CLOSER), UCL Institute of Education, London, UK
| | - Marie-France Hivert
- Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, 02215, USA
| | - Jens-Christian Holm
- Faculty of Health and Medical Sciences, Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
- Department of Pediatrics, The Children's Obesity Clinic, Copenhagen University Hospital Holbæk, Holbæk, Denmark
- The Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Vincent W V Jaddoe
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Marjo-Riitta Järvelin
- Center for Life Course Health Research, University of Oulu, Oulu, Finland
- Department of Epidemiology and Biostatistics, School of Public Health, MRC-PHE Centre for Environment and Health, Imperial College London, London, W2 1PG, UK
- Unit of Primary Health Care, Oulu University Hospital, OYS, Kajaanintie 50, 90220, Oulu, Finland
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Faculty of Medicine and Health Technology, Finnish Cardiovascular Research Center - Tampere, Tampere University, 33014, Tampere, Finland
- Department of Clinical Chemistry, Fimlab Laboratories, 33520, Tampere, Finland
| | - David A Mackey
- Lions Eye Institute, Centre for Ophthalmology and Visual Science, Centre for Eye Research Australia, University of Western Australia, Perth, WA, Australia
| | - David Meyre
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Canada
- Inserm UMR_S1256 Nutrition-Genetics-Environmental Risk Exposure, University of Lorraine, Nancy, France
- Department of Biochemistry-Molecular Biology-Nutrition, University Hospital Centre of Nancy, Nancy, France
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Juha Mykkänen
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
| | - Sharon Oberfield
- Division of Pediatric Endocrinology, Columbia University Medical Center, New York, NY, USA
| | - Craig E Pennell
- School of Medicine and Public Health, Faculty of Medicine and Health, University of Newcastle, Callaghan, NSW, 2308, Australia
- Hunter Medical Research Institute, Newcastle, NSW, 2305, Australia
- Department of Maternity and Gynaecology, John Hunter Hospital, Newcastle, NSW, 2305, Australia
| | - John R B Perry
- Metabolic Research Laboratory, School of Clinical Medicine, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, CB2 0QQ, UK
- MRC Epidemiology Unit, School of Clinical Medicine, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Olli Raitakari
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
| | - Fernando Rivadeneira
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Seang-Mei Saw
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Sylvain Sebert
- Center for Life Course Health Research, University of Oulu, Oulu, Finland
- Department of Epidemiology and Biostatistics, School of Public Health, MRC-PHE Centre for Environment and Health, Imperial College London, London, W2 1PG, UK
| | - John A Shepherd
- Department of Epidemiology and Population Science, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Marie Standl
- Institute of Epidemiology, Helmholtz Zentrum München- German Research Center for Environmental Health, Neuherberg, Germany
| | - Thorkild I A Sørensen
- Faculty of Health and Medical Sciences, Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
- Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
| | - Maties Torrent
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Fundació Institut d'Investigació Sanitària Illes Balears - IdISBa, Palma, Spain
| | - Gonneke Willemsen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Elina Hypponen
- UCL Great Ormond Street Institute of Child Health, London, UK
- Australian Centre for Precision Health, Unit of Clinical and Health Sciences, University of South Australia, Adelaide, Australia
- South Australian Health and Medical Research Institute, Adelaide, Australia
| | - Chris Power
- UCL Great Ormond Street Institute of Child Health, London, UK
| | - Mark I McCarthy
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
- Current Address: Genentech, 1 DNA Way, San Francisco, CA, 94080, USA
| | - Rachel M Freathy
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, EX2 5DW, UK
| | - Elisabeth Widén
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Hakon Hakonarson
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Department of Pediatrics, The University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Inga Prokopenko
- Department of Clinical & Experimental Medicine, University of Surrey, Guildford, UK
- People-Centred Artificial Intelligence Institute, University of Surrey, Guildford, UK
- UMR 8199 - EGID, Institut Pasteur de Lille, CNRS, University of Lille, 59000, Lille, France
| | - Benjamin F Voight
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Institute of Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Babette S Zemel
- Department of Pediatrics, The University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Division of Gastroenterology, Hepatology and Nutrition, The Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Struan F A Grant
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
- Department of Pediatrics, The University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA.
- Division of Endocrinology & Diabetes, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
| | - Diana L Cousminer
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
- Currently Employed By GlaxoSmithKline, 1250 S Collegeville Rd, Collegeville, PA, 19426, USA.
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Madley-Dowd P, Rast J, Ahlqvist VH, Zhong C, Martin FZ, Davies NM, Lyall K, Newschaffer C, Tomson T, Magnusson C, Rai D, Lee BK, Forbes H. Trends and patterns of antiseizure medication prescribing during pregnancy between 1995 and 2018 in the United Kingdom: A cohort study. BJOG 2024; 131:15-25. [PMID: 37340193 PMCID: PMC10730765 DOI: 10.1111/1471-0528.17573] [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: 03/03/2023] [Revised: 05/19/2023] [Accepted: 06/04/2023] [Indexed: 06/22/2023]
Abstract
OBJECTIVE To examine antiseizure medication (ASM) prescription during pregnancy. DESIGN Population-based drug utilisation study. SETTING UK primary and secondary care data, 1995-2018, from the Clinical Practice Research Datalink GOLD version. POPULATION OR SAMPLE 752 112 completed pregnancies among women registered for a minimum of 12 months with an 'up to standard' general practice prior to the estimated start of pregnancy and for the duration of their pregnancy. METHODS We described ASM prescription across the study period, overall and by ASM indication, examined patterns of prescription during pregnancy including continuous prescription and discontinuation, and used logistic regression to investigate factors associated with those ASM prescription patterns. MAIN OUTCOME MEASURES Prescription of ASMs during pregnancy and discontinuation of ASMs before and during pregnancy. RESULTS ASM prescription during pregnancy increased from 0.6% of pregnancies in 1995 to 1.6% in 2018, driven largely by an increase in women with indications other than epilepsy. Epilepsy was an indication for 62.5% of pregnancies with an ASM prescription and non-epilepsy indications were present for 66.6%. Continuous prescription of ASMs during pregnancy was more common in women with epilepsy (64.3%) than in women with other indications (25.3%). Switching ASMs was infrequent (0.8% of ASM users). Factors associated with discontinuation included age ≥35, higher social deprivation, more frequent contact with the GP and being prescribed antidepressants or antipsychotics. CONCLUSIONS ASM prescription during pregnancy increased between 1995 and 2018 in the UK. Patterns of prescription around the pregnancy period vary by indication and are associated with several maternal characteristics.
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Affiliation(s)
- Paul Madley-Dowd
- - Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- - Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, BS8 2BN, United Kingdom
| | - Jessica Rast
- - A.J. Drexel Autism Institute, Drexel University, Philadelphia, PA, USA
- - Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, Philadelphia, PA, USA
| | - Viktor H. Ahlqvist
- - Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, BS8 2BN, United Kingdom
- - Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden
| | - Caichen Zhong
- - A.J. Drexel Autism Institute, Drexel University, Philadelphia, PA, USA
| | - Florence Z. Martin
- - Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- - Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, BS8 2BN, United Kingdom
| | - Neil M. Davies
- - Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, BS8 2BN, United Kingdom
- - K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, 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
| | - Kristen Lyall
- - Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, Philadelphia, PA, USA
| | - Craig Newschaffer
- - College of Health and Human Development, The Pennsylvania State University, USA
| | - Torbjörn Tomson
- - Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Cecilia Magnusson
- - Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden
- - Centre for Epidemiology and Community Medicine, Region Stockholm, Stockholm, Sweden
| | - Dheeraj Rai
- - Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- - Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, BS8 2BN, United Kingdom
- - NIHR Biomedical Research Centre, University of Bristol, Bristol, United Kingdom
- - Avon and Wiltshire Partnership NHS Mental Health Trust, Bristol, United Kingdom
| | - Brian K. Lee
- - A.J. Drexel Autism Institute, Drexel University, Philadelphia, PA, USA
- - Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, Philadelphia, PA, USA
- - Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden
| | - Harriet Forbes
- - Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- - Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
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8
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Davies NM, Dickson M, Davey Smith G, Windmeijer F, van den Berg GJ. The causal effects of education on adult health, mortality and income: evidence from Mendelian randomization and the raising of the school leaving age. Int J Epidemiol 2023; 52:1878-1886. [PMID: 37463867 PMCID: PMC10749779 DOI: 10.1093/ije/dyad104] [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: 09/05/2022] [Accepted: 07/04/2023] [Indexed: 07/20/2023] Open
Abstract
BACKGROUND On average, educated people are healthier, wealthier and have higher life expectancy than those with less education. Numerous studies have attempted to determine whether education causes differences in later health outcomes or whether another factor ultimately causes differences in education and subsequent outcomes. Previous studies have used a range of natural experiments to provide causal evidence. Here we compare two natural experiments: a policy reform, raising the school leaving age in the UK in 1972; and Mendelian randomization. METHODS We used data from 334 974 participants of the UK Biobank, sampled between 2006 and 2010. We estimated the effect of an additional year of education on 25 outcomes, including mortality, measures of morbidity and health, ageing and income, using multivariable adjustment, the policy reform and Mendelian randomization. We used a range of sensitivity analyses and specification tests to assess the plausibility of each method's assumptions. RESULTS The three different estimates of the effects of educational attainment were largely consistent in direction for diabetes, stroke and heart attack, mortality, smoking, income, grip strength, height, body mass index (BMI), intelligence, alcohol consumption and sedentary behaviour. However, there was evidence that education reduced rates of moderate exercise and increased alcohol consumption. Our sensitivity analyses suggest that confounding by genotypic or phenotypic confounders or specific forms of pleiotropy are unlikely to explain our results. CONCLUSIONS Previous studies have suggested that the differences in outcomes associated with education may be due to confounding. However, the two independent sources of exogenous variation we exploit largely imply consistent causal effects of education on outcomes later in life.
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Affiliation(s)
- Neil M Davies
- Division of Psychiatry, University College London, London, UK
- Department of Statistical Sciences, University College London, London, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Tronheim, Norway
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Matt Dickson
- Institute for Policy Research, University of Bath, Bath, UK
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, University of Bristol, Bristol, UK
| | - Frank Windmeijer
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Department of Statistics and Nuffield College, University of Oxford, Oxford, UK
| | - Gerard J van den Berg
- Department of Economics, University of Groningen, Groningen, The Netherlands
- Department of Epidemiology, University Medical Center Groningen, Groningen, The Netherlands
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Korologou-Linden R, Schuurmans IK, Cecil CAM, White T, Banaschewski T, Bokde ALW, Desrivières S, Grigis A, Garavan H, Gowland P, Heinz A, Brühl R, Martinot JL, Martinot MLP, Artiges E, Nees F, Orfanos DP, Paus T, Poustka L, Holz N, Fröhner JH, Smolka M, Walter H, Winterer J, Whelan R, Schumann G, Howe LD, Ben-Shlomo Y, Davies NM, Anderson EL. The bidirectional effects between cognitive ability and brain morphology: A life course Mendelian randomization analysis. medRxiv 2023:2023.11.17.23297145. [PMID: 38014064 PMCID: PMC10680890 DOI: 10.1101/2023.11.17.23297145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Introduction Little is understood about the dynamic interplay between brain morphology and cognitive ability across the life course. Additionally, most existing research has focused on global morphology measures such as estimated total intracranial volume, mean thickness, and total surface area. Methods Mendelian randomization was used to estimate the bidirectional effects between cognitive ability, global and regional measures of cortical thickness and surface area, estimated total intracranial volume, total white matter, and the volume of subcortical structures (N=37,864). Analyses were stratified for developmental periods (childhood, early adulthood, mid-to-late adulthood; age range: 8-81 years). Results The earliest effects were observed in childhood and early adulthood in the frontoparietal lobes. A bidirectional relationship was identified between higher cognitive ability, larger estimated total intracranial volume (childhood, mid-to-late adulthood) and total surface area (all life stages). A thicker posterior cingulate cortex and a larger surface area in the caudal middle frontal cortex and temporal pole were associated with greater cognitive ability. Contrary, a thicker temporal pole was associated with lower cognitive ability. Discussion Stable effects of cognitive ability on brain morphology across the life course suggests that childhood is potentially an important window for intervention.
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10
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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 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] [What about the content of this article? (0)] [Affiliation(s)] [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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11
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Lee J, Jukarainen S, Karvanen A, Dixon P, Davies NM, Smith GD, Natarajan P, Ganna A. Quantifying the causal impact of biological risk factors on healthcare costs. Nat Commun 2023; 14:5672. [PMID: 37704630 PMCID: PMC10499912 DOI: 10.1038/s41467-023-41394-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [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: 11/11/2022] [Accepted: 08/29/2023] [Indexed: 09/15/2023] Open
Abstract
Understanding the causal impact that clinical risk factors have on healthcare-related costs is critical to evaluate healthcare interventions. Here, we used a genetically-informed design, Mendelian Randomization (MR), to infer the causal impact of 15 risk factors on annual total healthcare costs. We calculated healthcare costs for 373,160 participants from the FinnGen Study and replicated our results in 323,774 individuals from the United Kingdom and Netherlands. Robust causal effects were observed for waist circumference (WC), adult body mass index, and systolic blood pressure, in which a standard deviation increase corresponded to 22.78% [95% CI: 18.75-26.95], 13.64% [10.26-17.12], and 13.08% [8.84-17.48] increased healthcare costs, respectively. A lack of causal effects was observed for certain clinically relevant biomarkers, such as albumin, C-reactive protein, and vitamin D. Our results indicated that increased WC is a major contributor to annual total healthcare costs and more attention may be given to WC screening, surveillance, and mitigation.
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Affiliation(s)
- Jiwoo Lee
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Sakari Jukarainen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Antti Karvanen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Padraig Dixon
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Neil M Davies
- Division of Psychiatry, University College London, Maple House, 149 Tottenham Court Rd, London, W1T 7NF, 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, Norwegian University of Science and Technology, Trondheim, Norway
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
| | - Pradeep Natarajan
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Andrea Ganna
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
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12
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Heuvelman H, Davies NM, Ben-Shlomo Y, Emond A, Evans J, Gunnell D, Liebling R, Morris R, Payne R, Storey C, Viner M, Rai D. Antidepressants in pregnancy: applying causal epidemiological methods to understand service-use outcomes in women and long-term neurodevelopmental outcomes in exposed children. Health Technol Assess 2023; 27:1-83. [PMID: 37842916 DOI: 10.3310/aqtf4490] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2023] Open
Abstract
Background Antidepressants are commonly prescribed during pregnancy, despite a lack of evidence from randomised trials on the benefits or risks. Some studies have reported associations of antidepressants during pregnancy with adverse offspring neurodevelopment, but whether or not such associations are causal is unclear. Objectives To study the associations of antidepressants for depression in pregnancy with outcomes using multiple methods to strengthen causal inference. Design This was an observational cohort design using multiple methods to strengthen causal inference, including multivariable regression, propensity score matching, instrumental variable analysis, negative control exposures, comparison across indications and exposure discordant pregnancies analysis. Setting This took place in UK general practice. Participants Participants were pregnant women with depression. Interventions The interventions were initiation of antidepressants in pregnancy compared with no initiation, and continuation of antidepressants in pregnancy compared with discontinuation. Main outcome measures The maternal outcome measures were the use of primary care and secondary mental health services during pregnancy, and during four 6-month follow-up periods up to 24 months after pregnancy, and antidepressant prescription status 24 months following pregnancy. The child outcome measures were diagnosis of autism, diagnosis of attention deficit hyperactivity disorder and intellectual disability. Data sources UK Clinical Practice Research Datalink. Results Data on 80,103 pregnancies were used to study maternal primary care outcomes and were linked to 34,274 children with at least 4-year follow-up for neurodevelopmental outcomes. Women who initiated or continued antidepressants during pregnancy were more likely to have contact with primary and secondary health-care services during and after pregnancy and more likely to be prescribed an antidepressant 2 years following the end of pregnancy than women who did not initiate or continue antidepressants during pregnancy (odds ratioinitiation 2.16, 95% confidence interval 1.95 to 2.39; odds ratiocontinuation 2.40, 95% confidence interval 2.27 to 2.53). There was little evidence for any substantial association with autism (odds ratiomultivariableregression 1.10, 95% confidence interval 0.90 to 1.35; odds ratiopropensityscore 1.06, 95% confidence interval 0.84 to 1.32), attention deficit hyperactivity disorder (odds ratiomultivariableregression 1.02, 95% confidence interval 0.80 to 1.29; odds ratiopropensityscore 0.97, 95% confidence interval 0.75 to 1.25) or intellectual disability (odds ratiomultivariableregression 0.81, 95% confidence interval 0.55 to 1.19; odds ratiopropensityscore 0.89, 95% confidence interval 0.61 to 1.31) in children of women who continued antidepressants compared with those who discontinued antidepressants. There was inconsistent evidence of an association between initiation of antidepressants in pregnancy and diagnosis of autism in offspring (odds ratiomultivariableregression 1.23, 95% confidence interval 0.85 to 1.78; odds ratiopropensityscore 1.64, 95% confidence interval 1.01 to 2.66) but not attention deficit hyperactivity disorder or intellectual disability; however, but results were imprecise owing to smaller numbers. Limitations Several causal-inference analyses lacked precision owing to limited numbers. In addition, adherence to the prescribed treatment was not measured. Conclusions Women prescribed antidepressants during pregnancy had greater service use during and after pregnancy than those not prescribed antidepressants. The evidence against any substantial association with autism, attention deficit hyperactivity disorder or intellectual disability in the children of women who continued compared with those who discontinued antidepressants in pregnancy is reassuring. Potential association of initiation of antidepressants during pregnancy with offspring autism needs further investigation. Future work Further research on larger samples could increase the robustness and precision of these findings. These methods applied could be a template for future pharmaco-epidemiological investigation of other pregnancy-related prescribing safety concerns. Funding This project was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (15/80/19) and will be published in full in Health Technology Assessment; Vol. 27, No. 15. See the NIHR Journals Library website for further project information.
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Affiliation(s)
- Hein Heuvelman
- Department of Population Health Sciences, University of Bristol, Bristol, UK
| | - Neil M Davies
- Department of Population Health Sciences, University of Bristol, Bristol, UK
| | - Yoav Ben-Shlomo
- Department of Population Health Sciences, University of Bristol, Bristol, UK
| | - Alan Emond
- Department of Population Health Sciences, University of Bristol, Bristol, UK
| | - Jonathan Evans
- Department of Population Health Sciences, University of Bristol, Bristol, UK
| | - David Gunnell
- Department of Population Health Sciences, University of Bristol, Bristol, UK
| | - Rachel Liebling
- Fetal Medicine Unit, University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK
| | - Richard Morris
- Department of Population Health Sciences, University of Bristol, Bristol, UK
| | - Rupert Payne
- Department of Population Health Sciences, University of Bristol, Bristol, UK
| | | | | | - Dheeraj Rai
- Department of Population Health Sciences, University of Bristol, Bristol, UK
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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: 79] [Impact Index Per Article: 79.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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14
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Davies NM, Howe LD, Bann D. Educational attainment and health. BMJ 2023; 382:p1602. [PMID: 37479240 DOI: 10.1136/bmj.p1602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/23/2023]
Affiliation(s)
- Neil M Davies
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- KG 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, London, UK
- Department of Statistical Sciences, University College London, London, UK
| | - Laura D Howe
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, University of Bristol, Bristol, UK
| | - David Bann
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Centre for Longitudinal Studies, Social Research Institute, University College London, London, UK
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15
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Barry CJS, Walker VM, Cheesman R, Davey Smith G, Morris TT, Davies NM. How to estimate heritability: a guide for genetic epidemiologists. Int J Epidemiol 2023; 52:624-632. [PMID: 36427280 PMCID: PMC10114051 DOI: 10.1093/ije/dyac224] [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: 05/03/2022] [Accepted: 11/14/2022] [Indexed: 11/27/2022] Open
Abstract
Traditionally, heritability has been estimated using family-based methods such as twin studies. Advancements in molecular genomics have facilitated the development of methods that use large samples of (unrelated or related) genotyped individuals. Here, we provide an overview of common methods applied in genetic epidemiology to estimate heritability, i.e. the proportion of phenotypic variation explained by genetic variation. We provide a guide to key genetic concepts required to understand heritability estimation methods from family-based designs (twin and family studies), genomic designs based on unrelated individuals [linkage disequilibrium score regression, genomic relatedness restricted maximum-likelihood (GREML) estimation] and family-based genomic designs (sibling regression, GREML-kinship, trio-genome-wide complex trait analysis, maternal-genome-wide complex trait analysis, relatedness disequilibrium regression). We describe how heritability is estimated for each method and the assumptions underlying its estimation, and discuss the implications when these assumptions are not met. We further discuss the benefits and limitations of estimating heritability within samples of unrelated individuals compared with samples of related individuals. Overall, this article is intended to help the reader determine the circumstances when each method would be appropriate and why.
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Affiliation(s)
- Ciarrah-Jane S Barry
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Venexia M Walker
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | - Rosa Cheesman
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Tim T Morris
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Neil M Davies
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
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16
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Bann D, Wright L, Davies NM, Moulton V. Weakening of the cognition and height association from 1957 to 2018: Findings from four British birth cohort studies. eLife 2023; 12:e81099. [PMID: 37022953 PMCID: PMC10079289 DOI: 10.7554/elife.81099] [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: 06/15/2022] [Accepted: 03/19/2023] [Indexed: 04/07/2023] Open
Abstract
Background Taller individuals have been repeatedly found to have higher scores on cognitive assessments. Recent studies have suggested that this association can be explained by genetic factors, yet this does not preclude the influence of environmental or social factors that may change over time. We thus tested whether the association changed across time using data from four British birth cohorts (born in 1946, 1958, 1970, and 2001). Methods In each cohort height was measured and cognition via verbal reasoning, vocabulary/comprehension, and mathematical tests; at ages 10/11 and 14/17 years (N=41,418). We examined associations between height and cognition at each age, separately in each cohort, and for each cognitive test administered. Linear and quantile regression models were used. Results Taller participants had higher mean cognitive assessment scores in childhood and adolescence, yet the associations were weaker in later (1970 and 2001) cohorts. For example, the mean difference in height comparing the highest with lowest verbal cognition scores at 10/11 years was 0.57 SD (95% CI = 0.44-0.70) in the 1946 cohort, yet 0.30 SD (0.23-0.37) in the 2001 cohort. Expressed alternatively, there was a reduction in correlation from 0.17 (0.15-0.20) to 0.08 (0.06-0.10). This pattern of change in the association was observed across all ages and cognition measures used, was robust to adjustment for social class and parental height, and modeling of plausible missing-not-at-random scenarios. Quantile regression analyses suggested that these differences were driven by differences in the lower centiles of height, where environmental influence may be greatest. Conclusions Associations between height and cognitive assessment scores in childhood-adolescence substantially weakened from 1957-2018. These results support the notion that environmental and social change can markedly weaken associations between cognition and other traits. Funding DB is supported by the Economic and Social Research Council (grant number ES/M001660/1); DB and LW by the Medical Research Council (MR/V002147/1). The Medical Research Council (MRC) and the University of Bristol support the MRC Integrative Epidemiology Unit [MC_UU_00011/1]. NMD is supported by an Norwegian Research Council Grant number 295989. VM is supported by the CLOSER Innovation Fund WP19 which is funded by the Economic and Social Research Council (award reference: ES/K000357/1) and Economic and Social Research Council (ES/M001660/1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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Affiliation(s)
- David Bann
- Centre for Longitudinal Studies, Social Research Institute, University College LondonLondonUnited Kingdom
| | - Liam Wright
- Centre for Longitudinal Studies, Social Research Institute, University College LondonLondonUnited Kingdom
| | - Neil M Davies
- MRC IEU, University of BristolBristolUnited Kingdom
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and TechnologyTrondheimNorway
| | - Vanessa Moulton
- Centre for Longitudinal Studies, Social Research Institute, University College LondonLondonUnited Kingdom
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17
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Pingault JB, Barkhuizen W, Wang B, Hannigan LJ, Eilertsen EM, Corfield E, Andreassen OA, Ask H, Tesli M, Askeland RB, Davey Smith G, Stoltenberg C, Davies NM, Reichborn-Kjennerud T, Ystrom E, Havdahl A. Genetic nurture versus genetic transmission of risk for ADHD traits in the Norwegian Mother, Father and Child Cohort Study. Mol Psychiatry 2023; 28:1731-1738. [PMID: 36385167 PMCID: PMC10208953 DOI: 10.1038/s41380-022-01863-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 10/26/2022] [Accepted: 10/28/2022] [Indexed: 11/17/2022]
Abstract
Identifying mechanisms underlying the intergenerational transmission of risk for attention-deficit/hyperactivity disorder (ADHD) traits can inform interventions and provide insights into the role of parents in shaping their children's outcomes. We investigated whether genetic transmission and genetic nurture (environmentally mediated effects) underlie associations between polygenic scores indexing parental risk and protective factors and their offspring's ADHD traits. This birth cohort study included 19,506 genotyped mother-father-offspring trios from the Norwegian Mother, Father and Child Cohort Study. Polygenic scores were calculated for parental factors previously associated with ADHD, including psychopathology, substance use, neuroticism, educational attainment, and cognitive performance. Mothers reported on their 8-year-old children's ADHD traits (n = 9,454 children) using the Parent/Teacher Rating Scale for Disruptive Behaviour Disorders. We found that associations between ADHD maternal and paternal polygenic scores and child ADHD traits decreased significantly when adjusting for the child polygenic score (pΔβ = 9.95 × 10-17 for maternal and pΔβ = 1.48 × 10-14 for paternal estimates), suggesting genetic transmission of ADHD risk. Similar patterns suggesting genetic transmission of risk were observed for smoking, educational attainment, and cognition. The maternal polygenic score for neuroticism remained associated with children's ADHD ratings even after adjusting for the child polygenic score, indicating genetic nurture. There was no robust evidence of genetic nurture for other parental factors. Our findings indicate that the intergenerational transmission of risk for ADHD traits is largely explained by the transmission of genetic variants from parents to offspring rather than by genetic nurture. Observational associations between parental factors and childhood ADHD outcomes should not be interpreted as evidence for predominantly environmentally mediated effects.
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Affiliation(s)
- Jean-Baptiste Pingault
- Division of Psychology and Language Sciences, University College London, London, United Kingdom
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King's College, London, United Kingdom
| | - Wikus Barkhuizen
- Division of Psychology and Language Sciences, University College London, London, United Kingdom.
| | - Biyao Wang
- Division of Psychology and Language Sciences, University College London, London, United Kingdom
| | - Laurie J Hannigan
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Espen Moen Eilertsen
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Elizabeth Corfield
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
| | - Ole A Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Helga Ask
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Martin Tesli
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ragna Bugge Askeland
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, United Kingdom
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Camilla Stoltenberg
- Norwegian Institute of Public Health, Oslo, Norway
- University of Bergen, Bergen, Norway
| | - Neil M Davies
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ted Reichborn-Kjennerud
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Eivind Ystrom
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- School of Pharmacy, University of Oslo, Oslo, Norway
| | - Alexandra Havdahl
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
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Wright L, Davies NM, Bann D. The association between cognitive ability and body mass index: A sibling-comparison analysis in four longitudinal studies. PLoS Med 2023; 20:e1004207. [PMID: 37053134 PMCID: PMC10101525 DOI: 10.1371/journal.pmed.1004207] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 02/21/2023] [Indexed: 04/14/2023] Open
Abstract
BACKGROUND Body mass index (BMI) and obesity rates have increased sharply since the 1980s. While multiple epidemiologic studies have found that higher adolescent cognitive ability is associated with lower adult BMI, residual and unobserved confounding due to family background may explain these associations. We used a sibling design to test this association accounting for confounding factors shared within households. METHODS AND FINDINGS We used data from four United States general youth population cohort studies: the National Longitudinal Study of Youth 1979 (NLSY-79), the NLSY-79 Children and Young Adult, the NLSY 1997 (NLSY-97), and the Wisconsin Longitudinal Study (WLS); a total of 12,250 siblings from 5,602 households followed from adolescence up to age 62. We used random effects within-between (REWB) and residualized quantile regression (RQR) models to compare between- and within-family estimates of the association between adolescent cognitive ability and adult BMI (20 to 64 years). In REWB models, moving from the 25th to 75th percentile of adolescent cognitive ability was associated with -0.95 kg/m2 (95% CI = -1.21, -0.69) lower BMI between families. Adjusting for family socioeconomic position reduced the association to -0.61 kg/m2 (-0.90, -0.33). However, within families, the association was just -0.06 kg/m2 (-0.35, 0.23). This pattern of results was found across multiple specifications, including analyses conducted in separate cohorts, models examining age-differences in association, and in RQR models examining the association across the distribution of BMI. Limitations include the possibility that within-family estimates are biased due to measurement error of the exposure, confounding via non-shared factors, and carryover effects. CONCLUSIONS The association between high adolescent cognitive ability and low adult BMI was substantially smaller in within-family compared with between-family analysis. The well-replicated associations between cognitive ability and subsequent BMI may largely reflect confounding by family background factors.
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Affiliation(s)
- Liam Wright
- Centre for Longitudinal Studies, Social Research Institute, University College London, London, United Kingdom
| | - Neil M. Davies
- Division of Psychiatry, University College London, London, United Kingdom
- Department of Statistical Sciences, University College London, London, United Kingdom
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - David Bann
- Centre for Longitudinal Studies, Social Research Institute, University College London, London, United Kingdom
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19
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Svedahl ER, Pape K, Austad B, Vie GÅ, Sarheim Anthun K, Carlsen F, Davies NM, Bjørngaard JH. Impact of altering referral threshold from out-of-hours primary care to hospital on patient safety and further health service use: a cohort study. BMJ Qual Saf 2022; 32:330-340. [PMID: 36522178 DOI: 10.1136/bmjqs-2022-014944] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 09/12/2022] [Indexed: 12/23/2022]
Abstract
OBJECTIVES To estimate the impact of altering referral thresholds from out-of-hours services on older patients' further use of health services and risk of death. DESIGN Cohort study using patient data from primary and specialised health services and demographic data from Statistics Norway and the Norwegian Cause of Death Registry. SETTING Norway PARTICIPANTS: 491 653 patients aged 65 years and older contacting Norwegian out-of-hours services between 2008 and 2016. ANALYSIS Multivariable adjusted and instrumental variable associations between referrals to hospital from out-of-hours services and further health services use and death for up to 6 months.Physicians' proportions of acute referrals of older, unknown patients from out-of-hours work were used as an instrumental variable ('physician referral preference') for their threshold of referral for such patients whose clinical presentations were less clear cut. RESULTS For older patients, whose referrals could be attributed to their physicians' threshold for referral, mean length of stay in hospital increased 3.30 days (95% CI 3.13 to 3.27) within the first 10 days, compared with non-referred patients. Such referrals also increased 6 months use of outpatient specialist clinics and primary care physicians. Importantly, patients with referrals attributable to their physicians' threshold had a substantially reduced risk of death the first 10 days (HR 0.53, 95% CI 0.31 to 0.91), an effect sustaining through the 6-month follow-up period (HR 0.72, 95% CI 0.54 to 0.97). CONCLUSIONS Out-of-hours patients whose referrals are affected by physician referral threshold contribute substantially to the use of health services. However, the referral seems protective by reducing the risk of death in the first 6 months after the referral. Thus, raising the threshold for referral to lower pressure on overcrowded emergency departments and hospitals should not be encouraged without ensuring the accuracy of the referral decisions, ideally through high-quality randomised controlled trial evidence.
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Affiliation(s)
- Ellen Rabben Svedahl
- Department of Public Health and Nursing, NTNU Norwegian University of Science and Technology, Trondheim, Norway
| | - Kristine Pape
- Department of Public Health and Nursing, NTNU Norwegian University of Science and Technology, Trondheim, Norway
| | - Bjarne Austad
- Department of Public Health and Nursing, NTNU Norwegian University of Science and Technology, Trondheim, Norway
| | - Gunnhild Åberge Vie
- Department of Public Health and Nursing, NTNU Norwegian University of Science and Technology, Trondheim, Norway
| | | | - Fredrik Carlsen
- Department of Economics, Norwegian University of Science and Technology, Trondheim, Norway
| | - Neil M Davies
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK.,Bristol Medical School, University of Bristol, Bristol, UK
| | - Johan Håkon Bjørngaard
- Department of Public Health and Nursing, NTNU Norwegian University of Science and Technology, Trondheim, Norway.,Faculty of Nursing and Health Sciences, Nord University, Levanger, Norway
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Carter AR, Harrison S, Gill D, Smith GD, Taylor AE, Howe LD, Davies NM. Correction to: Educational attainment as a modifier for the effect of polygenic scores for cardiovascular risk factors: cross-sectional and prospective analysis of UK Biobank. Int J Epidemiol 2022; 51:1703. [PMID: 35994002 PMCID: PMC9557848 DOI: 10.1093/ije/dyac169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
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Korologou-Linden R, Bhatta L, Brumpton BM, Howe LD, Millard LAC, Kolaric K, Ben-Shlomo Y, Williams DM, Smith GD, Anderson EL, Stergiakouli E, Davies NM. The causes and consequences of Alzheimer's disease: phenome-wide evidence from Mendelian randomization. Nat Commun 2022; 13:4726. [PMID: 35953482 PMCID: PMC9372151 DOI: 10.1038/s41467-022-32183-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 07/20/2022] [Indexed: 12/12/2022] Open
Abstract
Alzheimer's disease (AD) has no proven causal and modifiable risk factors, or effective interventions. We report a phenome-wide association study (PheWAS) of genetic liability for AD in 334,968 participants of the UK Biobank study, stratified by age. We also examined the effects of AD genetic liability on previously implicated risk factors. We replicated these analyses in the HUNT study. PheWAS hits and previously implicated risk factors were followed up in a Mendelian randomization (MR) framework to identify the causal effect of each risk factor on AD risk. A higher genetic liability for AD was associated with medical history and cognitive, lifestyle, physical and blood-based measures as early as 39 years of age. These effects were largely driven by the APOE gene. The follow-up MR analyses were primarily null, implying that most of these associations are likely to be a consequence of prodromal disease or selection bias, rather than the risk factor causing the disease.
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Affiliation(s)
- Roxanna Korologou-Linden
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK.
| | - Laxmi Bhatta
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ben M Brumpton
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
- HUNT Research Center, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway
| | - Laura D Howe
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Louise A C Millard
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
- Intelligent Systems Laboratory, Department of Computer Science, University of Bristol, Bristol, UK
| | - Katarina Kolaric
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Yoav Ben-Shlomo
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Dylan M Williams
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
- Department of Medical Epidemiology & Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Emma L Anderson
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Evie Stergiakouli
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Neil M Davies
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
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22
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Dixon P, Harrison S, Hollingworth W, Davies NM, Davey Smith G. Estimating the causal effect of liability to disease on healthcare costs using Mendelian Randomization. Econ Hum Biol 2022; 46:101154. [PMID: 35803012 DOI: 10.1016/j.ehb.2022.101154] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 06/15/2022] [Accepted: 06/28/2022] [Indexed: 05/27/2023]
Abstract
Accurate measurement of the effects of disease status on healthcare costs is important in the pragmatic evaluation of interventions but is complicated by endogeneity bias. Mendelian Randomization, the use of random perturbations in germline genetic variation as instrumental variables, can avoid these limitations. We used a novel Mendelian Randomization analysis to model the causal impact on inpatient hospital costs of liability to six prevalent diseases and health conditions: asthma, eczema, migraine, coronary heart disease, Type 2 diabetes, and depression. We identified genetic variants from replicated genome-wide associations studies and estimated their association with inpatient hospital costs on over 300,000 individuals. There was concordance of findings across varieties of sensitivity analyses, including stratification by sex and methods robust to violations of the exclusion restriction. Results overall were imprecise and we could not rule out large effects of liability to disease on healthcare costs. In particular, genetic liability to coronary heart disease had substantial impacts on costs.
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Affiliation(s)
- Padraig Dixon
- Nuffield Department of Primary Care Health Sciences, University of Oxford, United Kingdom; MRC Integrative Epidemiology Unit, University of Bristol, United Kingdom.
| | - Sean Harrison
- MRC Integrative Epidemiology Unit, University of Bristol, United Kingdom; Population Health Sciences, University of Bristol, United Kingdom
| | | | - Neil M Davies
- MRC Integrative Epidemiology Unit, University of Bristol, United Kingdom; Population Health Sciences, University of Bristol, United Kingdom; K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Norway
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, United Kingdom; Population Health Sciences, University of Bristol, United Kingdom; NIHR Biomedical Research Centre, University of Bristol, United Kingdom
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23
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Nilsen SM, Asheim A, Carlsen F, Sarheim Anthun K, Vatten LJ, Aam S, Davies NM, Bjørngaard JH. How do busy hospital circumstances affect mortality and readmission within 60 days: A cohort study of 680 000 acute admissions in Norway. Health Policy 2022; 126:808-815. [PMID: 35644720 PMCID: PMC7614243 DOI: 10.1016/j.healthpol.2022.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 02/18/2022] [Accepted: 05/18/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVE To study mortality and readmissions for older patients admitted during more and less busy hospital circumstances. DESIGN Cohort study where we identified patients that were admitted to the same hospital, during the same month and day of the week. We estimated effects of inflow of acute patients and the number of concurrent acute inpatients. Mortality and readmissions were analysed using stratified Cox-regression. SETTING All people 80 years and older acutely admitted to Norwegian hospitals between 2008 and 2016. MAIN OUTCOME MEASURES Mortality and readmissions within 60 days from admission. RESULTS Among 294 653 patients with 685 197 admissions, mean age was 86 years (standard deviation 5). Overall, 13% died within 60 days. An interquartile range difference in inflow of acute patients was associated with a hazard ratio (HR) of 0.99, 95% confidence interval (95% CI) 0.98 to 1.00). There was little evidence of differences in readmissions, but a 7% higher risk (HR 1.07, 95% CI 1.06 to 1.09) of being discharged outside ordinary daytime working hours. CONCLUSIONS Older patients admitted during busier circumstances had similar mortality and readmissions to those admitted during less busy periods. Yet, they showed a higher risk of discharge outside daytime working hours. Despite limited effects of busyness on a hospital level, there could still be harmful effects of local situations.
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Affiliation(s)
- Sara Marie Nilsen
- Center for Health Care Improvement, St. Olav's hospital, Trondheim University Hospital, Trondheim, Norway.
| | - Andreas Asheim
- Center for Health Care Improvement, St. Olav’s hospital, Trondheim University Hospital, Trondheim, Norway,Norwegian University of Science and Technology, Department of Mathematical Sciences, Trondheim, Norway
| | - Fredrik Carlsen
- Norwegian University of Science and Technology, Department of Economics, Trondheim, Norway
| | - Kjartan Sarheim Anthun
- Department of Health Research, SINTFF Digital, Trondheim, Norway,Norwegian University of Science and Technology, Department of Public Health and Nursing, Trondheim, Norway
| | - Lars Johan Vatten
- Norwegian University of Science and Technology, Department of Public Health and Nursing, Trondheim, Norway
| | - Stina Aam
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Science, NTNU-Norwegian University of Science and Technology, Trondheim, Norway,Department of Geriatric Medicine, Clinic of Medicine, St. Olavs hospital, Trondheim University Hospital, Trondheim, Norway
| | - Neil M Davies
- Medical Research Council Integrative Epidemiology Unit, 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
| | - Johan Håkon Bjørngaard
- Norwegian University of Science and Technology, Department of Public Health and Nursing, Trondheim, Norway,Nord University, Faculty of Nursing and Health Sciences, Levanger, Norway
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24
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Austin TR, McHugh CP, Brody JA, Bis JC, Sitlani CM, Bartz TM, Biggs ML, Bansal N, Buzkova P, Carr SA, deFilippi CR, Elkind MSV, Fink HA, Floyd JS, Fohner AE, Gerszten RE, Heckbert SR, Katz DH, Kizer JR, Lemaitre RN, Longstreth WT, McKnight B, Mei H, Mukamal KJ, Newman AB, Ngo D, Odden MC, Vasan RS, Shojaie A, Simon N, Smith GD, Davies NM, Siscovick DS, Sotoodehnia N, Tracy RP, Wiggins KL, Zheng J, Psaty BM. Proteomics and Population Biology in the Cardiovascular Health Study (CHS): design of a study with mentored access and active data sharing. Eur J Epidemiol 2022; 37:755-765. [PMID: 35790642 PMCID: PMC9255954 DOI: 10.1007/s10654-022-00888-z] [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] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 06/03/2022] [Indexed: 11/08/2022]
Abstract
BACKGROUND In the last decade, genomic studies have identified and replicated thousands of genetic associations with measures of health and disease and contributed to the understanding of the etiology of a variety of health conditions. Proteins are key biomarkers in clinical medicine and often drug-therapy targets. Like genomics, proteomics can advance our understanding of biology. METHODS AND RESULTS In the setting of the Cardiovascular Health Study (CHS), a cohort study of older adults, an aptamer-based method that has high sensitivity for low-abundance proteins was used to assay 4979 proteins in frozen, stored plasma from 3188 participants (61% women, mean age 74 years). CHS provides active support, including central analysis, for seven phenotype-specific working groups (WGs). Each CHS WG is led by one or two senior investigators and includes 10 to 20 early or mid-career scientists. In this setting of mentored access, the proteomic data and analytic methods are widely shared with the WGs and investigators so that they may evaluate associations between baseline levels of circulating proteins and the incidence of a variety of health outcomes in prospective cohort analyses. We describe the design of CHS, the CHS Proteomics Study, characteristics of participants, quality control measures, and structural characteristics of the data provided to CHS WGs. We additionally highlight plans for validation and replication of novel proteomic associations. CONCLUSION The CHS Proteomics Study offers an opportunity for collaborative data sharing to improve our understanding of the etiology of a variety of health conditions in older adults.
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Affiliation(s)
- Thomas R Austin
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA. .,Department of Epidemiology, University of Washington, Seattle, WA, USA.
| | | | - Jennifer A Brody
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA.,Department of Medicine, University of Washington, Seattle, WA, USA
| | - Joshua C Bis
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA.,Department of Medicine, University of Washington, Seattle, WA, USA
| | - Colleen M Sitlani
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA.,Department of Medicine, University of Washington, Seattle, WA, USA
| | - Traci M Bartz
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA.,Department of Medicine, University of Washington, Seattle, WA, USA.,Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Mary L Biggs
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA.,Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Nisha Bansal
- Division of Nephrology, University of Washington, Seattle, WA, USA
| | - Petra Buzkova
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | | | | | | | - Howard A Fink
- Geriatric Research Education & Clinical Center, Minneapolis VA Healthcare System, Minneapolis, MN, USA
| | - James S Floyd
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA.,Department of Epidemiology, University of Washington, Seattle, WA, USA.,Department of Medicine, University of Washington, Seattle, WA, USA
| | - Alison E Fohner
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA.,Department of Epidemiology, University of Washington, Seattle, WA, USA.,Institute of Public Health Genetics, University of Washington, Seattle, WA, USA
| | - Robert E Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Susan R Heckbert
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA.,Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Daniel H Katz
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Jorge R Kizer
- Cardiology Section, San Francisco VA Health Care System, San Francisco, CA, USA.,Department of Biostatistics, University of California San Francisco, San Francisco, CA, USA.,Department of Epidemology, University of California San Francisco, San Francisco, CA, USA.,Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Rozenn N Lemaitre
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA.,Department of Medicine, University of Washington, Seattle, WA, USA
| | - W T Longstreth
- Department of Epidemiology, University of Washington, Seattle, WA, USA.,Department of Neurology, University of Washington, Seattle, WA, USA
| | - Barbara McKnight
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Hao Mei
- Department of Data Science, University of Mississippi Medical Center, Jackson, MS, USA
| | | | - Anne B Newman
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Debby Ngo
- Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Michelle C Odden
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA
| | - Ramachandran S Vasan
- Department of Epidemiology, School of Public Health, Boston University, Boston, MA, USA.,Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Ali Shojaie
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Noah Simon
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
| | - Neil M Davies
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK.,K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Norwegian, Norway.,Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
| | | | - Nona Sotoodehnia
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA.,Division of Cardiology, University of Washington, Seattle, WA, USA
| | - Russell P Tracy
- Departments of Pathology & Laboratory Medicine, and Biochemistry, Larner College of Medicine, University of Vermont, Burlington, VT, USA
| | - Kerri L Wiggins
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA.,Department of Medicine, University of Washington, Seattle, WA, USA
| | - Jie Zheng
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA.,Department of Epidemiology, University of Washington, Seattle, WA, USA.,Department of Medicine, University of Washington, Seattle, WA, USA.,Department of Health Systems and Population Health, University of Washington, Seattle, WA, USA
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25
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Bann D, Wright L, Hardy R, Williams DM, Davies NM. Polygenic and socioeconomic risk for high body mass index: 69 years of follow-up across life. PLoS Genet 2022; 18:e1010233. [PMID: 35834443 PMCID: PMC9282556 DOI: 10.1371/journal.pgen.1010233] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 05/03/2022] [Indexed: 11/29/2022] Open
Abstract
Genetic influences on body mass index (BMI) appear to markedly differ across life, yet existing research is equivocal and limited by a paucity of life course data. We thus used a birth cohort study to investigate differences in association and explained variance in polygenic risk for high BMI across infancy to old age (2-69 years). A secondary aim was to investigate how the association between BMI and a key purported environmental determinant (childhood socioeconomic position) differed across life, and whether this operated independently and/or multiplicatively of genetic influences. Data were from up to 2677 participants in the MRC National Survey of Health and Development, with measured BMI at 12 timepoints from 2-69 years. We used multiple polygenic indices from GWAS of adult and childhood BMI, and investigated their associations with BMI at each age. For polygenic liability to higher adult BMI, the trajectories of effect size (β) and explained variance (R2) diverged: explained variance peaked in early adulthood and plateaued thereafter, while absolute effect sizes increased throughout adulthood. For polygenic liability to higher childhood BMI, explained variance was largest in adolescence and early adulthood; effect sizes were marginally smaller in absolute terms from adolescence to adulthood. All polygenic indices were related to higher variation in BMI; quantile regression analyses showed that effect sizes were sizably larger at the upper end of the BMI distribution. Socioeconomic and polygenic risk for higher BMI across life appear to operate additively; we found little evidence of interaction. Our findings highlight the likely independent influences of polygenic and socioeconomic factors on BMI across life. Despite sizable associations, the BMI variance explained by each plateaued or declined across adulthood while BMI variance itself increased. This is suggestive of the increasing importance of chance ('non-shared') environmental influences on BMI across life.
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Affiliation(s)
- David Bann
- Centre for Longitudinal Studies, Social Research Institute, UCL, London, United Kingdom
- * E-mail: (DB); (LW)
| | - Liam Wright
- Centre for Longitudinal Studies, Social Research Institute, UCL, London, United Kingdom
- * E-mail: (DB); (LW)
| | - Rebecca Hardy
- School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, United Kingdom
- Social Research Institute, UCL, London, United Kingdom
| | - Dylan M. Williams
- MRC Unit for Lifelong Health and Ageing at UCL, London, United Kingdom
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Neil M. Davies
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
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26
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Barry CJS, Davies NM, Morris TT. Investigating how the accuracy of teacher expectations of pupil performance relate to socioeconomic and genetic factors. Sci Rep 2022; 12:7120. [PMID: 35504952 PMCID: PMC9065134 DOI: 10.1038/s41598-022-11347-w] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 04/01/2022] [Indexed: 11/29/2022] Open
Abstract
Teacher expectations of pupil ability can influence educational progression, impacting subsequent streaming and exam level. Systematic discrepancies between teacher expectations of pupil achievement may therefore have a detrimental effect on children's education. Associations between socioeconomic and demographic factors with teacher expectation accuracy have been demonstrated, but it is not known how teacher expectations of achievement may relate to genetic factors. We investigated these relationships using nationally standardized exam results at ages 11 and 14 from a UK longitudinal cohort study. We found that teacher expectation of achievement was strongly correlated with educational test scores. Furthermore, the accuracy of teacher expectation was patterned by pupil socioeconomic background but not teacher characteristics. The accuracy of teacher expectation related to pupil's genetic liability to education as captured by a polygenic score for educational attainment. Despite correlation with the polygenic score, we found no strong evidence for genomewide SNP heritability in teacher reporting accuracy.
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Affiliation(s)
- Ciarrah-Jane Shannon Barry
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, BS8 2BN, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK.
| | - Neil M Davies
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Tim T Morris
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
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27
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Wade KH, Yarmolinsky J, Giovannucci E, Lewis SJ, Millwood IY, Munafò MR, Meddens F, Burrows K, Bell JA, Davies NM, Mariosa D, Kanerva N, Vincent EE, Smith-Byrne K, Guida F, Gunter MJ, Sanderson E, Dudbridge F, Burgess S, Cornelis MC, Richardson TG, Borges MC, Bowden J, Hemani G, Cho Y, Spiller W, Richmond RC, Carter AR, Langdon R, Lawlor DA, Walters RG, Vimaleswaran KS, Anderson A, Sandu MR, Tilling K, Davey Smith G, Martin RM, Relton CL. Applying Mendelian randomization to appraise causality in relationships between nutrition and cancer. Cancer Causes Control 2022; 33:631-652. [PMID: 35274198 PMCID: PMC9010389 DOI: 10.1007/s10552-022-01562-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 02/10/2022] [Indexed: 02/08/2023]
Abstract
Dietary factors are assumed to play an important role in cancer risk, apparent in consensus recommendations for cancer prevention that promote nutritional changes. However, the evidence in this field has been generated predominantly through observational studies, which may result in biased effect estimates because of confounding, exposure misclassification, and reverse causality. With major geographical differences and rapid changes in cancer incidence over time, it is crucial to establish which of the observational associations reflect causality and to identify novel risk factors as these may be modified to prevent the onset of cancer and reduce its progression. Mendelian randomization (MR) uses the special properties of germline genetic variation to strengthen causal inference regarding potentially modifiable exposures and disease risk. MR can be implemented through instrumental variable (IV) analysis and, when robustly performed, is generally less prone to confounding, reverse causation and measurement error than conventional observational methods and has different sources of bias (discussed in detail below). It is increasingly used to facilitate causal inference in epidemiology and provides an opportunity to explore the effects of nutritional exposures on cancer incidence and progression in a cost-effective and timely manner. Here, we introduce the concept of MR and discuss its current application in understanding the impact of nutritional factors (e.g., any measure of diet and nutritional intake, circulating biomarkers, patterns, preference or behaviour) on cancer aetiology and, thus, opportunities for MR to contribute to the development of nutritional recommendations and policies for cancer prevention. We provide applied examples of MR studies examining the role of nutritional factors in cancer to illustrate how this method can be used to help prioritise or deprioritise the evaluation of specific nutritional factors as intervention targets in randomised controlled trials. We describe possible biases when using MR, and methodological developments aimed at investigating and potentially overcoming these biases when present. Lastly, we consider the use of MR in identifying causally relevant nutritional risk factors for various cancers in different regions across the world, given notable geographical differences in some cancers. We also discuss how MR results could be translated into further research and policy. We conclude that findings from MR studies, which corroborate those from other well-conducted studies with different and orthogonal biases, are poised to substantially improve our understanding of nutritional influences on cancer. For such corroboration, there is a requirement for an interdisciplinary and collaborative approach to investigate risk factors for cancer incidence and progression.
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Affiliation(s)
- Kaitlin H Wade
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK.
| | - James Yarmolinsky
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Edward Giovannucci
- Departments of Nutrition and Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Sarah J Lewis
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
- Bristol National Institute for Health Research (NIHR) Biomedical Research Centre, Bristol, UK
| | - Iona Y Millwood
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU) and the Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Marcus R Munafò
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
- Bristol National Institute for Health Research (NIHR) Biomedical Research Centre, Bristol, UK
- School of Psychological Science, University of Bristol, Bristol, UK
| | - Fleur Meddens
- Department of Economics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Applied Economics, Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands
| | - Kimberley Burrows
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Joshua A Bell
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Neil M Davies
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Daniela Mariosa
- International Agency for Research On Cancer (IARC), Lyon, France
| | | | - Emma E Vincent
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
- Cellular and Molecular Medicine, Faculty of Life Sciences, University of Bristol, Bristol, UK
| | - Karl Smith-Byrne
- International Agency for Research On Cancer (IARC), Lyon, France
| | - Florence Guida
- International Agency for Research On Cancer (IARC), Lyon, France
| | - Marc J Gunter
- International Agency for Research On Cancer (IARC), Lyon, France
| | - Eleanor Sanderson
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Frank Dudbridge
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | | | - Tom G Richardson
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Maria Carolina Borges
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Jack Bowden
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
- Research Innovation Learning and Development (RILD) Building, University of Exeter Medical School, Exeter, UK
| | - Gibran Hemani
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Yoonsu Cho
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Wes Spiller
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Rebecca C Richmond
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Alice R Carter
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Ryan Langdon
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Deborah A Lawlor
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
- Bristol National Institute for Health Research (NIHR) Biomedical Research Centre, Bristol, UK
| | - Robin G Walters
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU) and the Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | | | - Annie Anderson
- Population Health and Genomics, School of Medicine, University of Dundee, Dundee, Scotland, UK
| | - Meda R Sandu
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
- NIHR Biomedical Research Centre, Bristol, UK
| | - Kate Tilling
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
- Bristol National Institute for Health Research (NIHR) Biomedical Research Centre, Bristol, UK
| | - George Davey Smith
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
- Bristol National Institute for Health Research (NIHR) Biomedical Research Centre, Bristol, UK
| | - Richard M Martin
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Caroline L Relton
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
- Bristol National Institute for Health Research (NIHR) Biomedical Research Centre, Bristol, UK
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Walker VM, Vujkovic M, Carter AR, Davies NM, Udler MS, Levin MG, Davey Smith G, Voight BF, Gaunt TR, Damrauer SM. Separating the direct effects of traits on atherosclerotic cardiovascular disease from those mediated by type 2 diabetes. Diabetologia 2022; 65:790-799. [PMID: 35129650 PMCID: PMC8960614 DOI: 10.1007/s00125-022-05653-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 11/22/2021] [Indexed: 12/31/2022]
Abstract
AIMS/HYPOTHESIS Type 2 diabetes and atherosclerotic CVD share many risk factors. This study aimed to systematically assess a broad range of continuous traits to separate their direct effects on coronary and peripheral artery disease from those mediated by type 2 diabetes. METHODS Our main analysis was a two-step Mendelian randomisation for mediation to quantify the extent to which the associations observed between continuous traits and liability to atherosclerotic CVD were mediated by liability to type 2 diabetes. To support this analysis, we performed several univariate Mendelian randomisation analyses to examine the associations between our continuous traits, liability to type 2 diabetes and liability to atherosclerotic CVD. RESULTS Eight traits were eligible for the two-step Mendelian randomisation with liability to coronary artery disease as the outcome and we found similar direct and total effects in most cases. Exceptions included fasting insulin and hip circumference where the proportion mediated by liability to type 2 diabetes was estimated as 56% and 52%, respectively. Six traits were eligible for the analysis with liability to peripheral artery disease as the outcome. Again, we found limited evidence to support mediation by liability to type 2 diabetes for all traits apart from fasting insulin (proportion mediated: 70%). CONCLUSIONS/INTERPRETATION Most traits were found to affect liability to atherosclerotic CVD independently of their relationship with liability to type 2 diabetes. These traits are therefore important for understanding atherosclerotic CVD risk regardless of an individual's liability to type 2 diabetes.
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Affiliation(s)
- Venexia M Walker
- MRC University of Bristol Integrative Epidemiology Unit, Bristol, UK.
- Bristol Medical School: Population Health Sciences, University of Bristol, Bristol, UK.
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
| | - Marijana Vujkovic
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Alice R Carter
- MRC University of Bristol Integrative Epidemiology Unit, Bristol, UK
- Bristol Medical School: Population Health Sciences, University of Bristol, Bristol, UK
| | - Neil M Davies
- MRC University of Bristol Integrative Epidemiology Unit, Bristol, UK
- Bristol Medical School: Population Health Sciences, University of Bristol, Bristol, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Miriam S Udler
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Michael G Levin
- Division of Cardiovascular Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, USA
| | - George Davey Smith
- MRC University of Bristol Integrative Epidemiology Unit, Bristol, UK
- Bristol Medical School: Population Health Sciences, University of Bristol, Bristol, UK
| | - Benjamin F Voight
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Institute of Translational Medicine and Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Tom R Gaunt
- MRC University of Bristol Integrative Epidemiology Unit, Bristol, UK
- Bristol Medical School: Population Health Sciences, University of Bristol, Bristol, UK
| | - Scott M Damrauer
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
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29
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Howe LJ, Nivard MG, Morris TT, Hansen AF, Rasheed H, Cho Y, Chittoor G, Ahlskog R, Lind PA, Palviainen T, van der Zee MD, Cheesman R, Mangino M, Wang Y, Li S, Klaric L, Ratliff SM, Bielak LF, Nygaard M, Giannelis A, Willoughby EA, Reynolds CA, Balbona JV, Andreassen OA, Ask H, Baras A, Bauer CR, Boomsma DI, Campbell A, Campbell H, Chen Z, Christofidou P, Corfield E, Dahm CC, Dokuru DR, Evans LM, de Geus EJC, Giddaluru S, Gordon SD, Harden KP, Hill WD, Hughes A, Kerr SM, Kim Y, Kweon H, Latvala A, Lawlor DA, Li L, Lin K, Magnus P, Magnusson PKE, Mallard TT, Martikainen P, Mills MC, Njølstad PR, Overton JD, Pedersen NL, Porteous DJ, Reid J, Silventoinen K, Southey MC, Stoltenberg C, Tucker-Drob EM, Wright MJ, Hewitt JK, Keller MC, Stallings MC, Lee JJ, Christensen K, Kardia SLR, Peyser PA, Smith JA, Wilson JF, Hopper JL, Hägg S, Spector TD, Pingault JB, Plomin R, Havdahl A, Bartels M, Martin NG, Oskarsson S, Justice AE, Millwood IY, Hveem K, Naess Ø, Willer CJ, Åsvold BO, Koellinger PD, Kaprio J, Medland SE, Walters RG, Benjamin DJ, Turley P, Evans DM, Davey Smith G, Hayward C, Brumpton B, Hemani G, Davies NM. Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects. Nat Genet 2022; 54:581-592. [PMID: 35534559 PMCID: PMC9110300 DOI: 10.1038/s41588-022-01062-7] [Citation(s) in RCA: 95] [Impact Index Per Article: 47.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 03/25/2022] [Indexed: 02/01/2023]
Abstract
Estimates from genome-wide association studies (GWAS) of unrelated individuals capture effects of inherited variation (direct effects), demography (population stratification, assortative mating) and relatives (indirect genetic effects). Family-based GWAS designs can control for demographic and indirect genetic effects, but large-scale family datasets have been lacking. We combined data from 178,086 siblings from 19 cohorts to generate population (between-family) and within-sibship (within-family) GWAS estimates for 25 phenotypes. Within-sibship GWAS estimates were smaller than population estimates for height, educational attainment, age at first birth, number of children, cognitive ability, depressive symptoms and smoking. Some differences were observed in downstream SNP heritability, genetic correlations and Mendelian randomization analyses. For example, the within-sibship genetic correlation between educational attainment and body mass index attenuated towards zero. In contrast, analyses of most molecular phenotypes (for example, low-density lipoprotein-cholesterol) were generally consistent. We also found within-sibship evidence of polygenic adaptation on taller height. Here, we illustrate the importance of family-based GWAS data for phenotypes influenced by demographic and indirect genetic effects.
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Affiliation(s)
- Laurence J Howe
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Michel G Nivard
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, the Netherlands
| | - Tim T Morris
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Ailin F Hansen
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Humaira Rasheed
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Yoonsu Cho
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Geetha Chittoor
- Department of Population Health Sciences, Geisinger Health, Danville, PA, USA
| | - Rafael Ahlskog
- Department of Government, Uppsala University, Uppsala, Sweden
| | - Penelope A Lind
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia
- School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia
- Faculty of Medicine, University of Queensland, Brisbane, Australia
| | - Teemu Palviainen
- Institute for Molecular Medicine FIMM, University of Helsinki, Helsinki, Finland
| | - Matthijs D van der Zee
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, the Netherlands
| | - Rosa Cheesman
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- Social Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- NIHR Biomedical Research Centre at Guy's and St Thomas' Foundation Trust, London, UK
| | - Yunzhang Wang
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Lucija Klaric
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Scott M Ratliff
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Lawrence F Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Marianne Nygaard
- The Danish Twin Registry, Department of Public Health, University of Southern Denmark, Odense, Denmark
- Department of Clinical Genetics, Odense University Hospital, Odense, Denmark
| | | | | | - Chandra A Reynolds
- Department of Psychology, University of California, Riverside, Riverside, CA, USA
| | - Jared V Balbona
- Department of Psychology & Neuroscience, University of Colorado at Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - Ole A Andreassen
- NORMENT Centre, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Helga Ask
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
| | - Aris Baras
- Regeneron Genetics Center, Tarrytown, NY, USA
| | - Christopher R Bauer
- BioMarin Pharmaceutical Inc., Novato, CA, USA
- Biomedical and Translational Informatics, Geisinger Health, Danville, PA, USA
| | - Dorret I Boomsma
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, the Netherlands
- Amsterdam Public Health (APH) and Amsterdam Reproduction and Development (AR&D), Amsterdam, the Netherlands
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics & Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Harry Campbell
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Zhengming Chen
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | | | - Elizabeth Corfield
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
| | | | - Deepika R Dokuru
- Department of Psychology & Neuroscience, University of Colorado at Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - Luke M Evans
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
- Department of Ecology & Evolutionary Biology, University of Colorado at Boulder, Boulder, CO, USA
| | - Eco J C de Geus
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam UMC, Amsterdam, the Netherlands
| | - Sudheer Giddaluru
- Institute of Health and Society, University of Oslo, Oslo, Norway
- Norwegian Institute of Public Health, Oslo, Norway
| | - Scott D Gordon
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - K Paige Harden
- Department of Psychology and Population Research Center, University of Texas at Austin, Austin, TX, USA
| | - W David Hill
- Lothian Birth Cohorts Group, Department of Psychology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Amanda Hughes
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Shona M Kerr
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Yongkang Kim
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - Hyeokmoon Kweon
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Antti Latvala
- Institute for Molecular Medicine FIMM, University of Helsinki, Helsinki, Finland
- Institute of Criminology and Legal Policy, Faculty of Social Sciences, University of Helsinki, Helsinki, Finland
| | - Deborah A Lawlor
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Bristol NIHR Biomedical Research Centre, Bristol, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Kuang Lin
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Per Magnus
- Centre for Fertility and Health, Norwegian Institute of Public Health, Skøyen, Oslo, Norway
| | - Patrik K E Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Travis T Mallard
- Department of Psychology and Population Research Center, University of Texas at Austin, Austin, TX, USA
| | - Pekka Martikainen
- Population Research Unit, Faculty of Social Sciences, University of Helsinki, Helsinki, Finland
- The Max Planck Institute for Demographic Research, Rostock, Germany
- Department of Public Health Sciences, Stockholm University, Stockholm, Sweden
| | - Melinda C Mills
- Leverhulme Centre for Demographic Science, University of Oxford, Oxford, UK
| | - Pål Rasmus Njølstad
- Department of Clinical Science, University of Bergen, Bergen, Norway
- Children and Youth Clinic, Haukeland University Hospital, Bergen, Norway
| | | | - Nancy L Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - David J Porteous
- Centre for Genomic and Experimental Medicine, Institute of Genetics & Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | | | - Karri Silventoinen
- Population Research Unit, Faculty of Social Sciences, University of Helsinki, Helsinki, Finland
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Camilla Stoltenberg
- Norwegian Institute of Public Health, Oslo, Norway
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway
| | - Elliot M Tucker-Drob
- Department of Psychology and Population Research Center, University of Texas at Austin, Austin, TX, USA
| | - Margaret J Wright
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
| | | | | | - John K Hewitt
- Department of Psychology & Neuroscience, University of Colorado at Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - Matthew C Keller
- Department of Psychology & Neuroscience, University of Colorado at Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - Michael C Stallings
- Department of Psychology & Neuroscience, University of Colorado at Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - James J Lee
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Kaare Christensen
- The Danish Twin Registry, Department of Public Health, University of Southern Denmark, Odense, Denmark
- Department of Clinical Genetics, Odense University Hospital, Odense, Denmark
- Department of Clinical Biochemistry and Pharmacology, Odense University Hospital, Odense, Denmark
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - James F Wilson
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Sara Hägg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Jean-Baptiste Pingault
- Social Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Department of Clinical, Educational and Health Psychology, University College London, London, UK
| | - Robert Plomin
- Social Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Alexandra Havdahl
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
| | - Meike Bartels
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, the Netherlands
| | - Nicholas G Martin
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Sven Oskarsson
- Department of Government, Uppsala University, Uppsala, Sweden
| | - Anne E Justice
- Department of Population Health Sciences, Geisinger Health, Danville, PA, USA
| | - Iona Y Millwood
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - Kristian Hveem
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- HUNT Research Center, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway
| | - Øyvind Naess
- Institute of Health and Society, University of Oslo, Oslo, Norway
- Norwegian Institute of Public Health, Oslo, Norway
| | - Cristen J Willer
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Internal Medicine: Cardiology, University of Michigan, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Bjørn Olav Åsvold
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- HUNT Research Center, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway
- Department of Endocrinology, Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Philipp D Koellinger
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- La Follette School of Public Affairs, University of Wisconsin-Madison, Madison, WI, USA
| | - Jaakko Kaprio
- Institute for Molecular Medicine FIMM, University of Helsinki, Helsinki, Finland
| | - Sarah E Medland
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia
- Faculty of Medicine, University of Queensland, Brisbane, Australia
- School of Psychology, University of Queensland, Brisbane, Queensland, Australia
| | - Robin G Walters
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - Daniel J Benjamin
- UCLA Anderson School of Management, Los Angeles, CA, USA
- Human Genetics Department, UCLA David Geffen School of Medicine, Gonda (Goldschmied) Neuroscience and Genetics Research Center, Los Angeles, CA, USA
- National Bureau of Economic Research, Cambridge, MA, USA
| | - Patrick Turley
- Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA
- Department of Economics, University of Southern California, Los Angeles, CA, USA
| | - David M Evans
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- University of Queensland Diamantina Institute, University of Queensland, Brisbane, Queensland, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Ben Brumpton
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.
- HUNT Research Center, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway.
| | - Gibran Hemani
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Neil M Davies
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.
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30
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Carter AR, Harrison S, Gill D, Davey Smith G, Taylor AE, Howe LD, Davies NM. Educational attainment as a modifier for the effect of polygenic scores for cardiovascular risk factors: cross-sectional and prospective analysis of UK Biobank. Int J Epidemiol 2022; 51:885-897. [PMID: 35134953 PMCID: PMC9189971 DOI: 10.1093/ije/dyac002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 01/06/2022] [Indexed: 01/22/2023] Open
Abstract
Background Understanding the interplay between educational attainment and genetic predictors of cardiovascular risk may improve our understanding of the aetiology of educational inequalities in cardiovascular disease. Methods In up to 320 120 UK Biobank participants of White British ancestry (mean age = 57 years, female 54%), we created polygenic scores for nine cardiovascular risk factors or diseases: alcohol consumption, body mass index, low-density lipoprotein cholesterol, lifetime smoking behaviour, systolic blood pressure, atrial fibrillation, coronary heart disease, type 2 diabetes and stroke. We estimated whether educational attainment modified genetic susceptibility to these risk factors and diseases. Results On the additive scale, higher educational attainment reduced genetic susceptibility to higher body mass index, smoking, atrial fibrillation and type 2 diabetes, but increased genetic susceptibility to higher LDL-C and higher systolic blood pressure. On the multiplicative scale, there was evidence that higher educational attainment increased genetic susceptibility to atrial fibrillation and coronary heart disease, but little evidence of effect modification was found for all other traits considered. Conclusions Educational attainment modifies the genetic susceptibility to some cardiovascular risk factors and diseases. The direction of this effect was mixed across traits considered and differences in associations between the effect of the polygenic score across strata of educational attainment was uniformly small. Therefore, any effect modification by education of genetic susceptibility to cardiovascular risk factors or diseases is unlikely to substantially explain the development of inequalities in cardiovascular risk.
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Affiliation(s)
- Alice R Carter
- MRC Integrative Epidemiology Unit, University of Bristol Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Sean Harrison
- MRC Integrative Epidemiology Unit, University of Bristol Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Dipender Gill
- Clinical Pharmacology and Therapeutics Section, Institute of Medical and Biomedical Education and Institute for Infection and Immunity, St George’s, University of London, London, UK
- Clinical Pharmacology Group, Pharmacy and Medicines Directorate, St George’s University Hospitals NHS Foundation Trust, London, UK
- Novo Nordisk Research Centre Oxford, Old Road Campus, Oxford, UK
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, University of Bristol, Bristol, UK
| | - Amy E Taylor
- MRC Integrative Epidemiology Unit, University of Bristol Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, 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, Bristol, UK
| | - Neil M Davies
- MRC Integrative Epidemiology Unit, University of Bristol Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
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Dardani C, Riglin L, Leppert B, Sanderson E, Rai D, Howe LD, Davey Smith G, Tilling K, Thapar A, Davies NM, Anderson E, Stergiakouli E. Is genetic liability to ADHD and ASD causally linked to educational attainment? Int J Epidemiol 2022; 50:2011-2023. [PMID: 34999873 PMCID: PMC8743131 DOI: 10.1093/ije/dyab107] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 05/09/2021] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND The association patterns of attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) with educational attainment (EA) are complex; children with ADHD and ASD are at risk of poor academic outcomes, and parental EA has been associated with risk of ADHD/ASD in the offspring. Little is known on the causal links between ADHD, ASD, EA and the potential contribution of cognitive ability. METHODS Using the latest genome-wide association studies (GWAS) summary data on ADHD, ASD and EA, we applied two-sample Mendelian randomization (MR) to assess the effects of genetic liability to ADHD and ASD on EA. Reverse direction analyses were additionally performed. Multivariable MR was performed to estimate any effects independent of cognitive ability. RESULTS Genetic liability to ADHD had a negative effect on EA, independently of cognitive ability (MVMRIVW: -1.7 months of education per doubling of genetic liability to ADHD; 95% CI: -2.8 to -0.7), whereas genetic liability to ASD a positive effect (MVMRIVW: 30 days per doubling of the genetic liability to ASD; 95% CI: 2 to 53). Reverse direction analyses suggested that genetic liability to higher EA had an effect on lower risk of ADHD, independently of cognitive ability (MVMRIVWOR: 0.33 per SD increase; 95% CI: 0.26 to 0.43) and increased risk of ASD (MRIVWOR: 1.51 per SD increase; 95% CI: 1.29 to 1.77), which was partly explained by cognitive ability (MVMRIVWOR per SD increase: 1.24; 95%CI: 0.96 to 1.60). CONCLUSIONS Genetic liability to ADHD and ASD is likely to affect educational attainment, independently of underlying cognitive ability.
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Affiliation(s)
- Christina Dardani
- Centre of Academic Mental Health, Bristol Medical School, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Lucy Riglin
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - Beate Leppert
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Eleanor Sanderson
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Dheeraj Rai
- Centre of Academic Mental Health, Bristol Medical School, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Laura D Howe
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Kate Tilling
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Anita Thapar
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - Neil M Davies
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
| | - Emma Anderson
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Evie Stergiakouli
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
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Hughes AM, Sanderson E, Morris T, Ayorech Z, Tesli M, Ask H, Reichborn-Kjennerud T, Andreassen OA, Magnus P, Helgeland Ø, Johansson S, Njølstad P, Davey Smith G, Havdahl A, Howe LD, Davies NM. Body mass index and childhood symptoms of depression, anxiety, and attention-deficit hyperactivity disorder: A within-family Mendelian randomization study. eLife 2022; 11:74320. [PMID: 36537070 PMCID: PMC9767454 DOI: 10.7554/elife.74320] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 11/07/2022] [Indexed: 12/24/2022] Open
Abstract
Background Higher BMI in childhood is associated with emotional and behavioural problems, but these associations may not be causal. Results of previous genetic studies imply causal effects but may reflect influence of demography and the family environment. Methods This study used data on 40,949 8-year-old children and their parents from the Norwegian Mother, Father and Child Cohort Study (MoBa) and Medical Birth Registry of Norway (MBRN). We investigated the impact of BMI on symptoms of depression, anxiety, and attention-deficit hyperactivity disorder (ADHD) at age 8. We applied within-family Mendelian randomization, which accounts for familial effects by controlling for parental genotype. Results Within-family Mendelian randomization estimates using genetic variants associated with BMI in adults suggested that a child's own BMI increased their depressive symptoms (per 5 kg/m2 increase in BMI, beta = 0.26 S.D., CI = -0.01,0.52, p=0.06) and ADHD symptoms (beta = 0.38 S.D., CI = 0.09,0.63, p=0.009). These estimates also suggested maternal BMI, or related factors, may independently affect a child's depressive symptoms (per 5 kg/m2 increase in maternal BMI, beta = 0.11 S.D., CI:0.02,0.09, p=0.01). However, within-family Mendelian randomization using genetic variants associated with retrospectively-reported childhood body size did not support an impact of BMI on these outcomes. There was little evidence from any estimate that the parents' BMI affected the child's ADHD symptoms, or that the child's or parents' BMI affected the child's anxiety symptoms. Conclusions We found inconsistent evidence that a child's BMI affected their depressive and ADHD symptoms, and little evidence that a child's BMI affected their anxiety symptoms. There was limited evidence of an influence of parents' BMI. Genetic studies in samples of unrelated individuals, or using genetic variants associated with adult BMI, may have overestimated the causal effects of a child's own BMI. Funding This research was funded by the Health Foundation. It is part of the HARVEST collaboration, supported by the Research Council of Norway. Individual co-author funding: the European Research Council, the South-Eastern Norway Regional Health Authority, the Research Council of Norway, Helse Vest, the Novo Nordisk Foundation, the University of Bergen, the South-Eastern Norway Regional Health Authority, the Trond Mohn Foundation, the Western Norway Regional Health Authority, the Norwegian Diabetes Association, the UK Medical Research Council. The Medical Research Council (MRC) and the University of Bristol support the MRC Integrative Epidemiology Unit.
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Affiliation(s)
- Amanda M Hughes
- Medical Research Council Integrative Epidemiology Unit, University of BristolBristolUnited Kingdom,Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield GroveBristolUnited Kingdom
| | - Eleanor Sanderson
- Medical Research Council Integrative Epidemiology Unit, University of BristolBristolUnited Kingdom,Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield GroveBristolUnited Kingdom
| | - Tim Morris
- Medical Research Council Integrative Epidemiology Unit, University of BristolBristolUnited Kingdom,Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield GroveBristolUnited Kingdom
| | - Ziada Ayorech
- PROMENTA Research Centre, Department of Psychology, University of OsloOsloNorway,Nic Waals Institute, Lovisenberg Diaconal HospitalOsloNorway
| | - Martin Tesli
- Department of Mental Disorders, Norwegian Institute of Public HealthOsloNorway,Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University HospitalOsloNorway
| | - Helga Ask
- PROMENTA Research Centre, Department of Psychology, University of OsloOsloNorway,Department of Mental Disorders, Norwegian Institute of Public HealthOsloNorway
| | - Ted Reichborn-Kjennerud
- Department of Mental Disorders, Norwegian Institute of Public HealthOsloNorway,Institute of Clinical Medicine, University of OsloOsloNorway
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University HospitalOsloNorway,Institute of Clinical Medicine, University of OsloOsloNorway
| | - Per Magnus
- Centre for Fertility and Health, Norwegian Institute of Public HealthOsloNorway
| | - Øyvind Helgeland
- Center for Diabetes Research, Department of Clinical Science, University of BergenBergenNorway
| | - Stefan Johansson
- Department of Clinical Science, University of BergenBergenNorway,Department of Medical Genetics, Haukeland University HospitalBergenNorway
| | - Pål Njølstad
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of BergenBergenNorway,Children and Youth Clinic, Haukeland University HospitalBergenNorway
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, University of BristolBristolUnited Kingdom,Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield GroveBristolUnited Kingdom
| | - Alexandra Havdahl
- Medical Research Council Integrative Epidemiology Unit, University of BristolBristolUnited Kingdom,Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield GroveBristolUnited Kingdom,PROMENTA Research Centre, Department of Psychology, University of OsloOsloNorway,Nic Waals Institute, Lovisenberg Diaconal HospitalOsloNorway,Department of Mental Disorders, Norwegian Institute of Public HealthOsloNorway
| | - Laura D Howe
- Medical Research Council Integrative Epidemiology Unit, University of BristolBristolUnited Kingdom,Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield GroveBristolUnited Kingdom
| | - Neil M Davies
- Medical Research Council Integrative Epidemiology Unit, University of BristolBristolUnited Kingdom,Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield GroveBristolUnited Kingdom,K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and TechnologyHøgskoleringenNorway
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Sadoo AB, Davies NM, Kehoe PG, Martin RM, Walker VM. Investigating the relationship of drugs prescribed to treat hypertension and hypercholesterolemia with disease progression in Alzheimer’s disease and related dementias. Alzheimers Dement 2021. [DOI: 10.1002/alz.054081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Howe LJ, Battram T, Morris TT, Hartwig FP, Hemani G, Davies NM, Smith GD. Assortative mating and within-spouse pair comparisons. PLoS Genet 2021; 17:e1009883. [PMID: 34735433 PMCID: PMC8594845 DOI: 10.1371/journal.pgen.1009883] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 11/16/2021] [Accepted: 10/15/2021] [Indexed: 12/20/2022] Open
Abstract
Spousal comparisons have been proposed as a design that can both reduce confounding and estimate effects of the shared adulthood environment. However, assortative mating, the process by which individuals select phenotypically (dis)similar mates, could distort associations when comparing spouses. We evaluated the use of spousal comparisons, as in the within-spouse pair (WSP) model, for aetiological research such as genetic association studies. We demonstrated that the WSP model can reduce confounding but may be susceptible to collider bias arising from conditioning on assorted spouse pairs. Analyses using UK Biobank spouse pairs found that WSP genetic association estimates were smaller than estimates from random pairs for height, educational attainment, and BMI variants. Within-sibling pair estimates, robust to demographic and parental effects, were also smaller than random pair estimates for height and educational attainment, but not for BMI. WSP models, like other within-family models, may reduce confounding from demographic factors in genetic association estimates, and so could be useful for triangulating evidence across study designs to assess the robustness of findings. However, WSP estimates should be interpreted with caution due to potential collider bias.
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Affiliation(s)
- Laurence J. Howe
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Thomas Battram
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Tim T. Morris
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Fernando P. Hartwig
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil
| | - Gibran Hemani
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Neil M. Davies
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
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35
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Skrivankova VW, Richmond RC, Woolf BAR, Davies NM, Swanson SA, VanderWeele TJ, Timpson NJ, Higgins JPT, Dimou N, Langenberg C, Loder EW, Golub RM, Egger M, Davey Smith G, Richards JB. Strengthening the reporting of observational studies in epidemiology using mendelian randomisation (STROBE-MR): explanation and elaboration. BMJ 2021; 375:n2233. [PMID: 34702754 PMCID: PMC8546498 DOI: 10.1136/bmj.n2233] [Citation(s) in RCA: 331] [Impact Index Per Article: 110.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/02/2021] [Indexed: 12/15/2022]
Affiliation(s)
| | - Rebecca C Richmond
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Benjamin A R Woolf
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Department of Psychological Science, University of Bristol, Bristol, UK
| | - Neil M Davies
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- K G Jebsen Centre for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Sonja A Swanson
- Department of Epidemiology, Erasmus MC, Rotterdam, Netherlands
| | - Tyler J VanderWeele
- Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Nicholas J Timpson
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Julian P T Higgins
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
| | - Niki Dimou
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Claudia Langenberg
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | | | - Robert M Golub
- JAMA, Chicago, IL, USA
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Matthias Egger
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Centre for Infectious Disease Epidemiology and Research, University of Cape Town, Cape Town, South Africa
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
| | - J Brent Richards
- Departments of Medicine, Human Genetics, Epidemiology & Biostatistics, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, QC, Canada
- Department of Twin Research and Genetic Epidemiology, King's College London, University of London, London, UK
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36
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Skrivankova VW, Richmond RC, Woolf BAR, Yarmolinsky J, Davies NM, Swanson SA, VanderWeele TJ, Higgins JPT, Timpson NJ, Dimou N, Langenberg C, Golub RM, Loder EW, Gallo V, Tybjaerg-Hansen A, Davey Smith G, Egger M, Richards JB. Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement. JAMA 2021; 326:1614-1621. [PMID: 34698778 DOI: 10.1001/jama.2021.18236] [Citation(s) in RCA: 706] [Impact Index Per Article: 235.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Importance Mendelian randomization (MR) studies use genetic variation associated with modifiable exposures to assess their possible causal relationship with outcomes and aim to reduce potential bias from confounding and reverse causation. Objective To develop the STROBE-MR Statement as a stand-alone extension to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guideline for the reporting of MR studies. Design, Setting, and Participants The development of the STROBE-MR Statement followed the Enhancing the Quality and Transparency of Health Research (EQUATOR) framework guidance and used the STROBE Statement as a starting point to draft a checklist tailored to MR studies. The project was initiated in 2018 by reviewing the literature on the reporting of instrumental variable and MR studies. A group of 17 experts, including MR methodologists, MR study design users, developers of previous reporting guidelines, and journal editors, participated in a workshop in May 2019 to define the scope of the Statement and draft the checklist. The draft checklist was published as a preprint in July 2019 and discussed on the preprint platform, in social media, and at the 4th Mendelian Randomization Conference. The checklist was then revised based on comments, further refined through 2020, and finalized in July 2021. Findings The STROBE-MR checklist is organized into 6 sections (Title and Abstract, Introduction, Methods, Results, Discussion, and Other Information) and includes 20 main items and 30 subitems. It covers both 1-sample and 2-sample MR studies that assess 1 or multiple exposures and outcomes, and addresses MR studies that follow a genome-wide association study and are reported in the same article. The checklist asks authors to justify why MR is a helpful method to address the study question and state prespecified causal hypotheses. The measurement, quality, and selection of genetic variants must be described and attempts to assess validity of MR-specific assumptions should be well reported. An item on data sharing includes reporting when the data and statistical code required to replicate the analyses can be accessed. Conclusions and Relevance STROBE-MR provides guidelines for reporting MR studies. Improved reporting of these studies could facilitate their evaluation by editors, peer reviewers, researchers, clinicians, and other readers, and enhance the interpretation of their results.
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Affiliation(s)
| | - 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
| | - Benjamin A R Woolf
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Department of Psychological Science, University of Bristol, Bristol, United Kingdom
| | - James Yarmolinsky
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Neil M Davies
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Norway
| | - Sonja A Swanson
- Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands
| | - Tyler J VanderWeele
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Julian P T Higgins
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- NIHR Bristol Biomedical Research Centre, Bristol, United Kingdom
| | - Nicholas J Timpson
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Niki Dimou
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Robert M Golub
- JAMA , Chicago, Illinois
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Elizabeth W Loder
- Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
- BMJ , London, United Kingdom
| | - Valentina Gallo
- Campus Fryslân, University of Groningen, Leeuwarden, the Netherlands
- Institute of Population Health Sciences, Queen Mary, University of London, London, United Kingdom
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Anne Tybjaerg-Hansen
- Section for Molecular Genetics, Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- NIHR Bristol Biomedical Research Centre, Bristol, United Kingdom
| | - Matthias Egger
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- Centre for Infectious Disease Epidemiology and Research, University of Cape Town, Cape Town, South Africa
| | - J Brent Richards
- Departments of Medicine, Human Genetics, Epidemiology, & Biostatistics, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Quebec, Canada
- Department of Twin Research and Genetic Epidemiology, King's College London, University of London, London, United Kingdom
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Budu-Aggrey A, Joyce S, Davies NM, Paternoster L, Munafò MR, Brown SJ, Evans J, Sallis HM. Investigating the causal relationship between allergic disease and mental health. Clin Exp Allergy 2021; 51:1449-1458. [PMID: 34611950 DOI: 10.1111/cea.14010] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 08/06/2021] [Indexed: 11/28/2022]
Abstract
BACKGROUND Observational studies have reported an association between allergic disease and mental health, but a causal relationship has not been established. Here, we use Mendelian randomization (MR) to investigate a possible causal relationship between atopic disease and mental health phenotypes. METHODS The observational relationship between allergic disease and mental health was investigated in UK Biobank. The direction of causality was investigated with bidirectional two-sample MR using summary-level data from published genome-wide association studies. A genetic instrument was derived from associated variants for a broad allergic disease phenotype to test for causal relationships with various mental health outcomes. We also investigated whether these relationships were specific to atopic dermatitis (AD), asthma or hayfever. Given the multiple testing burden, we applied a Bonferroni correction to use an individual test p-value threshold of .0016 (32 tests). RESULTS We found strong evidence of an observational association between the broad allergic disease phenotype and depression (ORself-report =1.45, 95% CI: 1.41-1.50, p = 3.6 × 10-130 ), anxiety (OR=1.25, 95% CI: 1.18-1.33, p = 6.5 × 10-13 ), bipolar disorder (ORself-report =1.29, 95% CI: 1.12-1.47, p = 2.8 × 10-4 ) and neuroticism (β = 0.38, 95% CI: 0.36-0.41, p = 6.8 × 10-166 ). Similar associations were found between asthma, AD, hayfever individually with the mental health phenotypes, although the associations between AD and hayfever with bipolar disorder were weaker. There was little evidence of causality in either direction (all p-values>.02). CONCLUSION Using MR, we were unable to replicate most of the phenotypic associations between allergic disease and mental health. Any causal effects we detected were considerably attenuated compared with the phenotypic association. This suggests that most comorbidity observed clinically is unlikely to be causal.
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Affiliation(s)
- Ashley Budu-Aggrey
- Medical Research Council (MRC) Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.,Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
| | - Sally Joyce
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
| | - Neil M Davies
- Medical Research Council (MRC) Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.,Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK.,K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Lavinia Paternoster
- Medical Research Council (MRC) Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.,Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
| | - Marcus R Munafò
- Medical Research Council (MRC) Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.,School of Psychological Science, University of Bristol, Bristol, UK
| | - Sara J Brown
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, The University of Edinburgh, Edinburgh, UK
| | - Jonathan Evans
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK.,Centre for Academic Mental Health, Population Health Sciences, University of Bristol, Bristol, UK
| | - Hannah M Sallis
- Medical Research Council (MRC) Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.,Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK.,School of Psychological Science, University of Bristol, Bristol, UK.,Centre for Academic Mental Health, Population Health Sciences, University of Bristol, Bristol, UK
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38
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Howe LJ, Tudball M, Davey Smith G, Davies NM. Interpreting Mendelian-randomization estimates of the effects of categorical exposures such as disease status and educational attainment. Int J Epidemiol 2021; 51:948-957. [PMID: 34570226 PMCID: PMC9189950 DOI: 10.1093/ije/dyab208] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/16/2021] [Indexed: 11/29/2022] Open
Abstract
Background Mendelian randomization has been previously used to estimate the effects of binary and ordinal categorical exposures—e.g. Type 2 diabetes or educational attainment defined by qualification—on outcomes. Binary and categorical phenotypes can be modelled in terms of liability—an underlying latent continuous variable with liability thresholds separating individuals into categories. Genetic variants influence an individual’s categorical exposure via their effects on liability, thus Mendelian-randomization analyses with categorical exposures will capture effects of liability that act independently of exposure category. Methods and results We discuss how groups in which the categorical exposure is invariant can be used to detect liability effects acting independently of exposure category. For example, associations between an adult educational-attainment polygenic score (PGS) and body mass index measured before the minimum school leaving age (e.g. age 10 years), cannot indicate the effects of years in full-time education on this outcome. Using UK Biobank data, we show that a higher educational-attainment PGS is strongly associated with lower smoking initiation and higher odds of glasses use at age 15 years. These associations were replicated in sibling models. An orthogonal approach using the raising of the school leaving age (ROSLA) policy change found that individuals who chose to remain in education to age 16 years before the reform likely had higher liability to educational attainment than those who were compelled to remain in education to age 16 years after the reform, and had higher income, lower pack-years of smoking, higher odds of glasses use and lower deprivation in adulthood. These results suggest that liability to educational attainment is associated with health and social outcomes independently of years in full-time education. Conclusions Mendelian-randomization studies with non-continuous exposures should be interpreted in terms of liability, which may affect the outcome via changes in exposure category and/or independently.
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Affiliation(s)
- Laurence J Howe
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.,Population Health Sciences, University of Bristol, Bristol, UK
| | - Matthew Tudball
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.,Population Health Sciences, University of Bristol, Bristol, UK
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.,Population Health Sciences, University of Bristol, Bristol, UK
| | - Neil M Davies
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.,Population Health Sciences, University of Bristol, Bristol, UK.,K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
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Harrison S, Dixon P, Jones HE, Davies AR, Howe LD, Davies NM. Long-term cost-effectiveness of interventions for obesity: A mendelian randomisation study. PLoS Med 2021; 18:e1003725. [PMID: 34449774 PMCID: PMC8437285 DOI: 10.1371/journal.pmed.1003725] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 09/13/2021] [Accepted: 07/09/2021] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND The prevalence of obesity has increased in the United Kingdom, and reliably measuring the impact on quality of life and the total healthcare cost from obesity is key to informing the cost-effectiveness of interventions that target obesity, and determining healthcare funding. Current methods for estimating cost-effectiveness of interventions for obesity may be subject to confounding and reverse causation. The aim of this study is to apply a new approach using mendelian randomisation for estimating the cost-effectiveness of interventions that target body mass index (BMI), which may be less affected by confounding and reverse causation than previous approaches. METHODS AND FINDINGS We estimated health-related quality-adjusted life years (QALYs) and both primary and secondary healthcare costs for 310,913 men and women of white British ancestry aged between 39 and 72 years in UK Biobank between recruitment (2006 to 2010) and 31 March 2017. We then estimated the causal effect of differences in BMI on QALYs and total healthcare costs using mendelian randomisation. For this, we used instrumental variable regression with a polygenic risk score (PRS) for BMI, derived using a genome-wide association study (GWAS) of BMI, with age, sex, recruitment centre, and 40 genetic principal components as covariables to estimate the effect of a unit increase in BMI on QALYs and total healthcare costs. Finally, we used simulations to estimate the likely effect on BMI of policy relevant interventions for BMI, then used the mendelian randomisation estimates to estimate the cost-effectiveness of these interventions. A unit increase in BMI decreased QALYs by 0.65% of a QALY (95% confidence interval [CI]: 0.49% to 0.81%) per year and increased annual total healthcare costs by £42.23 (95% CI: £32.95 to £51.51) per person. When considering only health conditions usually considered in previous cost-effectiveness modelling studies (cancer, cardiovascular disease, cerebrovascular disease, and type 2 diabetes), we estimated that a unit increase in BMI decreased QALYs by only 0.16% of a QALY (95% CI: 0.10% to 0.22%) per year. We estimated that both laparoscopic bariatric surgery among individuals with BMI greater than 35 kg/m2, and restricting volume promotions for high fat, salt, and sugar products, would increase QALYs and decrease total healthcare costs, with net monetary benefits (at £20,000 per QALY) of £13,936 (95% CI: £8,112 to £20,658) per person over 20 years, and £546 million (95% CI: £435 million to £671 million) in total per year, respectively. The main limitations of this approach are that mendelian randomisation relies on assumptions that cannot be proven, including the absence of directional pleiotropy, and that genotypes are independent of confounders. CONCLUSIONS Mendelian randomisation can be used to estimate the impact of interventions on quality of life and healthcare costs. We observed that the effect of increasing BMI on health-related quality of life is much larger when accounting for 240 chronic health conditions, compared with only a limited selection. This means that previous cost-effectiveness studies have likely underestimated the effect of BMI on quality of life and, therefore, the potential cost-effectiveness of interventions to reduce BMI.
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Affiliation(s)
- Sean Harrison
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- * E-mail:
| | - Padraig Dixon
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Hayley E. Jones
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Alisha R. Davies
- Research and Evaluation Division, Public Health Wales NHS Trust, Cardiff, United Kingdom
| | - Laura D. Howe
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Neil M. Davies
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
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40
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Davies NM, Korologou-Linden R, Anderson EL. BMI is unlikely to be a plausible intervention target for reducing the incidence of dementia. Int J Epidemiol 2021; 50:1040-1041. [PMID: 33846722 DOI: 10.1093/ije/dyab062] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/08/2021] [Indexed: 11/14/2022] Open
Affiliation(s)
- Neil M Davies
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.,Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.,K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Norway
| | - Roxanna Korologou-Linden
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.,Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Emma L Anderson
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.,Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
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41
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Carter AR, Gill D, Davey Smith G, Taylor AE, Davies NM, Howe LD. Cross-sectional analysis of educational inequalities in primary prevention statin use in UK Biobank. Heart 2021; 108:536-542. [PMID: 34315717 PMCID: PMC8921562 DOI: 10.1136/heartjnl-2021-319238] [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: 02/23/2021] [Accepted: 06/29/2021] [Indexed: 11/25/2022] Open
Abstract
Objective Identify whether participants with lower education are less likely to report taking statins for primary cardiovascular prevention than those with higher education, but an equivalent increase in underlying cardiovascular risk. Methods Using data from a large prospective cohort study, UK Biobank, we calculated a QRISK3 cardiovascular risk score for 472 097 eligible participants with complete data on self-reported educational attainment and statin use (55% female participants; mean age 56 years). We used logistic regression to explore the association between (i) QRISK3 score and (ii) educational attainment on self-reported statin use. We then stratified the association between QRISK3 score and statin use, by educational attainment to test for interactions. Results There was evidence of an interaction between QRISK3 score and educational attainment. Per unit increase in QRISK3 score, more educated individuals were more likely to report taking statins. In women with ≤7 years of schooling, a one unit increase in QRISK3 score was associated with a 7% higher odds of statin use (OR 1.07, 95% CI 1.07 to 1.07). In women with ≥20 years of schooling, a one unit increase in QRISK3 score was associated with an 14% higher odds of statin use (OR 1.14, 95% CI 1.14 to 1.15). Comparable ORs in men were 1.04 (95% CI 1.04 to 1.05) for ≤7 years of schooling and 1.08 (95% CI 1.08, 1.08) for ≥20 years of schooling. Conclusion Per unit increase in QRISK3 score, individuals with lower educational attainment were less likely to report using statins, likely contributing to health inequalities.
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Affiliation(s)
- Alice Rose Carter
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK .,Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.,Clinical Pharmacology and Therapeutics Section, Institute of Medical and Biomedical Education and Institute for Infection and Immunity, St George's University Hospitals NHS Foundation Trust, London, UK.,Novo Nordisk Research Centre Oxford, Old Road Campus, Oxford, UK.,Clinical Pharmacology and Therapeutics Section, Institute of Medical and Biomedical Education and Institute for Infection and Immunity, St George's, University of London, London, UK.,Clinical Pharmacology Group, Pharmacy and Medicines Directorate, St George's University Hospitals NHS Foundation Trust, London, UK
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.,Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.,NIHR Bristol Biomedical Research Centre, Bristol, UK
| | - Amy E Taylor
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.,NIHR Bristol Biomedical Research Centre, Bristol, UK
| | - Neil M Davies
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.,Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.,K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Laura D Howe
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.,Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
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42
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Harrison S, Davies NM, Howe LD, Hughes A. Testosterone and socioeconomic position: Mendelian randomization in 306,248 men and women in UK Biobank. Sci Adv 2021; 7:7/31/eabf8257. [PMID: 34321204 PMCID: PMC8318368 DOI: 10.1126/sciadv.abf8257] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 06/10/2021] [Indexed: 05/02/2023]
Abstract
Men with more advantaged socioeconomic position (SEP) have been observed to have higher levels of testosterone. It is unclear whether these associations arise because testosterone has a causal impact on SEP. In 306,248 participants of UK Biobank, we performed sex-stratified genome-wide association analysis to identify genetic variants associated with testosterone. Using the identified variants, we performed Mendelian randomization analysis of the influence of testosterone on socioeconomic position, including income, employment status, neighborhood-level deprivation, and educational qualifications; on health, including self-rated health and body mass index; and on risk-taking behavior. We found little evidence that testosterone affected socioeconomic position, health, or risk-taking. Our results therefore suggest that it is unlikely that testosterone meaningfully affects these outcomes in men or women. Differences between Mendelian randomization and multivariable-adjusted estimates suggest that previously reported associations with socioeconomic position and health may be due to residual confounding or reverse causation.
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Affiliation(s)
- Sean Harrison
- 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
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Norway
| | - Laura D Howe
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Amanda Hughes
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
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43
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Morris TT, Dorling D, Davies NM, Davey Smith G. Associations between school enjoyment at age 6 and later educational achievement: evidence from a UK cohort study. NPJ Sci Learn 2021; 6:18. [PMID: 34131153 PMCID: PMC8206254 DOI: 10.1038/s41539-021-00092-w] [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] [Received: 08/19/2020] [Accepted: 04/24/2021] [Indexed: 06/12/2023]
Abstract
Education is influenced by a broad range of factors but there has been limited research into the role that early school enjoyment plays in pupil's educational achievement. Here we used data from a UK cohort to answer three research questions. What is the association between early school enjoyment and later academic achievement? To what extent do family background factors underlie this association? Do sex differences in school enjoyment underlie sex differences in achievement? School enjoyment was self-reported in two questionnaires completed at age 6. We used multiple imputation to account for missing covariates in this study, giving an imputed sample size of 12,135. Children's school enjoyment at age 6 associated with sex and cognitive ability but not family socioeconomic background. For example, girls were twice as likely to report enjoying school than boys (OR: 1.97; 95% CI: 1.56, 2.48). School enjoyment strongly associated with later achievement in age 16 compulsory GCSE exams even after adjustment for socioeconomic background and cognitive ability; pupils who reported enjoying school scored on average 14.4 (95% CI: 6.9, 21.9) more points (equivalent to almost a 3-grade increase across all subjects) and were 29% more likely to obtain 5 + A*-C GCSE's including Maths and English (OR: 1.29; 95% CI: 0.99, 1.7) than those who did not enjoy school. These results highlight the importance of school enjoyment for educational achievement. As a potentially more modifiable factor than socioeconomic background, cognitive ability or sex, school enjoyment may represent a promising intervention target for improving educational outcomes.
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Affiliation(s)
- Tim T Morris
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Danny Dorling
- School of Geography and the Environment, University of Oxford, Oxford, UK
| | - Neil M Davies
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - George Davey Smith
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
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44
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Holmes MV, Richardson TG, Ference BA, Davies NM, Davey Smith G. Integrating genomics with biomarkers and therapeutic targets to invigorate cardiovascular drug development. Nat Rev Cardiol 2021; 18:435-453. [PMID: 33707768 DOI: 10.1038/s41569-020-00493-1] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/07/2020] [Indexed: 01/30/2023]
Abstract
Drug development in cardiovascular disease is stagnating, with lack of efficacy and adverse effects being barriers to innovation. Human genetics can provide compelling evidence of causation through approaches such as Mendelian randomization, with genetic support for causation increasing the probability of a clinical trial succeeding. Mendelian randomization applied to quantitative traits can identify risk factors for disease that are both causal and amenable to therapeutic modification. However, important differences exist between genetic investigations of a biomarker (such as HDL cholesterol) and a drug target aimed at modifying the same biomarker of interest (such as cholesteryl ester transfer protein), with implications for the methodology, interpretation and application of Mendelian randomization to drug development. Differences include the comparative nature of the genetic architecture - that is, biomarkers are typically polygenic, whereas protein drug targets are influenced by either cis-acting or trans-acting genetic variants - and the potential for drug targets to show disease associations that might differ from those of the biomarker that they are intended to modify (target-mediated pleiotropy). In this Review, we compare and contrast the use of Mendelian randomization to evaluate potential drug targets versus quantitative traits. We explain how genetic epidemiological studies can be used to assess the aetiological roles of biomarkers in disease and to prioritize drug targets, including designing their evaluation in clinical trials.
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Affiliation(s)
- Michael V Holmes
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK.
| | - Tom G Richardson
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Brian A Ference
- Centre for Naturally Randomised Trials, University of Cambridge, Cambridge, UK
| | - Neil M Davies
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
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Levin MG, Klarin D, Walker VM, Gill D, Lynch J, Hellwege JN, Keaton JM, Lee KM, Assimes TL, Natarajan P, Hung AM, Edwards T, Rader DJ, Gaziano JM, Davies NM, Tsao PS, Chang KM, Voight BF, Damrauer SM. Association Between Genetic Variation in Blood Pressure and Increased Lifetime Risk of Peripheral Artery Disease. Arterioscler Thromb Vasc Biol 2021; 41:2027-2034. [PMID: 33853351 PMCID: PMC8159880 DOI: 10.1161/atvbaha.120.315482] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [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] [Indexed: 12/24/2022]
Abstract
[Figure: see text].
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Affiliation(s)
- Michael G. Levin
- Division of Cardiovascular Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA
| | - Derek Klarin
- Malcolm Randall VA Medical Center, Gainesville, FL
- Department of Surgery, University of Florida, Gainesville, FL
| | - Venexia M. Walker
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- Centre for Pharmacology & Therapeutics, Department of Medicine, Hammersmith Campus, Imperial College London, London, United Kingdom
- Novo Nordisk Research Centre Oxford, Old Road Campus, Oxford, United Kingdom
- Clinical Pharmacology and Therapeutics Section, Institute of Medical and Biomedical Education and Institute for Infection and Immunity, St George’s, University of London, London, United Kingdom
- Clinical Pharmacology Group, Pharmacy and Medicines Directorate, St George’s University Hospitals NHS Foundation Trust, London, United Kingdom
| | - Julie Lynch
- Edith Nourse VA Medical Center, Bedford, MA
- VA Informatics and Computing Infrastructure, Department of Veterans Affairs, Salt Lake City Health Care System, Salt Lake City, CT, USA
| | - Jacklyn N. Hellwege
- Division of Genetic Medicine, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN
- Biomedical Laboratory Research and Development, Tennessee Valley Healthcare System (626)/Vanderbilt University, Nashville, TN
| | - Jacob M. Keaton
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Kyung M. Lee
- VA Informatics and Computing Infrastructure, Department of Veterans Affairs, Salt Lake City Health Care System, Salt Lake City, CT, USA
- Center for Healthcare Organization and Implementation Research, Edith Nourse Rogers Memorial VA Hospital, Bedford, MA, USA
- Department of Health Law, Policy & Management, Boston University School of Public Health, Boston, MA
| | - Themistocles L. Assimes
- Palo Alto VA Healthcare System, Palo Alto, CA
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA
| | - Pradeep Natarajan
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA
- Broad Institute of Harvard and MIT, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- VA Boston Healthcare System, Boston, MA
| | - Adriana M. Hung
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Todd Edwards
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Vanderbilt University, Nashville, TN
| | - Daniel J. Rader
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - J. Michael Gaziano
- VA Boston Healthcare System, Boston, MA
- Division of Aging, Department of Preventive Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | - Neil M. Davies
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Norway
| | - Philip S. Tsao
- Palo Alto VA Healthcare System, Palo Alto, CA
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA
| | - Kyong-Mi Chang
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA
| | - Benjamin F. Voight
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Scott M. Damrauer
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
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Thomas KH, Davies NM, Taylor AE, Taylor GMJ, Gunnell D, Martin RM, Douglas I. Risk of neuropsychiatric and cardiovascular adverse events following treatment with varenicline and nicotine replacement therapy in the UK Clinical Practice Research Datalink: a case-cross-over study. Addiction 2021; 116:1532-1545. [PMID: 33197082 PMCID: PMC8246946 DOI: 10.1111/add.15338] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [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: 10/05/2019] [Revised: 12/30/2019] [Accepted: 11/09/2020] [Indexed: 01/09/2023]
Abstract
BACKGROUND AND AIMS Varenicline and nicotine replacement therapy (NRT) are the most commonly used medications to quit smoking. Given their widespread use, monitoring adverse risks remains important. This study aimed to estimate the neuropsychiatric and cardiovascular risks associated with varenicline and NRT as used in routine UK care. DESIGN Case-cross-over study. SETTING UK-based electronic primary care records in the Clinical Practice Research Datalink from 2006 to 2015 linked to hospital and mortality data sets. PARTICIPANTS Adult smokers (n =282,429) observed during periods when exposed and not exposed to either varenicline or NRT. MEASUREMENTS Main outcomes included suicide, self-harm, myocardial infarction (MI), all-cause death and cause-specific death [MI, chronic obstructive pulmonary disease (COPD)]. In primary analyses, conditional logistic regression was used to compare the chance of varenicline or NRT exposure during the risk period (90 days prior to the event) with the chance of exposure during an earlier single reference period (91-180 days prior to the event) or multiple 90-day reference periods to increase statistical power. FINDINGS In the primary analyses, findings were inconclusive for the associations between varenicline and the main outcomes using a single reference period, while NRT was associated with MI [odds ratio (OR) = 1.40, 95% confidence interval (CI) = 1.18-1.67]. Using multiple reference periods, varenicline was associated with an increased risk of self-harm (OR = 1.32, 95% CI = 1.12-1.56) and suicide (OR = 3.56, 95% CI = 1.32-9.60) but a reduction in all-cause death (OR = 0.75, 95% CI = 0.61-0.93). NRT was associated with MI (OR = 1.54, 95% CI = 1.36-1.74), self-harm (OR = 1.30, 95% CI = 1.18-1.44) and deaths from MI (OR = 1.53, 95% CI = 1.11-2.10), COPD (OR = 1.33, 95% CI = 1.14-1.56) and all causes (OR = 1.28, 95% CI = 1.18-1.40) when using multiple reference periods. CONCLUSIONS There appear to be positive associations between (1) nicotine replacement therapy (NRT) and myocardial infarction, death and risk of self-harm and (2) varenicline and increased risk of self-harm and suicide, as well as a negative association between varenicline and all-cause death. The associations may not be causal. They may reflect health changes at the time of smoking cessation (nicotine replacement therapy is prescribed for people with cardiac problems) or be associated with quit attempts (exposure to both medicines was associated with self-harm).
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Affiliation(s)
- Kyla H. Thomas
- Bristol Medical School, Population Health SciencesUniversity of BristolBristolUK
| | - Neil M. Davies
- Bristol Medical School, Population Health SciencesUniversity of BristolBristolUK,Medical Research Council Integrative Epidemiology UnitUniversity of BristolBristolUK,K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNUNorwegian University of Science and TechnologyNorway
| | - Amy E. Taylor
- Bristol Medical School, Population Health SciencesUniversity of BristolBristolUK,National Institute for Health Research, Bristol Biomedical Research CentreUniversity Hospitals Bristol NHS Foundation Trust and University of BristolBristolUK
| | - Gemma M. J. Taylor
- Addiction and Mental Health Group (AIM), Department of PsychologyUniversity of BathBathUK
| | - David Gunnell
- Bristol Medical School, Population Health SciencesUniversity of BristolBristolUK,National Institute for Health Research, Bristol Biomedical Research CentreUniversity Hospitals Bristol NHS Foundation Trust and University of BristolBristolUK
| | - Richard M. Martin
- Bristol Medical School, Population Health SciencesUniversity of BristolBristolUK,Medical Research Council Integrative Epidemiology UnitUniversity of BristolBristolUK,National Institute for Health Research, Bristol Biomedical Research CentreUniversity Hospitals Bristol NHS Foundation Trust and University of BristolBristolUK
| | - Ian Douglas
- Department of Non‐communicable Disease Epidemiology, Faculty of Epidemiology and Population HealthLSHTMLondonUK
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47
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Liu CY, Schoeler T, Davies NM, Peyre H, Lim KX, Barker ED, Llewellyn C, Dudbridge F, Pingault JB. Are there causal relationships between attention-deficit/hyperactivity disorder and body mass index? Evidence from multiple genetically informed designs. Int J Epidemiol 2021; 50:496-509. [PMID: 33221865 DOI: 10.1093/ije/dyaa214] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/03/2020] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Attention-deficit/hyperactivity disorder (ADHD) and body mass index (BMI) are associated. However, it remains unclear whether this association reflects causal relationships in either direction or confounding. Here, we implemented genetically informed methods to examine bidirectional causality and potential confounding. METHODS Three genetically informed methods were employed: (i) cross-lagged twin-differences analyses to assess bidirectional effects of ADHD symptoms and BMI at ages 8, 12, 14 and 16 years in 2386 pairs of monozygotic twins from the Twins Early Development Study (TEDS); (ii) within- and between-family ADHD and BMI polygenic score (PS) analyses in 3320 pairs of dizygotic TEDS twins; and (iii) two-sample bidirectional Mendelian randomization (MR) using summary statistics from genome-wide association studies (GWAS) on ADHD (N = 55,374) and BMI (N = 806,834). RESULTS Mixed results were obtained across the three methods. Twin-difference analyses provided little support for cross-lagged associations between ADHD symptoms and BMI over time. PS analyses were consistent with bidirectional relationships between ADHD and BMI, with plausible time-varying effects from childhood to adolescence. MR findings also suggested bidirectional causal effects between ADHD and BMI. Multivariable MR indicated the presence of substantial confounding in bidirectional relationships. CONCLUSIONS The three methods converged to highlight multiple sources of confounding in the association between ADHD and BMI. PS and MR analyses suggested plausible causal relationships in both directions. Possible explanations for mixed causal findings across methods are discussed.
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Affiliation(s)
- Chao-Yu Liu
- Department of Clinical, Educational and Health Psychology, University College London, London, UK
| | - Tabea Schoeler
- Department of Clinical, Educational and Health Psychology, University College London, London, UK
| | - Neil M Davies
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK.,Department of Public Health and Nursing, K.G. Jebsen Center for Genetic Epidemiology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Hugo Peyre
- Laboratoire de Sciences Cognitives et Psycholinguistique, PSL University, Paris, France.,Child and Adolescent Psychiatry Department, Robert Debré Hospital, Paris, France
| | - Kai-Xiang Lim
- Social, Genetic & Developmental Psychiatry Centre, King's College London, London, UK
| | - Edward D Barker
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Clare Llewellyn
- Research Department of Behavioural Science and Health, University College London, London, UK
| | - Frank Dudbridge
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - Jean-Baptiste Pingault
- Department of Clinical, Educational and Health Psychology, University College London, London, UK.,Social, Genetic & Developmental Psychiatry Centre, King's College London, London, UK
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48
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Carter AR, Sanderson E, Hammerton G, Richmond RC, Davey Smith G, Heron J, Taylor AE, Davies NM, Howe LD. Mendelian randomisation for mediation analysis: current methods and challenges for implementation. Eur J Epidemiol 2021; 36:465-478. [PMID: 33961203 PMCID: PMC8159796 DOI: 10.1007/s10654-021-00757-1] [Citation(s) in RCA: 210] [Impact Index Per Article: 70.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 04/22/2021] [Indexed: 12/22/2022]
Abstract
Mediation analysis seeks to explain the pathway(s) through which an exposure affects an outcome. Traditional, non-instrumental variable methods for mediation analysis experience a number of methodological difficulties, including bias due to confounding between an exposure, mediator and outcome and measurement error. Mendelian randomisation (MR) can be used to improve causal inference for mediation analysis. We describe two approaches that can be used for estimating mediation analysis with MR: multivariable MR (MVMR) and two-step MR. We outline the approaches and provide code to demonstrate how they can be used in mediation analysis. We review issues that can affect analyses, including confounding, measurement error, weak instrument bias, interactions between exposures and mediators and analysis of multiple mediators. Description of the methods is supplemented by simulated and real data examples. Although MR relies on large sample sizes and strong assumptions, such as having strong instruments and no horizontally pleiotropic pathways, our simulations demonstrate that these methods are unaffected by confounders of the exposure or mediator and the outcome and non-differential measurement error of the exposure or mediator. Both MVMR and two-step MR can be implemented in both individual-level MR and summary data MR. MR mediation methods require different assumptions to be made, compared with non-instrumental variable mediation methods. Where these assumptions are more plausible, MR can be used to improve causal inference in mediation analysis.
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Affiliation(s)
- Alice R Carter
- 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.
| | - 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
| | - Gemma Hammerton
- 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
- Centre for Academic Mental Health, University of Bristol, Bristol, UK
| | - Rebecca C 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
| | - 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
- National Institute for Health Research Biomedical Research Centre At the University Hospitals Bristol NHS Foundation Trust and the University of Bristol, Bristol, 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
- Centre for Academic Mental Health, University of Bristol, Bristol, UK
| | - Amy E Taylor
- 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
- National Institute for Health Research Biomedical Research Centre At the University Hospitals Bristol NHS Foundation Trust and the University of Bristol, Bristol, UK
| | - Neil M Davies
- 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
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - 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
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49
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Walker VM, Kehoe PG, Martin RM, Davies NM. Repurposing antihypertensive drugs for the prevention of Alzheimer's disease: a Mendelian randomization study. Int J Epidemiol 2021; 49:1132-1140. [PMID: 31335937 PMCID: PMC7751008 DOI: 10.1093/ije/dyz155] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/04/2019] [Indexed: 02/02/2023] Open
Abstract
Background Evidence concerning the potential repurposing of antihypertensives for Alzheimer’s disease prevention is inconclusive. We used Mendelian randomization, which can be more robust to confounding by indication and patient characteristics, to investigate the effects of lowering systolic blood pressure, via the protein targets of different antihypertensive drug classes, on Alzheimer’s disease. Methods We used summary statistics from genome-wide association studies of systolic blood pressure and Alzheimer’s disease in a two-sample Mendelian randomization analysis. We identified single-nucleotide polymorphisms (SNPs) that mimic the action of antihypertensive protein targets and estimated the effect of lowering systolic blood pressure on Alzheimer’s disease in three ways: (i) combining the protein targets of antihypertensive drug classes, (ii) combining all protein targets and (iii) without consideration of the protein targets. Results There was limited evidence that lowering systolic blood pressure, via the protein targets of antihypertensive drug classes, affected Alzheimer’s disease risk. For example, the protein targets of calcium channel blockers had an odds ratio (OR) per 10 mmHg lower systolic blood pressure of 1.53 [95% confidence interval (CI): 0.94 to 2.49; p = 0.09; SNPs = 17]. We also found limited evidence for an effect when combining all protein targets (OR per 10 mmHg lower systolic blood pressure: 1.14; 95% CI: 0.83 to 1.56; p = 0.41; SNPs = 59) and without consideration of the protein targets (OR per 10 mmHg lower systolic blood pressure: 1.04; 95% CI: 0.95 to 1.13; p = 0.45; SNPs = 153). Conclusions Mendelian randomization suggests that lowering systolic blood pressure via the protein targets of antihypertensive drugs is unlikely to affect the risk of developing Alzheimer’s disease. Consequently, if specific antihypertensive drug classes do affect the risk of Alzheimer’s disease, they may not do so via systolic blood pressure.
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Affiliation(s)
- Venexia M Walker
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.,Bristol Medical School: Population Health Sciences, University of Bristol, Bristol, UK
| | - Patrick G Kehoe
- Dementia Research Group, University of Bristol, Bristol, UK.,Bristol Medical School: Translational Health Sciences, University of Bristol, Bristol, UK
| | - Richard M Martin
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.,Bristol Medical School: Population Health Sciences, University of Bristol, Bristol, UK
| | - Neil M Davies
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.,Bristol Medical School: Population Health Sciences, University of Bristol, Bristol, UK
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50
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Dardani C, Howe LJ, Mukhopadhyay N, Stergiakouli E, Wren Y, Humphries K, Davies A, Ho K, Weinberg SM, Marazita ML, Mangold E, Ludwig KU, Relton CL, Davey Smith G, Lewis SJ, Sandy J, Davies NM, Sharp GC. Cleft lip/palate and educational attainment: cause, consequence or correlation? A Mendelian randomization study. Int J Epidemiol 2021; 49:1282-1293. [PMID: 32373937 PMCID: PMC7660147 DOI: 10.1093/ije/dyaa047] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Accepted: 03/04/2020] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Previous studies have found that children born with a non-syndromic orofacial cleft have lower-than-average educational attainment. Differences could be due to a genetic predisposition to low intelligence and academic performance, factors arising due to the cleft phenotype (such as social stigmatization, impaired speech/language development) or confounding by the prenatal environment. A clearer understanding of this mechanism will inform interventions to improve educational attainment in individuals born with a cleft, which could substantially improve their quality of life. We assessed evidence for the hypothesis that common variant genetic liability to non-syndromic cleft lip with or without cleft palate (nsCL/P) influences educational attainment. METHODS We performed a genome-wide association study (GWAS) meta-analysis of nsCL/P with 1692 nsCL/P cases and 4259 parental and unrelated controls. Using GWAS summary statistics, we performed Linkage Disequilibrium (LD)-score regression to estimate the genetic correlation between nsCL/P, educational attainment (GWAS n = 766 345) and intelligence (GWAS n = 257 828). We used two-sample Mendelian randomization to evaluate the causal effects of genetic liability to nsCL/P on educational attainment and intelligence. RESULTS There was limited evidence for shared genetic aetiology or causal relationships between nsCL/P and educational attainment [genetic correlation (rg) -0.05, 95% confidence interval (CI) -0.12 to 0.01, P 0.13; MR estimate (βMR) -0.002, 95% CI -0.009 to 0.006, P 0.679) or intelligence (rg -0.04, 95% CI -0.13 to 0.04, P 0.34; βMR -0.009, 95% CI -0.02 to 0.002, P 0.11). CONCLUSIONS Common variants are unlikely to predispose individuals born with nsCL/P to low educational attainment or intelligence. This is an important first step towards understanding the aetiology of low educational attainment in this group.
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Affiliation(s)
- Christina Dardani
- Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Laurence J Howe
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.,Institute of Cardiovascular Science, University College London, London, UK
| | - Nandita Mukhopadhyay
- Centre for Craniofacial and Dental Genetics, Department of Oral Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Evie Stergiakouli
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.,The Cleft Collective, University of Bristol, Bristol, UK
| | - Yvonne Wren
- The Cleft Collective, University of Bristol, Bristol, UK.,Bristol Speech and Language Therapy Research Unit, North Bristol NHS Trust, Bristol, UK
| | | | - Amy Davies
- The Cleft Collective, University of Bristol, Bristol, UK
| | - Karen Ho
- The Cleft Collective, University of Bristol, Bristol, UK.,Bristol Bioresource Laboratories, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Seth M Weinberg
- Centre for Craniofacial and Dental Genetics, Department of Oral Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Mary L Marazita
- Centre for Craniofacial and Dental Genetics, Department of Oral Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Kerstin U Ludwig
- Institute of Human Genetics, University of Bonn, Bonn, Germany.,Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany
| | - Caroline L Relton
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Sarah J Lewis
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.,The Cleft Collective, University of Bristol, Bristol, UK
| | - Jonathan Sandy
- The Cleft Collective, University of Bristol, Bristol, UK.,Dean of the Faculty of Health Sciences, University of Bristol, Bristol, UK
| | - Neil M Davies
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Gemma C Sharp
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.,The Cleft Collective, University of Bristol, Bristol, UK
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