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Carter AR, Anderson EL. Correct illustration of assumptions in Mendelian randomization. Int J Epidemiol 2024; 53:dyae050. [PMID: 38580457 DOI: 10.1093/ije/dyae050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 03/20/2024] [Indexed: 04/07/2024] Open
Affiliation(s)
- Alice R Carter
- Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
| | - Emma L Anderson
- Division of Psychiatry, Department of Mental Health of Older People, University College London, London, UK
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2
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Carter AR, Clayton GL, Borges MC, Howe LD, Hughes RA, Smith GD, Lawlor DA, Tilling K, Griffith GJ. Time-sensitive testing pressures and COVID-19 outcomes: are socioeconomic inequalities over the first year of the pandemic explained by selection bias? BMC Public Health 2023; 23:1863. [PMID: 37752486 PMCID: PMC10521522 DOI: 10.1186/s12889-023-16767-5] [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: 02/28/2023] [Accepted: 09/15/2023] [Indexed: 09/28/2023] Open
Abstract
BACKGROUND There are many ways in which selection bias might impact COVID-19 research. Here we focus on selection for receiving a polymerase-chain-reaction (PCR) SARS-CoV-2 test and how known changes to selection pressures over time may bias research into COVID-19 infection. METHODS Using UK Biobank (N = 420,231; 55% female; mean age = 66.8 [SD = 8·11]) we estimate the association between socio-economic position (SEP) and (i) being tested for SARS-CoV-2 infection versus not being tested (ii) testing positive for SARS-CoV-2 infection versus testing negative and (iii) testing negative for SARS-CoV-2 infection versus not being tested. We construct four distinct time-periods between March 2020 and March 2021, representing distinct periods of testing pressures and lockdown restrictions and specify both time-stratified and combined models for each outcome. We explore potential selection bias by examining associations with positive and negative control exposures. RESULTS The association between more disadvantaged SEP and receiving a SARS-CoV-2 test attenuated over time. Compared to individuals with a degree, individuals whose highest educational qualification was a GCSE or equivalent had an OR of 1·27 (95% CI: 1·18 to 1·37) in March-May 2020 and 1·13 (95% CI: 1.·10 to 1·16) in January-March 2021. The magnitude of the association between educational attainment and testing positive for SARS-CoV-2 infection increased over the same period. For the equivalent comparison, the OR for testing positive increased from 1·25 (95% CI: 1·04 to 1·47), to 1·69 (95% CI: 1·55 to 1·83). We found little evidence of an association between control exposures, and any considered outcome. CONCLUSIONS The association between SEP and SARS-CoV-2 testing changed over time, highlighting the potential of time-specific selection pressures to bias analyses of COVID-19. Positive and negative control analyses suggest that changes in the association between SEP and SARS-CoV-2 infection over time likely reflect true increases in socioeconomic inequalities.
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Affiliation(s)
- Alice R Carter
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Gemma L Clayton
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - M Carolina Borges
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Laura D Howe
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Rachael A Hughes
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, 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, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Kate Tilling
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Gareth J Griffith
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
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North TL, Harrison S, Bishop DC, Wootton RE, Carter AR, Richardson TG, Payne RA, Salisbury C, Howe LD. Educational inequality in multimorbidity: causality and causal pathways. A mendelian randomisation study in UK Biobank. BMC Public Health 2023; 23:1644. [PMID: 37641019 PMCID: PMC10463319 DOI: 10.1186/s12889-023-16369-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 07/24/2023] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND Multimorbidity, typically defined as having two or more long-term health conditions, is associated with reduced wellbeing and life expectancy. Understanding the determinants of multimorbidity, including whether they are causal, may help with the design and prioritisation of prevention interventions. This study seeks to assess the causality of education, BMI, smoking and alcohol as determinants of multimorbidity, and the degree to which BMI, smoking and alcohol mediate differences in multimorbidity by level of education. METHODS Participants were 181,214 females and 155,677 males, mean ages 56.7 and 57.1 years respectively, from UK Biobank. We used a Mendelian randomization design; an approach that uses genetic variants as instrumental variables to interrogate causality. RESULTS The prevalence of multimorbidity was 55.1%. Mendelian randomization suggests that lower education, higher BMI and higher levels of smoking causally increase the risk of multimorbidity. For example, one standard deviation (equivalent to 5.1 years) increase in genetically-predicted years of education decreases the risk of multimorbidity by 9.0% (95% CI: 6.5 to 11.4%). A 5 kg/m2 increase in genetically-predicted BMI increases the risk of multimorbidity by 9.2% (95% CI: 8.1 to 10.3%) and a one SD higher lifetime smoking index increases the risk of multimorbidity by 6.8% (95% CI: 3.3 to 10.4%). Evidence for a causal effect of genetically-predicted alcohol consumption on multimorbidity was less strong; an increase of 5 units of alcohol per week increases the risk of multimorbidity by 1.3% (95% CI: 0.2 to 2.5%). The proportions of the association between education and multimorbidity explained by BMI and smoking are 20.4% and 17.6% respectively. Collectively, BMI and smoking account for 31.8% of the educational inequality in multimorbidity. CONCLUSIONS Education, BMI, smoking and alcohol consumption are intervenable causal risk factors for multimorbidity. Furthermore, BMI and lifetime smoking make a considerable contribution to the generation of educational inequalities in multimorbidity. Public health interventions that improve population-wide levels of these risk factors are likely to reduce multimorbidity and inequalities in its occurrence.
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Affiliation(s)
- Teri-Louise North
- MRC Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, UK.
| | - Sean Harrison
- MRC Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, UK
| | - Deborah C Bishop
- MRC Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, UK
| | - Robyn E Wootton
- MRC Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, UK
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
- School of Psychological Science, University of Bristol, Bristol, UK
| | - Alice R Carter
- MRC Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, UK
| | - Tom G Richardson
- MRC Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, UK
| | - Rupert A Payne
- Centre for Academic Primary Care, Population Health Sciences, University of Bristol, Bristol, UK
- Exeter Collaboration for Academic Primary Care, Department of Health and Community Sciences, University of Exeter, Exeter, UK
| | - Chris Salisbury
- Centre for Academic Primary Care, Population Health Sciences, University of Bristol, Bristol, UK
| | - Laura D Howe
- MRC Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, UK
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Crick DCP, Sanderson E, Jones H, Goulding N, Borges MC, Clayton G, Carter AR, Halligan S, Lawlor DA, Khandaker GM, Fraser A. Glycoprotein acetyls and depression: Testing for directionality and potential causality using longitudinal data and Mendelian randomization analyses. J Affect Disord 2023; 335:431-439. [PMID: 37196932 PMCID: PMC7615476 DOI: 10.1016/j.jad.2023.05.033] [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: 02/01/2023] [Revised: 05/05/2023] [Accepted: 05/11/2023] [Indexed: 05/19/2023]
Abstract
BACKGROUND Inflammation is associated with depression, but causality remains unclear. We investigated potential causality and direction of effect between inflammation and depression. METHODS Using data from the ALSPAC birth cohort (n = 4021; 42.18 % male), we used multivariable regression to investigate bidirectional longitudinal associations of GlycA and depression and depression symptoms, assessed at ages 18y and 24y. We used two-sample Mendelian randomization (MR) to investigate potential causality and directionality. Genetic variants for GlycA were obtained from UK Biobank (UKB) (N = 115,078); for depression from the Psychiatric Genomics Consortium and UKB (N = 500,199); and for depressive symptoms (N = 161,460) from the Social Science Genetic Association Consortium. In addition to the Inverse Variance Weighted method, we used sensitivity analyses to strengthen causal inference. We conducted multivariable MR adjusting for body mass index (BMI) due to known genetic correlation between inflammation, depression and BMI. RESULTS In the cohort analysis, after adjusting for potential confounders we found no evidence of associations between GlycA and depression symptoms score or vice versa. We observed an association between GlycA and depression (OR = 1∙18, 95 % CI: 1∙03-1∙36). MR suggested no causal effect of GlycA on depression, but there was a causal effect of depression on GlycA (mean difference in GlycA = 0∙09; 95 % CI: 0∙03-0∙16), which was maintained in some, but not all, sensitivity analyses. LIMITATIONS The GWAS sample overlap could incur bias. CONCLUSION We found no consistent evidence for an effect of GlycA on depression. There was evidence that depression increases GlycA in the MR analysis, but this may be confounded/mediated by BMI.
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Affiliation(s)
- Daisy C P Crick
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
| | - Eleanor Sanderson
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
| | - Hannah Jones
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
| | - Neil Goulding
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
| | - Maria Carolina Borges
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
| | - Gemma Clayton
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
| | - Alice R Carter
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
| | - Sarah Halligan
- Department of Psychology, University of Bath, Bath, UK; Department of Psychiatry and Mental Health, University of Cape Town, South Africa
| | - Deborah A Lawlor
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
| | - Golam M Khandaker
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; NIHR Bristol Biomedical Research Centre, Bristol, UK; Avon and Wiltshire Mental Health Partnership NHS Trust, Bristol, UK
| | - Abigail Fraser
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
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Carter AR, Fraser A, Howe LD, Harris S, Hughes A. Why caution should be applied when interpreting and promoting findings from Mendelian randomisation studies. Gen Psychiatr 2023; 36:e101047. [PMID: 37583791 PMCID: PMC10423826 DOI: 10.1136/gpsych-2023-101047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 07/19/2023] [Indexed: 08/17/2023] Open
Affiliation(s)
- Alice R Carter
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, University of Bristol, Bristol, UK
| | - Abigail Fraser
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, University of Bristol, Bristol, UK
| | - Laura D Howe
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, University of Bristol, Bristol, UK
| | - Sian Harris
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Amanda Hughes
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, University of Bristol, Bristol, UK
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Zollner L, Boekstegers F, Barahona Ponce C, Scherer D, Marcelain K, Gárate-Calderón V, Waldenberger M, Morales E, Rojas A, Munoz C, Retamales J, De Toro G, Kortmann AV, Barajas O, Rivera MT, Cortés A, Loader D, Saavedra J, Gutiérrez L, Ortega A, Bertrán ME, Bartolotti L, Gabler F, Campos M, Alvarado J, Moisán F, Spencer L, Nervi B, Carvajal D, Losada H, Almau M, Fernández P, Olloquequi J, Carter AR, Miquel Poblete JF, Bustos BI, Fuentes Guajardo M, Gonzalez-Jose R, Bortolini MC, Acuña-Alonzo V, Gallo C, Ruiz Linares A, Rothhammer F, Lorenzo Bermejo J. Gallbladder Cancer Risk and Indigenous South American Mapuche Ancestry: Instrumental Variable Analysis Using Ancestry-Informative Markers. Cancers (Basel) 2023; 15:4033. [PMID: 37627062 PMCID: PMC10452561 DOI: 10.3390/cancers15164033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 07/28/2023] [Accepted: 08/04/2023] [Indexed: 08/27/2023] Open
Abstract
A strong association between the proportion of indigenous South American Mapuche ancestry and the risk of gallbladder cancer (GBC) has been reported in observational studies. Chileans show the highest incidence of GBC worldwide, and the Mapuche are the largest indigenous people in Chile. We set out to assess the confounding-free effect of the individual proportion of Mapuche ancestry on GBC risk and to investigate the mediating effects of gallstone disease and body mass index (BMI) on this association. Genetic markers of Mapuche ancestry were selected based on the informativeness for assignment measure, and then used as instrumental variables in two-sample Mendelian randomization analyses and complementary sensitivity analyses. Results suggested a putatively causal effect of Mapuche ancestry on GBC risk (inverse variance-weighted (IVW) risk increase of 0.8% per 1% increase in Mapuche ancestry proportion, 95% CI 0.4% to 1.2%, p = 6.7 × 10-5) and also on gallstone disease (3.6% IVW risk increase, 95% CI 3.1% to 4.0%), pointing to a mediating effect of gallstones on the association between Mapuche ancestry and GBC. In contrast, the proportion of Mapuche ancestry showed a negative effect on BMI (IVW estimate -0.006 kg/m2, 95% CI -0.009 to -0.003). The results presented here may have significant implications for GBC prevention and are important for future admixture mapping studies. Given that the association between the individual proportion of Mapuche ancestry and GBC risk previously noted in observational studies appears to be free of confounding, primary and secondary prevention strategies that consider genetic ancestry could be particularly efficient.
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Affiliation(s)
- Linda Zollner
- Statistical Genetics Research Group, Institute of Medical Biometry, Heidelberg University, 69120 Heidelberg, Germany; (L.Z.); (F.B.); (C.B.P.); (D.S.); (V.G.-C.)
- Division of Proteomics of Stem Cells and Cancer, German Cancer Research Center, 69120 Heidelberg, Germany
| | - Felix Boekstegers
- Statistical Genetics Research Group, Institute of Medical Biometry, Heidelberg University, 69120 Heidelberg, Germany; (L.Z.); (F.B.); (C.B.P.); (D.S.); (V.G.-C.)
| | - Carol Barahona Ponce
- Statistical Genetics Research Group, Institute of Medical Biometry, Heidelberg University, 69120 Heidelberg, Germany; (L.Z.); (F.B.); (C.B.P.); (D.S.); (V.G.-C.)
| | - Dominique Scherer
- Statistical Genetics Research Group, Institute of Medical Biometry, Heidelberg University, 69120 Heidelberg, Germany; (L.Z.); (F.B.); (C.B.P.); (D.S.); (V.G.-C.)
| | - Katherine Marcelain
- Department of Basic and Clinical Oncology, Medical Faculty, University of Chile, Santiago 8380000, Chile; (K.M.); (O.B.)
| | - Valentina Gárate-Calderón
- Statistical Genetics Research Group, Institute of Medical Biometry, Heidelberg University, 69120 Heidelberg, Germany; (L.Z.); (F.B.); (C.B.P.); (D.S.); (V.G.-C.)
- Department of Basic and Clinical Oncology, Medical Faculty, University of Chile, Santiago 8380000, Chile; (K.M.); (O.B.)
| | - Melanie Waldenberger
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany;
| | - Erik Morales
- Hospital Regional de Talca, Talca 3460000, Chile; (E.M.); (C.M.)
- Facultad de Medicina, Universidad Católica del Maule, Talca 3460000, Chile;
| | - Armando Rojas
- Facultad de Medicina, Universidad Católica del Maule, Talca 3460000, Chile;
| | - César Munoz
- Hospital Regional de Talca, Talca 3460000, Chile; (E.M.); (C.M.)
- Facultad de Medicina, Universidad Católica del Maule, Talca 3460000, Chile;
| | | | - Gonzalo De Toro
- Hospital de Puerto Montt, Puerto Montt 5480000, Chile; (G.D.T.); (A.V.K.)
- Escuela de Tecnología Médica, Universidad Austral de Chile sede Puerto Montt, Puerto Montt 5480000, Chile
| | | | - Olga Barajas
- Department of Basic and Clinical Oncology, Medical Faculty, University of Chile, Santiago 8380000, Chile; (K.M.); (O.B.)
- Hospital Clínico Universidad de Chile, Santiago 8380456, Chile
| | | | - Analía Cortés
- Hospital del Salvador, Santiago 7500922, Chile; (M.T.R.); (A.C.)
| | - Denisse Loader
- Hospital Padre Hurtado, Santiago 8880456, Chile; (D.L.); (J.S.)
| | | | | | | | | | | | - Fernando Gabler
- Hospital San Borja Arriarán, Santiago 8320000, Chile; (F.G.); (M.C.)
| | - Mónica Campos
- Hospital San Borja Arriarán, Santiago 8320000, Chile; (F.G.); (M.C.)
| | - Juan Alvarado
- Hospital Regional Guillermo Grant Benavente, Concepción 4070386, Chile; (J.A.); (F.M.); (L.S.)
| | - Fabricio Moisán
- Hospital Regional Guillermo Grant Benavente, Concepción 4070386, Chile; (J.A.); (F.M.); (L.S.)
| | - Loreto Spencer
- Hospital Regional Guillermo Grant Benavente, Concepción 4070386, Chile; (J.A.); (F.M.); (L.S.)
| | - Bruno Nervi
- Departamento de Hematología y Oncología, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago 8330077, Chile;
| | - Daniel Carvajal
- Facultad de Medicina, Clínica Alemana Universidad del Desarrollo, Santiago 7650568, Chile;
| | - Héctor Losada
- Departamento de Cirugía, Universidad de la Frontera, Temuco 4780000, Chile;
| | - Mauricio Almau
- Hospital de Rancagua, Rancagua 2820000, Chile; (M.A.); (P.F.)
| | | | - Jordi Olloquequi
- Department of Biochemistry and Physiology, Faculty of Pharmacy and Food Sciences, University of Barcelona, 08028 Barcelona, Spain;
- Facultad de Ciencias de la Salud, Universidad Autónoma de Chile, Talca 3460000, Chile
| | - Alice R. Carter
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 1UD, UK;
| | - Juan Francisco Miquel Poblete
- Departamento de Gastroenterología, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago 8320000, Chile;
| | - Bernabe Ignacio Bustos
- Ken and Ruth Davee Department of Neurology and Simpson Querrey Center for Neurogenetics, Northwestern University, Feinberg School of Medicine, Chicago, IL 60611, USA;
| | - Macarena Fuentes Guajardo
- Departamento de Tecnología Médica, Facultad de Ciencias de la Salud, Tarapacá University, Arica 1000815, Chile;
| | - Rolando Gonzalez-Jose
- Instituto Patagónico de Ciencias Sociales y Humanas, Centro Nacional Patagónico, CONICET, Puerto Madryn U9120ACD, Argentina;
| | - Maria Cátira Bortolini
- Instituto de Biociências, Universidad Federal do Rio Grande do Sul, Puerto Alegre 15053, Brazil;
| | | | - Carla Gallo
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima 15102, Peru;
| | - Andres Ruiz Linares
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center of Genetics and Development, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai 200434, China;
- ADES (Anthropologie Bio-Culturelle, Droit, Éthique et Santé), UFR de Médecine, Aix-Marseille University, 13007 Marseille, France
- Department of Genetics, Evolution and Environment and UCL Genetics Institute, University College London, London WC1E 6BT, UK
| | | | - Justo Lorenzo Bermejo
- Statistical Genetics Research Group, Institute of Medical Biometry, Heidelberg University, 69120 Heidelberg, Germany; (L.Z.); (F.B.); (C.B.P.); (D.S.); (V.G.-C.)
- Department of Biostatistics for Precision Oncology, Institut de Cancérologie Strasbourg Europe, 67200 Strasbourg, France
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Clayton GL, Gonçalves A, Soares, Goulding N, Borges MC, Holmes MV, Davey G, Smith, Tilling K, Lawlor DA, Carter AR. A framework for assessing selection and misclassification bias in mendelian randomisation studies: an illustrative example between body mass index and covid-19. BMJ 2023; 381:e072148. [PMID: 37336561 PMCID: PMC10277657 DOI: 10.1136/bmj-2022-072148] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/15/2023] [Indexed: 06/21/2023]
Affiliation(s)
- Gemma L Clayton
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | | | - Soares
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Neil Goulding
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Maria Carolina Borges
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Michael V Holmes
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | | | - Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- National Institute for Health Research Bristol Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, Bristol, UK
| | - Kate Tilling
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- National Institute for Health Research Bristol Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, Bristol, UK
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- National Institute for Health Research Bristol Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, Bristol, UK
| | - Alice R Carter
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
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8
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Urquijo H, Soares AG, Fraser A, Howe LD, Carter AR. Investigating effect modification between childhood maltreatment and genetic risk for cardiovascular disease in the UK Biobank. PLoS One 2023; 18:e0285258. [PMID: 37141292 PMCID: PMC10159177 DOI: 10.1371/journal.pone.0285258] [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: 11/09/2022] [Accepted: 04/18/2023] [Indexed: 05/05/2023] Open
Abstract
Cardiovascular disease (CVD) is influenced by genetic and environmental factors. Childhood maltreatment is associated with CVD and may modify genetic susceptibility to cardiovascular risk factors. We used genetic and phenotypic data from 100,833 White British UK Biobank participants (57% female; mean age = 55.9 years). We regressed nine cardiovascular risk factors/diseases (alcohol consumption, body mass index [BMI], low-density lipoprotein cholesterol, lifetime smoking behaviour, systolic blood pressure, atrial fibrillation, coronary heart disease, type 2 diabetes, and stroke) on their respective polygenic scores (PGS) and self-reported exposure to childhood maltreatment. Effect modification was tested on the additive and multiplicative scales by including a product term (PGS*maltreatment) in regression models. On the additive scale, childhood maltreatment accentuated the effect of genetic susceptibility to higher BMI (Peffect modification: 0.003). Individuals not exposed to childhood maltreatment had an increase in BMI of 0.12 SD (95% CI: 0.11, 0.13) per SD increase in BMI PGS, compared to 0.17 SD (95% CI: 0.14, 0.19) in those exposed to all types of childhood maltreatment. On the multiplicative scale, similar results were obtained for BMI though these did not withstand to Bonferroni correction. There was little evidence of effect modification by childhood maltreatment in relation to other outcomes, or of sex-specific effect modification. Our study suggests the effects of genetic susceptibility to a higher BMI may be moderately accentuated in individuals exposed to childhood maltreatment. However, gene*environment interactions are likely not a major contributor to the excess CVD burden experienced by childhood maltreatment victims.
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Affiliation(s)
- Helena Urquijo
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Ana Gonçalves Soares
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Abigail Fraser
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Laura D. Howe
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Alice R. Carter
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
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Millard LAC, Fernández-Sanlés A, Carter AR, Hughes RA, Tilling K, Morris TP, Major-Smith D, Griffith GJ, Clayton GL, Kawabata E, Davey Smith G, Lawlor DA, Borges MC. Exploring the impact of selection bias in observational studies of COVID-19: a simulation study. Int J Epidemiol 2023; 52:44-57. [PMID: 36474414 PMCID: PMC9908043 DOI: 10.1093/ije/dyac221] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 11/28/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Non-random selection of analytic subsamples could introduce selection bias in observational studies. We explored the potential presence and impact of selection in studies of SARS-CoV-2 infection and COVID-19 prognosis. METHODS We tested the association of a broad range of characteristics with selection into COVID-19 analytic subsamples in the Avon Longitudinal Study of Parents and Children (ALSPAC) and UK Biobank (UKB). We then conducted empirical analyses and simulations to explore the potential presence, direction and magnitude of bias due to this selection (relative to our defined UK-based adult target populations) when estimating the association of body mass index (BMI) with SARS-CoV-2 infection and death-with-COVID-19. RESULTS In both cohorts, a broad range of characteristics was related to selection, sometimes in opposite directions (e.g. more-educated people were more likely to have data on SARS-CoV-2 infection in ALSPAC, but less likely in UKB). Higher BMI was associated with higher odds of SARS-CoV-2 infection and death-with-COVID-19. We found non-negligible bias in many simulated scenarios. CONCLUSIONS Analyses using COVID-19 self-reported or national registry data may be biased due to selection. The magnitude and direction of this bias depend on the outcome definition, the true effect of the risk factor and the assumed selection mechanism; these are likely to differ between studies with different target populations. Bias due to sample selection is a key concern in COVID-19 research based on national registry data, especially as countries end free mass testing. The framework we have used can be applied by other researchers assessing the extent to which their results may be biased for their research question of interest.
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Affiliation(s)
- Louise A C Millard
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Alba Fernández-Sanlés
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Alice R Carter
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Rachael A Hughes
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Kate Tilling
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Biomedical Research Centre, Bristol, UK
| | - Tim P Morris
- MRC Clinical Trials Unit, University College London, London, UK
| | - Daniel Major-Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Gareth J Griffith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Gemma L Clayton
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Emily Kawabata
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, 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 Biomedical Research Centre, Bristol, UK
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Biomedical Research Centre, Bristol, UK
| | - Maria Carolina Borges
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
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10
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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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11
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Fernández-Sanlés A, Smith D, Clayton GL, Northstone K, Carter AR, Millard LAC, Borges MC, Timpson NJ, Tilling K, Griffith GJ, Lawlor DA. Bias from questionnaire invitation and response in COVID-19 research: an example using ALSPAC. Wellcome Open Res 2022; 6:184. [PMID: 35919505 PMCID: PMC9294498 DOI: 10.12688/wellcomeopenres.17041.2] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/05/2022] [Indexed: 11/20/2022] Open
Abstract
Background: Longitudinal studies are crucial for identifying potential risk factors for infection with, and consequences of, COVID-19, but relationships can be biased if they are associated with invitation and response to data collection. We describe factors relating to questionnaire invitation and response in COVID-19 questionnaire data collection in a multigenerational birth cohort (the Avon Longitudinal Study of Parents and Children, ALSPAC). Methods: We analysed online questionnaires completed between the beginning of the pandemic and easing of the first UK lockdown by participants with valid email addresses who had not actively disengaged from the study. We assessed associations of pre-pandemic sociodemographic, behavioural, anthropometric and health-related factors with: i) being sent a questionnaire; ii) returning a questionnaire; and iii) item response (for specific questions). Analyses were conducted in three cohorts: the index children born in the early 1990s (now young adults; 41 variables assessed), their mothers (35 variables) and the mothers' partners (27 variables). Results: Of 14,849 young adults, 41% were sent a questionnaire, of whom 57% returned one. Item response was >95%. In this cohort, 78% of factors were associated with being sent a questionnaire, 56% with returning one, and, as an example of item response, 20% with keyworker status response. For instance, children from mothers educated to degree-level had greater odds of being sent a questionnaire (OR=5.59; 95% CI=4.87-6.41), returning one (OR=1.60; 95% CI=1.31-1.95), and responding to items (e.g., keyworker status OR=1.65; 95% CI=0.88-3.04), relative to children from mothers with fewer qualifications. Invitation and response rates and associations were similar in all cohorts. Conclusions: These results highlight the importance of considering potential biases due to non-response when using longitudinal studies in COVID-19 research and interpreting results. We recommend researchers report response rates and factors associated with invitation and response in all COVID-19 observational research studies, which can inform sensitivity analyses.
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Affiliation(s)
- Alba Fernández-Sanlés
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Daniel Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Gemma L Clayton
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Kate Northstone
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Alice R Carter
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Louise AC Millard
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Maria Carolina Borges
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Nicholas John Timpson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Kate Tilling
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Gareth J Griffith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- Bristol National Institute of Health Research (NIHR) Biomedical Research Centre, Bristol, UK
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12
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Zhao SS, Holmes MV, Zheng J, Sanderson E, Carter AR. The impact of education inequality on rheumatoid arthritis risk is mediated by smoking and body mass index: Mendelian randomization study. Rheumatology (Oxford) 2022; 61:2167-2175. [PMID: 34436562 PMCID: PMC9071527 DOI: 10.1093/rheumatology/keab654] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [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: 04/15/2021] [Revised: 08/10/2021] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE To estimate the causal relationship between educational attainment-as a proxy for socioeconomic inequality-and risk of RA, and quantify the roles of smoking and BMI as potential mediators. METHODS Using the largest genome-wide association studies (GWAS), we performed a two-sample Mendelian randomization (MR) study of genetically predicted educational attainment (instrumented using 1265 variants from 766 345 individuals) and RA (14 361 cases, 43 923 controls). We used two-step MR to quantify the proportion of education's effect on RA mediated by smoking exposure (as a composite index capturing duration, heaviness and cessation, using 124 variants from 462 690 individuals) and BMI (517 variants, 681 275 individuals), and multivariable MR to estimate proportion mediated by both factors combined. RESULTS Each s.d. increase in educational attainment (4.2 years of schooling) was protective of RA (odds ratio 0.37; 95% CI: 0.31, 0.44). Higher educational attainment was also protective for smoking exposure (β = -0.25 s.d.; 95% CI: -0.26, -0.23) and BMI [β = -0.27 s.d. (∼1.3 kg/m2); 95% CI: -0.31, -0.24]. Smoking mediated 24% (95% CI: 13%, 35%) and BMI 17% (95% CI: 11%, 23%) of the total effect of education on RA. Combined, the two risk factors explained 47% (95% CI: 11%, 82%) of the total effect. CONCLUSION Higher educational attainment has a protective effect on RA risk. Interventions to reduce smoking and excess adiposity at a population level may reduce this risk, but a large proportion of education's effect on RA remains unexplained. Further research into other risk factors that act as potentially modifiable mediators are required.
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Affiliation(s)
- Sizheng Steven Zhao
- Musculoskeletal Biology, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol
| | - Michael V Holmes
- MRC Population Health Research Unit at the University of Oxford, Oxford
| | - Jie Zheng
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol
| | - Eleanor Sanderson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Alice R Carter
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
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13
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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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15
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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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16
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Jones DP, Wootton RE, Gill D, Carter AR, Gunnell D, Munafò MR, Sallis HM. Mental Health as a Mediator of the Association Between Educational Inequality and Cardiovascular Disease: A Mendelian Randomization Study. J Am Heart Assoc 2021; 10:e019340. [PMID: 34472355 PMCID: PMC8649303 DOI: 10.1161/jaha.120.019340] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background Education is inversely associated with cardiovascular disease (CVD). Several mediators of this have been established; however, a proportion of the protective effect remains unaccounted for. Mental health is a proposed mediator, but current evidence is mixed and subject to bias from confounding factors and reverse causation. Mendelian randomization is an instrumental variable technique that uses genetic proxies for exposures and mediators to reduce such bias. Methods and Results We performed logistic regression and 2‐step Mendelian randomization analyses using UK Biobank data and genetic summary statistics to investigate whether educational attainment affects risk of mental health disorders. We then performed mediation analyses to explore whether mental health disorders mediate the association between educational attainment and cardiovascular risk. Higher levels of educational attainment were associated with reduced depression, anxiety, and CVD in observational analyses (odds ratio [OR], 0.79 [95% CI, 0.77–0.81], 0.76 [95% CI, 0.73–0.79], and 0.75 [95% CI, 0.74–0.76], respectively), and Mendelian randomization analyses provided evidence of causality (OR, 0.72 [95% CI, 0.67–0.77], 0.50 [95% CI, 0.42–0.59], and 0.62 [95% CI, 0.58–0.66], respectively). Both anxiety and depression were associated with CVD in observational analyses (OR, 1.63 [95% CI, 1.49–1.79] and 1.70 [95% CI, 1.59–1.82], respectively) but only depression showed evidence of causality in the Mendelian randomization analyses (OR, 1.09; 95% CI, 1.03–1.15). An estimated 2% of the total protective effect of education on CVD was mediated by depression. Conclusions Higher levels of educational attainment protect against mental health disorders, and reduced depression accounts for a small proportion of the total protective effect of education on CVD.
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Affiliation(s)
- Daniel P Jones
- Department of Population Health Sciences Bristol Medical School University of Bristol UK.,Division of Population Medicine School of MedicineCardiff University UK
| | - Robyn E Wootton
- Department of Population Health Sciences Bristol Medical School University of Bristol UK.,MRC Integrative Epidemiology Unit University of BristolOakfield House Bristol UK
| | - Dipender Gill
- Department of Epidemiology and Biostatistics Imperial College London London UK.,Clinical Pharmacology and Therapeutics Section Institute of Medical and Biomedical Education and Institute for Infection and Immunity St George'sUniversity of London UK
| | - Alice R Carter
- Department of Population Health Sciences Bristol Medical School University of Bristol UK.,MRC Integrative Epidemiology Unit University of BristolOakfield House Bristol UK
| | - David Gunnell
- Department of Population Health Sciences Bristol Medical School University of Bristol UK.,NIHR Biomedical Research Centre at the University Hospitals Bristol and Weston NHS Foundation TrustUniversity of Bristol UK
| | - Marcus R Munafò
- MRC Integrative Epidemiology Unit University of BristolOakfield House Bristol UK.,NIHR Biomedical Research Centre at the University Hospitals Bristol and Weston NHS Foundation TrustUniversity of Bristol UK.,School of Psychological Science University of Bristol UK
| | - Hannah M Sallis
- Department of Population Health Sciences Bristol Medical School University of Bristol UK.,MRC Integrative Epidemiology Unit University of BristolOakfield House Bristol UK.,School of Psychological Science University of Bristol UK.,Centre for Academic Mental HealthPopulation Health SciencesBristol Medical SchoolUniversity of Bristol UK
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17
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Robinson O, Carter AR, Ala-Korpela M, Casas JP, Chaturvedi N, Engmann J, Howe LD, Hughes AD, Järvelin MR, Kähönen M, Karhunen V, Kuh D, Shah T, Ben-Shlomo Y, Sofat R, Lau CHE, Lehtimäki T, Menon U, Raitakari O, Ryan A, Providencia R, Smith S, Taylor J, Tillin T, Viikari J, Wong A, Hingorani AD, Kivimäki M, Vineis P. Metabolic profiles of socio-economic position: a multi-cohort analysis. Int J Epidemiol 2021; 50:768-782. [PMID: 33221853 PMCID: PMC8271201 DOI: 10.1093/ije/dyaa188] [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] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/01/2020] [Indexed: 12/11/2022] Open
Abstract
Background Low socio-economic position (SEP) is a risk factor for multiple health outcomes, but its molecular imprints in the body remain unclear. Methods We examined SEP as a determinant of serum nuclear magnetic resonance metabolic profiles in ∼30 000 adults and 4000 children across 10 UK and Finnish cohort studies. Results In risk-factor-adjusted analysis of 233 metabolic measures, low educational attainment was associated with 37 measures including higher levels of triglycerides in small high-density lipoproteins (HDL) and lower levels of docosahexaenoic acid (DHA), omega-3 fatty acids, apolipoprotein A1, large and very large HDL particles (including levels of their respective lipid constituents) and cholesterol measures across different density lipoproteins. Among adults whose father worked in manual occupations, associations with apolipoprotein A1, large and very large HDL particles and HDL-2 cholesterol remained after adjustment for SEP in later life. Among manual workers, levels of glutamine were higher compared with non-manual workers. All three indicators of low SEP were associated with lower DHA, omega-3 fatty acids and HDL diameter. At all ages, children of manual workers had lower levels of DHA as a proportion of total fatty acids. Conclusions Our work indicates that social and economic factors have a measurable impact on human physiology. Lower SEP was independently associated with a generally unfavourable metabolic profile, consistent across ages and cohorts. The metabolites we found to be associated with SEP, including DHA, are known to predict cardiovascular disease and cognitive decline in later life and may contribute to health inequalities.
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Affiliation(s)
- Oliver Robinson
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Alice R Carter
- MRC Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, UK
| | - Mika Ala-Korpela
- Systems Epidemiology, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.,Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland.,NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland.,Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing and Health Sciences, The Alfred Hospital, Monash University, Melbourne, Victoria, Australia
| | - Juan P Casas
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA.,Division of Aging, Department of Medicine, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA
| | - Nishi Chaturvedi
- MRC Unit for Lifelong Health and Ageing at UCL, Institute of Cardiovascular Science, University College London, UK
| | - Jorgen Engmann
- Institute of Cardiovascular Science, University College London, UK
| | - Laura D Howe
- MRC Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, UK
| | - Alun D Hughes
- MRC Unit for Lifelong Health and Ageing at UCL, Institute of Cardiovascular Science, University College London, UK
| | - Marjo-Riitta Järvelin
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK.,Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland.,Biocenter Oulu, University of Oulu, Oulu, Finland.,Unit of Primary Health Care, Oulu University Hospital, Oulu, Finland.,Department of Life Sciences, College of Health and Life Sciences, Brunel University London, Uxbridge, UK
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, University of Tampere, Tampere, Finland
| | - Ville Karhunen
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Diana Kuh
- MRC Unit for Lifelong Health and Ageing at UCL, Institute of Cardiovascular Science, University College London, UK
| | - Tina Shah
- Institute of Cardiovascular Science, University College London, UK
| | - Yoav Ben-Shlomo
- MRC Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, UK
| | - Reecha Sofat
- Institute of Health Informatics, University College London, London, UK
| | - Chung-Ho E Lau
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK.,Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, University of Tampere, Tampere, Finland
| | - Usha Menon
- MRC Clinical Trials Unit at UCL, University College London, UK
| | - Olli Raitakari
- Centre for Population Health Research, Turku University Hospital, University of Turku, Turku, Finland.,Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland.,Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
| | - Andy Ryan
- MRC Clinical Trials Unit at UCL, University College London, UK
| | - Rui Providencia
- Institute of Health Informatics, University College London, London, UK
| | - Stephanie Smith
- Department of Medicine, University of Turku, (and) Division of Medicine, Turku University Hospital, Turku, Finland
| | - Julie Taylor
- Institute of Health Informatics, University College London, London, UK
| | - Therese Tillin
- MRC Unit for Lifelong Health and Ageing at UCL, Institute of Cardiovascular Science, University College London, UK
| | - Jorma Viikari
- Department of Medicine, University of Turku, (and) Division of Medicine, Turku University Hospital, Turku, Finland
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, Institute of Cardiovascular Science, University College London, UK
| | - Aroon D Hingorani
- Institute of Cardiovascular Science, University College London, UK.,Health Data Research UK, London, UK.,University College London British Heart Foundation Research Accelerator, UK
| | - Mika Kivimäki
- Department of Epidemiology and Public Health, University College London, London, UK
| | - Paolo Vineis
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
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18
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Fernández-Sanlés A, Smith D, Clayton GL, Northstone K, Carter AR, Millard LAC, Borges MC, Timpson NJ, Tilling K, Griffith GJ, Lawlor DA. Bias from questionnaire invitation and response in COVID-19 research: an example using ALSPAC. Wellcome Open Res 2021; 6:184. [PMID: 35919505 PMCID: PMC9294498 DOI: 10.12688/wellcomeopenres.17041.1] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [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: 07/12/2021] [Indexed: 11/20/2022] Open
Abstract
Background: Longitudinal studies are crucial for identifying potential risk factors for infection with, and consequences of, COVID-19, but relationships can be biased if they are associated with invitation and response to data collection. We describe factors relating to questionnaire invitation and response in COVID-19 questionnaire data collection in a multigenerational birth cohort (the Avon Longitudinal Study of Parents and Children, ALSPAC). Methods: We analysed online questionnaires completed between the beginning of the pandemic and easing of the first UK lockdown by participants with valid email addresses who had not actively disengaged from the study. We assessed associations of pre-pandemic sociodemographic, behavioural, anthropometric and health-related factors with: i) being sent a questionnaire; ii) returning a questionnaire; and iii) item response (for specific questions). Analyses were conducted in three cohorts: the index children born in the early 1990s (now young adults; 41 variables assessed), their mothers (35 variables) and the mothers' partners (27 variables). Results: Of 14,849 young adults, 41% were sent a questionnaire, of whom 57% returned one. Item response was >95%. In this cohort, 78% of factors were associated with being sent a questionnaire, 56% with returning one, and, as an example of item response, 20% with keyworker status response. For instance, children from mothers educated to degree-level had greater odds of being sent a questionnaire (OR=5.59; 95% CI=4.87-6.41), returning one (OR=1.60; 95% CI=1.31-1.95), and responding to items (e.g., keyworker status OR=1.65; 95% CI=0.88-3.04), relative to children from mothers with fewer qualifications. Invitation and response rates and associations were similar in all cohorts. Conclusions: These results highlight the importance of considering potential biases due to non-response when using longitudinal studies in COVID-19 research and interpreting results. We recommend researchers report response rates and factors associated with invitation and response in all COVID-19 observational research studies, which can inform sensitivity analyses.
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Affiliation(s)
- Alba Fernández-Sanlés
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Daniel Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Gemma L Clayton
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Kate Northstone
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Alice R Carter
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Louise AC Millard
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Maria Carolina Borges
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Nicholas John Timpson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Kate Tilling
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Gareth J Griffith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- Bristol National Institute of Health Research (NIHR) Biomedical Research Centre, Bristol, UK
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19
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Gill D, Zuber V, Dawson J, Pearson-Stuttard J, Carter AR, Sanderson E, Karhunen V, Levin MG, Wootton RE, Klarin D, Tsao PS, Tsilidis KK, Damrauer SM, Burgess S, Elliott P. Risk factors mediating the effect of body mass index and waist-to-hip ratio on cardiovascular outcomes: Mendelian randomization analysis. Int J Obes (Lond) 2021; 45:1428-1438. [PMID: 34002035 PMCID: PMC8236409 DOI: 10.1038/s41366-021-00807-4] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [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: 04/03/2020] [Revised: 02/23/2021] [Accepted: 03/22/2021] [Indexed: 12/29/2022]
Abstract
BACKGROUND Higher body mass index (BMI) and waist-to-hip ratio (WHR) increase the risk of cardiovascular disease, but the extent to which this is mediated by blood pressure, diabetes, lipid traits, and smoking is not fully understood. METHODS Using consortia and UK Biobank genetic association summary data from 140,595 to 898,130 participants predominantly of European ancestry, Mendelian randomization mediation analysis was performed to investigate the degree to which systolic blood pressure (SBP), diabetes, lipid traits, and smoking mediated an effect of BMI and WHR on the risk of coronary artery disease (CAD), peripheral artery disease (PAD) and stroke. RESULTS The odds ratio of CAD per 1-standard deviation increase in genetically predicted BMI was 1.49 (95% CI 1.39 to 1.60). This attenuated to 1.34 (95% CI 1.24 to 1.45) after adjusting for genetically predicted SBP (proportion mediated 27%, 95% CI 3% to 50%), to 1.27 (95% CI 1.17 to 1.37) after adjusting for genetically predicted diabetes (41% mediated, 95% CI 18% to 63%), to 1.47 (95% CI 1.36 to 1.59) after adjusting for genetically predicted lipids (3% mediated, 95% -23% to 29%), and to 1.46 (95% CI 1.34 to 1.58) after adjusting for genetically predicted smoking (6% mediated, 95% CI -20% to 32%). Adjusting for all the mediators together, the estimate attenuated to 1.14 (95% CI 1.04 to 1.26; 66% mediated, 95% CI 42% to 91%). A similar pattern was observed when considering genetically predicted WHR as the exposure, and PAD or stroke as the outcome. CONCLUSIONS Measures to reduce obesity will lower the risk of cardiovascular disease primarily by impacting downstream metabolic risk factors, particularly diabetes and hypertension. Reduction of obesity prevalence alongside control and management of its mediators is likely to be most effective for minimizing the burden of obesity.
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Affiliation(s)
- 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 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, Oxford, UK.
| | - Verena Zuber
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, UK
- Medical Research Council Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Jesse Dawson
- University of Glasgow, Institute of Cardiovascular and Medical Sciences, Glasgow, UK
| | - Jonathan Pearson-Stuttard
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Medical Research Council Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Alice R Carter
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Eleanor Sanderson
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Ville Karhunen
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Michael G Levin
- Division of Cardiovascular Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
| | - Robyn E Wootton
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- School of Psychological Science, University of Bristol, Bristol, UK
- National Institute for Health Research Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust and the University of Bristol, Bristol, UK
| | - Derek Klarin
- Malcom Randall VA Medical Center, Gainesville, FL, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Boston, MA, USA
- Division of Vascular Surgery and Endovascular Therapy, University of Florida School of Medicine, Gainesville, Fl, USA
| | - Philip S Tsao
- VA Palo Alto Health Care System, Livermore, CA, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Konstantinos K Tsilidis
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Scott M Damrauer
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Stephen Burgess
- Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, UK
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Medical Research Council Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- UK Dementia Research Institute at Imperial College London, London, UK
- Imperial Biomedical Research Centre, Imperial College London and Imperial College NHS Healthcare Trust, London, UK
- Health Data Research UK-London, London, UK
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20
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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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21
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Carter AR, Santos Ferreira DL, Taylor AE, Lawlor DA, Davey Smith G, Sattar N, Chaturvedi N, Hughes AD, Howe LD. Role of the Metabolic Profile in Mediating the Relationship Between Body Mass Index and Left Ventricular Mass in Adolescents: Analysis of a Prospective Cohort Study. J Am Heart Assoc 2020; 9:e016564. [PMID: 33030065 PMCID: PMC7763376 DOI: 10.1161/jaha.120.016564] [Citation(s) in RCA: 2] [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] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Background We aimed to quantify the role of the plasma metabolic profile in explaining the effect of adiposity on cardiac structure. Methods and Results Body mass index (BMI) was measured at age 11 in the Avon Longitudinal Study of Parents and Children. Left ventricular mass indexed to height2.7 (LVMI) was assessed by echocardiography at age 17. The metabolic profile was quantified via 1H-nuclear magnetic resonance spectroscopy at age 15. Multivariable confounder (maternal age, parity, highest qualification, maternal smoking, prepregnancy BMI, prepregnancy height, household social class, adolescent birthweight, adolescent smoking, fruit and vegetable consumption, and physical activity)-adjusted linear regression estimated the association of BMI with LVMI and mediation by metabolic traits. We considered 156 metabolomic traits individually and jointly as principal components explaining 95% of the variance in the nuclear magnetic resonance platform and assessed whether the principal components for the metabolic traits added to the proportion of the association explained by putative cardiovascular risk factors (systolic and diastolic blood pressures, insulin, triglycerides, low-density lipoprotein cholesterol, and glucose). A 1 kg/m2 higher BMI was associated with a 0.70 g/m2.7 (95% CI, 0.53-0.88 g/m2.7) and 0.66 g/m2.7 (95% CI, 0.53-0.79 g/m2.7) higher LVMI in males (n=437) and females (n=536), respectively. Putative risk factors explained 3% (95% CI, 2%-5%) of this association in males, increasing to 10% (95% CI, 8%-13%) when including metabolic principal components. In females, the standard risk factors explained 3% (95% CI, 2%-5%) of the association and did not increase when including the metabolic principal components. Conclusions The addition of the nuclear magnetic resonance-measured metabolic traits appears to mediate more of the association of BMI on LVMI than the putative risk factors alone in adolescent males, but not females.
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Affiliation(s)
- Alice R Carter
- MRC Integrative Epidemiology Unit Population Health Sciences University of Bristol United Kingdom
| | - Diana L Santos Ferreira
- MRC Integrative Epidemiology Unit Population Health Sciences University of Bristol United Kingdom
| | - Amy E Taylor
- MRC Integrative Epidemiology Unit Population Health Sciences University of Bristol United Kingdom.,National Institute for Health Research Biomedical Research Centre at the University Hospitals Bristol NHS Foundation Trust and the University of Bristol Bristol United Kingdom
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit Population Health Sciences University of Bristol United Kingdom.,National Institute for Health Research Biomedical Research Centre at the University Hospitals Bristol NHS Foundation Trust and the University of Bristol Bristol United Kingdom
| | - George Davey Smith
- MRC Integrative Epidemiology Unit Population Health Sciences University of Bristol United Kingdom.,National Institute for Health Research Biomedical Research Centre at the University Hospitals Bristol NHS Foundation Trust and the University of Bristol Bristol United Kingdom
| | - Naveed Sattar
- Institute of Cardiovascular and Medical Science University of Glasgow United Kingdom
| | - Nishi Chaturvedi
- Institute of Cardiovascular Science University College London London United Kingdom
| | - Alun D Hughes
- Institute of Cardiovascular Science University College London London United Kingdom
| | - Laura D Howe
- MRC Integrative Epidemiology Unit Population Health Sciences University of Bristol United Kingdom
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Carter AR, Gill D, Davies NM, Taylor AE, Tillmann T, Vaucher J, Wootton RE, Munafò MR, Hemani G, Malik R, Seshadri S, Woo D, Burgess S, Davey Smith G, Holmes MV, Tzoulaki I, Howe LD, Dehghan A. Understanding the consequences of education inequality on cardiovascular disease: mendelian randomisation study. BMJ 2019; 365:l1855. [PMID: 31122926 PMCID: PMC6529852 DOI: 10.1136/bmj.l1855] [Citation(s) in RCA: 129] [Impact Index Per Article: 25.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/25/2019] [Indexed: 12/19/2022]
Abstract
OBJECTIVES To investigate the role of body mass index (BMI), systolic blood pressure, and smoking behaviour in explaining the effect of education on the risk of cardiovascular disease outcomes. DESIGN Mendelian randomisation study. SETTING UK Biobank and international genome-wide association study data. PARTICIPANTS Predominantly participants of European ancestry. EXPOSURE Educational attainment, BMI, systolic blood pressure, and smoking behaviour in observational analysis, and randomly allocated genetic variants to instrument these traits in mendelian randomisation. MAIN OUTCOMES MEASURE The risk of coronary heart disease, stroke, myocardial infarction, and cardiovascular disease (all subtypes; all measured in odds ratio), and the degree to which this is mediated through BMI, systolic blood pressure, and smoking behaviour respectively. RESULTS Each additional standard deviation of education (3.6 years) was associated with a 13% lower risk of coronary heart disease (odds ratio 0.86, 95% confidence interval 0.84 to 0.89) in observational analysis and a 37% lower risk (0.63, 0.60 to 0.67) in mendelian randomisation analysis. As a proportion of the total risk reduction, BMI was estimated to mediate 15% (95% confidence interval 13% to 17%) and 18% (14% to 23%) in the observational and mendelian randomisation estimates, respectively. Corresponding estimates were 11% (9% to 13%) and 21% (15% to 27%) for systolic blood pressure and 19% (15% to 22%) and 34% (17% to 50%) for smoking behaviour. All three risk factors combined were estimated to mediate 42% (36% to 48%) and 36% (5% to 68%) of the effect of education on coronary heart disease in observational and mendelian randomisation analyses, respectively. Similar results were obtained when investigating the risk of stroke, myocardial infarction, and cardiovascular disease. CONCLUSIONS BMI, systolic blood pressure, and smoking behaviour mediate a substantial proportion of the protective effect of education on the risk of cardiovascular outcomes and intervening on these would lead to reductions in cases of cardiovascular disease attributable to lower levels of education. However, more than half of the protective effect of education remains unexplained and requires further investigation.
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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
| | - Dipender Gill
- Department of Biostatistics and Epidemiology, School of Public Health, Imperial College London, London, 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
| | - Amy E Taylor
- 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
| | - Taavi Tillmann
- Institute for Global Health, University College London, London, UK
| | - Julien Vaucher
- Lausanne University Hospital, Lausanne, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Robyn E Wootton
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- National Institute for Health Research Biomedical Research Centre at the University Hospitals Bristol NHS Foundation Trust and the University of Bristol, Bristol, UK
- School of Experimental Psychology, University of Bristol, Bristol, UK
| | - Marcus R Munafò
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- National Institute for Health Research Biomedical Research Centre at the University Hospitals Bristol NHS Foundation Trust and the University of Bristol, Bristol, UK
- School of Experimental Psychology, University of Bristol, Bristol, UK
- UK Centre for Tobacco and Alcohol Studies, School of Psychological Science, University of Bristol, Bristol, UK
| | - Gibran Hemani
- 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
| | - Rainer Malik
- Institute for Stroke and Dementia Research, University Hospital of Ludwig-Maximilians University, Munich, Germany
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Sciences Centre, San Antonio, TX, USA
- The Framingham Heart Study, Framingham, MA, USA
| | - Daniel Woo
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Stephen Burgess
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Department of Public Health and Primary Care, 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, 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
| | - Michael V Holmes
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- MRC Population Health Research Unit at the University of Oxford, Oxford, UK
- National Institute for Health Research, Oxford Biomedical Research Centre, Oxford University Hospital, Oxford, UK
| | - Ioanna Tzoulaki
- Department of Biostatistics and Epidemiology, School of Public Health, Imperial College London, London, UK
- MRC-PHE Centre for Environment, School of Public Health, Imperial College London, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - 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
| | - Abbas Dehghan
- Department of Biostatistics and Epidemiology, School of Public Health, Imperial College London, London, UK
- MRC-PHE Centre for Environment, School of Public Health, Imperial College London, London, UK
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Carter AR, Borges MC, Benn M, Tybjærg-Hansen A, Davey Smith G, Nordestgaard BG, Lawlor DA. Combined Association of Body Mass Index and Alcohol Consumption With Biomarkers for Liver Injury and Incidence of Liver Disease: A Mendelian Randomization Study. JAMA Netw Open 2019; 2:e190305. [PMID: 30848805 PMCID: PMC6484655 DOI: 10.1001/jamanetworkopen.2019.0305] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
IMPORTANCE Individually, higher body mass index (BMI) and alcohol consumption increase the risk of liver disease. Evidence of a joint association is mixed; however, previous studies have not used causal inference methods robust to confounding and reverse causation. Understanding any true effect is key to developing effective interventions to reduce liver disease. OBJECTIVE To investigate the joint association of BMI and alcohol consumption with liver injury biomarkers and incident liver disease using factorial mendelian randomization (MR). DESIGN, SETTING, AND PARTICIPANTS A population-based cohort study (Copenhagen General Population Study) recruited a random sample of Copenhagen, Denmark, residents aged 20 years or older of white, Danish descent (N = 98 643) between November 25, 2003, and July 1, 2014. Data were also obtained from ongoing links to national registers, and then analyzed from September 30, 2016, to April 23, 2018. EXPOSURES High and low BMI and alcohol consumption categories from baseline-measured or self-reported observational data and genetic variants predicting BMI and alcohol consumption. MAIN OUTCOMES AND MEASURES Plasma biomarkers of liver injury (alanine aminotransferase [ALT] and γ-glutamyltransferase [GGT]) and incident cases of liver disease from hospital records were the outcomes. RESULTS Of the 98 643 individuals recruited, 91 552 (54 299 [45.2%] women; mean [SD] age, 58 [13.05] years) with no baseline liver disease were included in main analyses. Individuals had a mean (SD) BMI of 26.2 (4.3) and consumed a mean (SD) of 10.6 (10.2) U/wk of alcohol. In factorial MR analyses, considering the high BMI/high alcohol group as the reference, mean circulating ALT and GGT levels were lowest in the low BMI/low alcohol group (ALT: -2.32%; 95% CI, -4.29% to -0.35%, and GGT: -3.56%; 95% CI, -5.88% to -1.24%). Individuals with low BMI/high alcohol use and high BMI/low alcohol use also had lower mean circulating ALT levels (low BMI/high alcohol use: -1.31%; 95% CI, -1.88% to -0.73%, and high BMI/low alcohol use: -0.81%; 95% CI, -2.86% to 1.22%) and GGT levels (low BMI/high alcohol use: -0.91%; 95% CI, -1.60% to -0.22%, and high BMI/low alcohol use: -1.13%; 95% CI, -3.55% to 1.30%) compared with the high BMI/high alcohol use reference group. These patterns were similar in multivariable factorial analyses. For incident liver disease (N = 580), factorial MR results were less conclusive (odds ratio of liver disease vs high BMI/high alcohol group: 1.01; 95% CI, 0.84 to 1.18, for the low BMI/high alcohol group, 1.22; 95% CI, 0.56 to 1.88 for the high BMI/low alcohol group, and 0.99 (95% CI, 0.41 to 1.56 for the low BMI/low alcohol group). CONCLUSIONS AND RELEVANCE Interventions to reduce both BMI and alcohol consumption might reduce population levels of biomarkers of liver injury more than interventions aimed at either BMI or alcohol use alone. However, it is not clear whether this intervention will directly translate to a reduced risk of liver disease.
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Affiliation(s)
- Alice R. Carter
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Science, Bristol Medical School, University of Bristol, United Kingdom
| | - Maria-Carolina Borges
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Science, Bristol Medical School, University of Bristol, United Kingdom
| | - Marianne Benn
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen, Denmark
- The Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen, Denmark
- Copenhagen University Hospital, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Anne Tybjærg-Hansen
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen, Denmark
- The Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen, Denmark
- Copenhagen University Hospital, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Science, Bristol Medical School, University of Bristol, United Kingdom
| | - Børge G. Nordestgaard
- The Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen, Denmark
- Copenhagen University Hospital, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen, Denmark
| | - Debbie A. Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Science, Bristol Medical School, University of Bristol, United Kingdom
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Carter AR, France NE, Lewis BW, Shaw DG. Cholesterol ester storage disease. Radiological features. Pediatr Radiol 2005; 2:135-6. [PMID: 15822337 DOI: 10.1007/bf01314945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Abstract
Pathophysiological features of both primary aldosteronism and pseudohyperaldosteronism are hyperactive amiloride-sensitive epithelial Na(+) channels (ENaC) and refractory hypertension. Peripheral blood lymphocytes express ENaC, which functions and is regulated similarly to ENaC expressed by renal principal cells. Thus it was hypothesized that individuals with either of these hypertensive etiologies could be identified by assessment of the function and regulation of peripheral blood lymphocyte ENaC, by whole cell patch clamp. We also tested the hypothesis that specific inhibition of hyperactive ENaC with amiloride could ameliorate the hypertension. To test these hypotheses, we solicited blood samples from normotensive, controlled hypertensive, and refractory hypertensive individuals. Lymphocytes were examined electrophysiologically to determine whether ENaC was hyperactive. All positive findings were from refractory hypertensive individuals. Nine refractory hypertensive patients had amiloride added to their hypertensive therapy. Amiloride normalized the blood pressure of four subjects. These individuals all had hyperactive ENaC. Amiloride had no effect on individuals with normal ENaC. These findings suggest that whole-cell patch clamp of peripheral blood lymphocytes can be used to identify accurately and rapidly hypertensive individuals who will respond to amiloride therapy.
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Affiliation(s)
- A R Carter
- Vascular Biology and Hypertension Program, Division of Cardiovascular Disease, Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama 35294, USA
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Herman AE, Galaburda AM, Fitch RH, Carter AR, Rosen GD. Cerebral microgyria, thalamic cell size and auditory temporal processing in male and female rats. Cereb Cortex 1997; 7:453-64. [PMID: 9261574 DOI: 10.1093/cercor/7.5.453] [Citation(s) in RCA: 92] [Impact Index Per Article: 3.4] [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] [Indexed: 02/05/2023] Open
Abstract
Induction of microgyria by freezing injury to the developing somatosensory cortex of neonatal rats causes a defect in fast auditory processing in males, but not in females. It was speculated that early damage to the cortex has sexually dimorphic cascading effects on other brain regions mediating auditory processing, which can lead to the observed behavioral deficits. In the current series of experiments, bilateral microgyri were induced by placement of a freezing probe on the skulls of newborn male and female rats, and these animals were tested in adulthood for auditory temporal processing. Control animals received sham surgery. The brains from these animals were embedded in celloidin, cut in the coronal plane and the following morphometric measures assessed: microgyric volume, medial geniculate nucleus (MGN) volume, cell number, and cell size, and, as a control, dorsal lateral geniculate nucleus (dLGN) volume, cell number and cell size. There were no sex differences in the cortical pathology of lesioned animals. However, microgyric males had more small and fewer large neurons in the MGN than their sham-operated counterparts, whereas there was no difference between lesioned and sham-operated females. There was no effect on dLGN cell size distribution in either sex. Microgyric males were significantly impaired in fast auditory temporal processing when compared to control males, whereas lesioned females exhibited no behavioral deficits. These results suggest that early injury to the cerebral cortex may have different effects on specific thalamic nuclei in males and females, with corresponding differences in behavioral effects.
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Affiliation(s)
- A E Herman
- Dyslexia Research Laboratory, Beth Israel Deaconess Medical Center, Beth Israel Hospital, Boston, MA 02215, USA
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Abstract
In a prospective study ultrasound was compared to palpation in 41 instances in which a testis was not present in the scrotum. A testis was palpable in 20 of these instances and not palpated in 21. Of 20 palpable undescended testes 14 (70 per cent) were identified by ultrasound. Of the 21 instances in which a testis was not palpated 3 intra-abdominal and 5 inguinal testes were identified at exploration. One of these organs (an inguinal testis) was identified by ultrasound. Two false positive sonograms in which a gubernacular structure mimicked an undescended testis occurred. Sonography cannot satisfactorily stand alone as a screening modality in the management of the undescended testis.
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Abstract
Three cases of cavernous transformation of the portal vein are presented, which emphasize the value of duplex Doppler sonography in the recognition of abnormal vascular structures. In all three cases, cavernous transformation was unsuspected; in two, the initial sonographic or CT examinations were interpreted incorrectly. These cases suggest that the combination of characteristic pulsed-Doppler waveforms and the real-time appearance of cavernous transformation is virtually diagnostic.
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Abstract
A triangular echogenic area in the upper pole renal parenchyma can be identified at times during routine sonography of the right kidney. Thirty such cases are presented. Occasionally similar echogenic defects in the parenchyma can be seen posteriorly in the lower pole and in the left kidney. These defects in the parenchyma result from normal extensions of the renal sinus of kidneys that have a distinct division of their upper and lower poles. This is due to partial fusion of two embryonic parenchymatous masses called renunculi. The defects in the parenchyma occur at the junction of the renunculi; hence we have termed them junctional parenchymal defects. In order to differentiate them from pathologic conditions, one must identify their characteristic location and demonstrate continuity with the renal sinus.
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Abstract
A series of 92 adult patients undergoing elective cardiac surgery was reviewed to distinguish routine postoperative radiographic alterations from signs of clinically significant complications. Two postoperative complications required decisive clinical intervention: mediastinal hemorrhage (7% of cases) and sternal wound infections (3% of cases). Mediastinal hemorrhage was most often diagnosed by excessive bloody mediastinal tube drainage alone, although progressive mediastinal widening and pleural or apical extrapleural hematomas provided corroborating or, rarely, the sole evidence of mediastinal hemorrhage. Sternal wound infections were most often diagnosed clinically, but increasing pre- and retrosternal gas collections provided radiographic confirmation. Atelectasis was the most common postoperative finding. There were many abnormal gas and soft-tissue collections posteroperatively that were notable for their lack of clinical importance. Serial postoperative films were necessary to demonstrate the progression of radiographic findings which indicate the two important postoperative complications.
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Abstract
Two patients with rheumatoid arthritis are described, who developed very large bone cysts or geodes adjacent to the knee-joint. The existence of cysts adjacent to joints involved by rheumatoid arthritis is well recognised, but the occurrence of very large cysts is unusual and may present diagnostic difficulties. Possible aetiological factors are discussed.
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Krikler DM, Zilberg B, Carter AR. Upper limb-cardiovascular syndrome. S Afr Med J 1969; 43:897-900. [PMID: 5821602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
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