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Lau CE, Manou M, Markozannes G, Ala‐Korpela M, Ben‐Shlomo Y, Chaturvedi N, Engmann J, Gentry‐Maharaj A, Herzig K, Hingorani A, Järvelin M, Kähönen M, Kivimäki M, Lehtimäki T, Marttila S, Menon U, Munroe PB, Palaniswamy S, Providencia R, Raitakari O, Schmidt AF, Sebert S, Wong A, Vineis P, Tzoulaki I, Robinson O. NMR metabolomic modeling of age and lifespan: A multicohort analysis. Aging Cell 2024; 23:e14164. [PMID: 38637937 PMCID: PMC11258446 DOI: 10.1111/acel.14164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 03/05/2024] [Accepted: 03/19/2024] [Indexed: 04/20/2024] Open
Abstract
Metabolomic age models have been proposed for the study of biological aging, however, they have not been widely validated. We aimed to assess the performance of newly developed and existing nuclear magnetic resonance spectroscopy (NMR) metabolomic age models for prediction of chronological age (CA), mortality, and age-related disease. Ninety-eight metabolic variables were measured in blood from nine UK and Finnish cohort studies (N ≈31,000 individuals, age range 24-86 years). We used nonlinear and penalized regression to model CA and time to all-cause mortality. We examined associations of four new and two previously published metabolomic age models, with aging risk factors and phenotypes. Within the UK Biobank (N ≈102,000), we tested prediction of CA, incident disease (cardiovascular disease (CVD), type-2 diabetes mellitus, cancer, dementia, and chronic obstructive pulmonary disease), and all-cause mortality. Seven-fold cross-validated Pearson's r between metabolomic age models and CA ranged between 0.47 and 0.65 in the training cohort set (mean absolute error: 8-9 years). Metabolomic age models, adjusted for CA, were associated with C-reactive protein, and inversely associated with glomerular filtration rate. Positively associated risk factors included obesity, diabetes, smoking, and physical inactivity. In UK Biobank, correlations of metabolomic age with CA were modest (r = 0.29-0.33), yet all metabolomic model scores predicted mortality (hazard ratios of 1.01 to 1.06/metabolomic age year) and CVD, after adjustment for CA. While metabolomic age models were only moderately associated with CA in an independent population, they provided additional prediction of morbidity and mortality over CA itself, suggesting their wider applicability.
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Affiliation(s)
- Chung‐Ho E. Lau
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public HealthImperial College LondonLondonUK
| | - Maria Manou
- Department of Hygiene and EpidemiologyUniversity of Ioannina Medical SchoolIoanninaGreece
| | - Georgios Markozannes
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public HealthImperial College LondonLondonUK
- Department of Hygiene and EpidemiologyUniversity of Ioannina Medical SchoolIoanninaGreece
| | - Mika Ala‐Korpela
- Systems Epidemiology, Faculty of MedicineUniversity of OuluOuluFinland
- Research Unit of Population Health, Faculty of MedicineUniversity of OuluOuluFinland
- Biocenter OuluUniversity of OuluOuluFinland
- NMR Metabolomics Laboratory, School of Pharmacy, Faculty of Health SciencesUniversity of Eastern FinlandKuopioFinland
| | | | - Nish Chaturvedi
- MRC Unit for Lifelong Health and Ageing at UCLUniversity College LondonLondonUK
| | - Jorgen Engmann
- UCL Institute of Cardiovascular Science, Population Science and Experimental Medicine, Centre for Translational GenomicsLondonUK
| | - Aleksandra Gentry‐Maharaj
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and MethodologyUniversity College LondonLondonUK
- Department of Women's Cancer, Elizabeth Garrett Anderson Institute for Women's HealthUniversity College LondonLondonUK
| | - Karl‐Heinz Herzig
- Institute of Biomedicine and Internal Medicine, Biocenter of Oulu, Medical Research Center Oulu, Oulu University Hospital, Faculty of MedicineOulu UniversityOuluFinland
- Department of Pediatric Gastroenterology and Metabolic DiseasesPoznan University of Medical SciencesPoznanPoland
| | - Aroon Hingorani
- UCL Institute of Cardiovascular Science, Population Science and Experimental Medicine, Centre for Translational GenomicsLondonUK
| | - Marjo‐Riitta Järvelin
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public HealthImperial College LondonLondonUK
- Research Unit of Population Health, Faculty of MedicineUniversity of OuluOuluFinland
- Department of Life Sciences, College of Health and Life SciencesBrunel University LondonLondonUK
| | - Mika Kähönen
- Department of Clinical PhysiologyTampere University HospitalTampereFinland
- Faculty of Medicine and Health TechnologyTampere UniversityTampereFinland
| | | | - Terho Lehtimäki
- Faculty of Medicine and Health Technology and Finnish Cardiovascular Research Center TampereTampere UniversityTampereFinland
- Department of Clinical Chemistry Fimlab LaboratoriesTampereFinland
| | - Saara Marttila
- Molecular Epidemiology, Faculty of Medicine and Health TechnologyTampere UniversityTampereFinland
- Gerontology Research Center (GEREC)Tampere UniversityTampereFinland
| | - Usha Menon
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and MethodologyUniversity College LondonLondonUK
| | - Patricia B. Munroe
- William Harvey Research Institute, Barts and the London Faculty of Medicine and DentistryQueen Mary University of LondonLondonUK
- National Institute of Health and Care Research, Barts Cardiovascular Biomedical Research CentreQueen Mary University of LondonLondonUK
| | - Saranya Palaniswamy
- Research Unit of Population Health, Faculty of MedicineUniversity of OuluOuluFinland
| | - Rui Providencia
- Institute of Health Informatics Research, University College LondonLondonUK
- Barts Heart Centre, Barts Health NHS TrustLondonUK
| | - Olli Raitakari
- Centre for Population Health Research, University of Turku and Turku University HospitalTurkuFinland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of TurkuTurkuFinland
- Department of Clinical Physiology and Nuclear MedicineTurku University HospitalTurkuFinland
| | - Amand Floriaan Schmidt
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College LondonLondonUK
- Department of Cardiology, Amsterdam Cardiovascular Science, Amsterdam University Medical CentersUniversity of AmsterdamAmsterdamThe Netherlands
- UCL BHF Research Accelerator CentreLondonUK
| | - Sylvain Sebert
- Research Unit of Population Health, Faculty of MedicineUniversity of OuluOuluFinland
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCLUniversity College LondonLondonUK
| | - Paolo Vineis
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public HealthImperial College LondonLondonUK
| | - Ioanna Tzoulaki
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public HealthImperial College LondonLondonUK
- Biomedical Research Foundation, Academy of AthensAthensGreece
| | - Oliver Robinson
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public HealthImperial College LondonLondonUK
- Ageing Epidemiology (AGE) Research Unit, School of Public HealthImperial College LondonLondonUK
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2
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Sae-Jie W, Supasai S, Kivimaki M, Price JF, Wong A, Kumari M, Engmann J, Shah T, Schmidt AF, Gaunt TR, Hingorani A, Charoen P. Triangulating evidence from observational and Mendelian randomization studies of ketone bodies for cognitive performance. BMC Med 2023; 21:340. [PMID: 37667256 PMCID: PMC10478491 DOI: 10.1186/s12916-023-03047-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 08/24/2023] [Indexed: 09/06/2023] Open
Abstract
BACKGROUND Ketone bodies (KBs) are an alternative energy supply for brain functions when glucose is limited. The most abundant ketone metabolite, 3-β-hydroxybutyrate (BOHBUT), has been suggested to prevent or delay cognitive impairment, but the evidence remains unclear. We triangulated observational and Mendelian randomization (MR) studies to investigate the association and causation between KBs and cognitive function. METHODS In observational analyses of 5506 participants aged ≥ 45 years from the Whitehall II study, we used multiple linear regression to investigate the associations between categorized KBs and cognitive function scores. Two-sample MR was carried out using summary statistics from an in-house KBs meta-analysis between the University College London-London School of Hygiene and Tropical Medicine-Edinburgh-Bristol (UCLEB) Consortium and Kettunen et al. (N = 45,031), and publicly available summary statistics of cognitive performance and Alzheimer's disease (AD) from the Social Science Genetic Association Consortium (N = 257,841), and the International Genomics of Alzheimer's Project (N = 54,162), respectively. Both strong (P < 5 × 10-8) and suggestive (P < 1 × 10-5) sets of instrumental variables for BOHBUT were applied. Finally, we performed cis-MR on OXCT1, a well-known gene for KB catabolism. RESULTS BOHBUT was positively associated with general cognitive function (β = 0.26, P = 9.74 × 10-3). In MR analyses, we observed a protective effect of BOHBUT on cognitive performance (inverse variance weighted: βIVW = 7.89 × 10-2, PIVW = 1.03 × 10-2; weighted median: βW-Median = 8.65 × 10-2, PW-Median = 9.60 × 10-3) and a protective effect on AD (βIVW = - 0.31, odds ratio: OR = 0.74, PIVW = 3.06 × 10-2). Cis-MR showed little evidence of therapeutic modulation of OXCT1 on cognitive impairment. CONCLUSIONS Triangulation of evidence suggests that BOHBUT has a beneficial effect on cognitive performance. Our findings raise the hypothesis that increased BOHBUT may improve general cognitive functions, delaying cognitive impairment and reducing the risk of AD.
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Affiliation(s)
- Wichanon Sae-Jie
- Department of Mathematics, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
| | - Suangsuda Supasai
- Department of Molecular Tropical Medicine and Genetics, Faculty of Tropical Medicine, Mahidol University, Bangkok, 10400, Thailand
| | - Mika Kivimaki
- UCL Brain Sciences, University College London, 149 Tottenham Court Road, London, W1T 7NF, UK
| | - Jackie F Price
- Usher Institute, University of Edinburgh, Edinburgh, EH8 9AG, UK
| | - Andrew Wong
- MRC Unit Lifelong Health and Ageing at UCL, London, UK
| | - Meena Kumari
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, WC1E 6BT, UK
- Institute for Social and Economic Research, University of Essex, Colchester, UK
| | - Jorgen Engmann
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, WC1E 6BT, UK
| | - Tina Shah
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, WC1E 6BT, UK
| | - Amand F Schmidt
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, WC1E 6BT, UK
- UCL British Heart Foundation Research Accelerator, Department of Cardiology, Division Heart and Lungs, University College London, London, UK
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol National Health Service Foundation Trust and University of Bristol, Bristol, UK
| | - Aroon Hingorani
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, WC1E 6BT, UK
| | - Pimphen Charoen
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, WC1E 6BT, UK.
- Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Ratchawithi Road, Ratchathewi, Bangkok, 10400, Thailand.
- Integrative Computational Bioscience (ICBS) Center, Mahidol University, Bangkok, Thailand.
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3
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Schmidt AF, Bourfiss M, Alasiri A, Puyol-Anton E, Chopade S, van Vugt M, van der Laan SW, Gross C, Clarkson C, Henry A, Lumbers TR, van der Harst P, Franceschini N, Bis JC, Velthuis BK, te Riele AS, Hingorani AD, Ruijsink B, Asselbergs FW, van Setten J, Finan C. Druggable proteins influencing cardiac structure and function: Implications for heart failure therapies and cancer cardiotoxicity. SCIENCE ADVANCES 2023; 9:eadd4984. [PMID: 37126556 PMCID: PMC10132758 DOI: 10.1126/sciadv.add4984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 03/24/2023] [Indexed: 05/03/2023]
Abstract
Dysfunction of either the right or left ventricle can lead to heart failure (HF) and subsequent morbidity and mortality. We performed a genome-wide association study (GWAS) of 16 cardiac magnetic resonance (CMR) imaging measurements of biventricular function and structure. Cis-Mendelian randomization (MR) was used to identify plasma proteins associating with CMR traits as well as with any of the following cardiac outcomes: HF, non-ischemic cardiomyopathy, dilated cardiomyopathy (DCM), atrial fibrillation, or coronary heart disease. In total, 33 plasma proteins were prioritized, including repurposing candidates for DCM and/or HF: IL18R (providing indirect evidence for IL18), I17RA, GPC5, LAMC2, PA2GA, CD33, and SLAF7. In addition, 13 of the 25 druggable proteins (52%; 95% confidence interval, 0.31 to 0.72) could be mapped to compounds with known oncological indications or side effects. These findings provide leads to facilitate drug development for cardiac disease and suggest that cardiotoxicities of several cancer treatments might represent mechanism-based adverse effects.
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Affiliation(s)
- Amand F. Schmidt
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- UCL BHF Research Accelerator Centre, London, UK
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, Netherlands
| | - Mimount Bourfiss
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Abdulrahman Alasiri
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- Medical Genomics Research Department, King Abdullah International Medical Research Center, King Saud Bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Esther Puyol-Anton
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, UK
| | - Sandesh Chopade
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- UCL BHF Research Accelerator Centre, London, UK
| | - Marion van Vugt
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- UCL BHF Research Accelerator Centre, London, UK
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, Netherlands
| | - Sander W. van der Laan
- Central Diagnostics Laboratory, Division Laboratory, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Christian Gross
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Chris Clarkson
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- UCL BHF Research Accelerator Centre, London, UK
| | - Albert Henry
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- UCL BHF Research Accelerator Centre, London, UK
- Institute of Health Informatics, Faculty of Population Health, University College London, London, UK
| | - Tom R. Lumbers
- UCL BHF Research Accelerator Centre, London, UK
- Institute of Health Informatics, Faculty of Population Health, University College London, London, UK
| | - Pim van der Harst
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Nora Franceschini
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Joshua C. Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Birgitta K. Velthuis
- Department of Radiology, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Anneline S. J. M. te Riele
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- Netherlands Heart Institute, Utrecht, Netherlands
- Member of the European Reference Network for rare, low prevalence, and complex diseases of the heart (ERN GUARD HEART; http://guardheart.ern-net.eu)
| | - Aroon D. Hingorani
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- UCL BHF Research Accelerator Centre, London, UK
| | - Bram Ruijsink
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, UK
| | - Folkert W. Asselbergs
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, Netherlands
- Institute of Health Informatics, Faculty of Population Health, University College London, London, UK
- Member of the European Reference Network for rare, low prevalence, and complex diseases of the heart (ERN GUARD HEART; http://guardheart.ern-net.eu)
| | - Jessica van Setten
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Chris Finan
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- UCL BHF Research Accelerator Centre, London, UK
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
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4
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Schmidt AF, Joshi R, Gordillo-Marañón M, Drenos F, Charoen P, Giambartolomei C, Bis JC, Gaunt TR, Hughes AD, Lawlor DA, Wong A, Price JF, Chaturvedi N, Wannamethee G, Franceschini N, Kivimaki M, Hingorani AD, Finan C. Biomedical consequences of elevated cholesterol-containing lipoproteins and apolipoproteins on cardiovascular and non-cardiovascular outcomes. COMMUNICATIONS MEDICINE 2023; 3:9. [PMID: 36670186 PMCID: PMC9859819 DOI: 10.1038/s43856-022-00234-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 12/22/2022] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Higher concentrations of cholesterol-containing low-density lipoprotein (LDL-C) increase the risk of cardiovascular disease (CVD). The association of LDL-C with non-CVD traits remains unclear, as are the possible independent contributions of other cholesterol-containing lipoproteins and apolipoproteins. METHODS Nuclear magnetic resonance spectroscopy was used to measure the cholesterol content of high density (HDL-C), very low-density (VLDL-C), intermediate-density (IDL-C), as well as low-density lipoprotein fractions, the apolipoproteins Apo-A1 and Apo-B, as well as total triglycerides (TG), remnant-cholesterol (Rem-Chol) and total cholesterol (TC). The causal effects of these exposures were assessed against 33 outcomes using univariable and multivariable Mendelian randomization (MR). RESULTS The majority of cholesterol containing lipoproteins and apolipoproteins affect coronary heart disease (CHD), carotid intima-media thickness, carotid plaque, C-reactive protein (CRP) and blood pressure. Multivariable MR indicated that many of these effects act independently of HDL-C, LDL-C and TG, the most frequently measured lipid fractions. Higher concentrations of TG, VLDL-C, Rem-Chol and Apo-B increased heart failure (HF) risk; often independently of LDL-C, HDL-C or TG. Finally, a subset of these exposures associated with non-CVD traits such as Alzheimer's disease (AD: HDL-C, LDL-C, IDL-C, Apo-B), type 2 diabetes (T2DM: VLDL-C, IDL-C, LDL-C), and inflammatory bowel disease (IBD: LDL-C, IDL-C). CONCLUSIONS The cholesterol content of a wide range of lipoprotein and apolipoproteins associate with measures of atherosclerosis, blood pressure, CRP, and CHD, with a subset affecting HF, T2DM, AD and IBD risk. Many of the observed effects appear to act independently of LDL-C, HDL-C, and TG, supporting the targeting of lipid fractions beyond LDL-C for disease prevention.
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Affiliation(s)
- Amand F Schmidt
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK.
- UCL BHF Research Accelerator Centre, London, UK.
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
- Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, The Netherlands.
| | - Roshni Joshi
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- UCL BHF Research Accelerator Centre, London, UK
| | - Maria Gordillo-Marañón
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- UCL BHF Research Accelerator Centre, London, UK
| | - Fotios Drenos
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- Department of Life Sciences, College of Health, Medicine and Life Sciences, Brunel University London, Uxbridge, UK
| | - Pimphen Charoen
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Bangkok, 10400, Thailand
- Integrative Computational BioScience (ICBS) Center, Mahidol University, Bangkok, 10400, Thailand
| | - Claudia Giambartolomei
- Istituto Italiano di Tecnologia, Non-coding RNAs and RNA-based Therapeutics, Via Morego, 30, 16163, Genova, Italy
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol National Health Service Foundation Trust and University of Bristol, Bristol, UK
| | - Alun D Hughes
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- UCL BHF Research Accelerator Centre, London, UK
- MRC Unit for Lifelong Health and Ageing, University College London, London, UK
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol National Health Service Foundation Trust and University of Bristol, Bristol, UK
- Population Health, Bristol Medical School, University of Bristol, Bristol, UK
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing, University College London, London, UK
| | | | - Nishi Chaturvedi
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- MRC Unit for Lifelong Health and Ageing, University College London, London, UK
| | - Goya Wannamethee
- Primary Care and Population Health, University College London, London, UK
| | - Nora Franceschini
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Mika Kivimaki
- Department of Mental Health of Older People, Division of Brain Sciences, University College London, London, UK
| | - Aroon D Hingorani
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- UCL BHF Research Accelerator Centre, London, UK
| | - Chris Finan
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- UCL BHF Research Accelerator Centre, London, UK
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
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5
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Gordillo-Marañón M, Zwierzyna M, Charoen P, Drenos F, Chopade S, Shah T, Engmann J, Chaturvedi N, Papacosta O, Wannamethee G, Wong A, Sofat R, Kivimaki M, Price JF, Hughes AD, Gaunt TR, Lawlor DA, Gaulton A, Hingorani AD, Schmidt AF, Finan C. Validation of lipid-related therapeutic targets for coronary heart disease prevention using human genetics. Nat Commun 2021; 12:6120. [PMID: 34675202 PMCID: PMC8531035 DOI: 10.1038/s41467-021-25731-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Accepted: 08/26/2021] [Indexed: 12/14/2022] Open
Abstract
Drug target Mendelian randomization (MR) studies use DNA sequence variants in or near a gene encoding a drug target, that alter the target's expression or function, as a tool to anticipate the effect of drug action on the same target. Here we apply MR to prioritize drug targets for their causal relevance for coronary heart disease (CHD). The targets are further prioritized using independent replication, co-localization, protein expression profiles and data from the British National Formulary and clinicaltrials.gov. Out of the 341 drug targets identified through their association with blood lipids (HDL-C, LDL-C and triglycerides), we robustly prioritize 30 targets that might elicit beneficial effects in the prevention or treatment of CHD, including NPC1L1 and PCSK9, the targets of drugs used in CHD prevention. We discuss how this approach can be generalized to other targets, disease biomarkers and endpoints to help prioritize and validate targets during the drug development process.
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Affiliation(s)
- María Gordillo-Marañón
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, WC1E 6BT, UK.
| | - Magdalena Zwierzyna
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, WC1E 6BT, UK
- UCL British Heart Foundation Research Accelerator, London, UK
| | - Pimphen Charoen
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, WC1E 6BT, UK
- Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Bangkok, 10400, Thailand
- Integrative Computational BioScience (ICBS) Center, Mahidol University, Bangkok, 10400, Thailand
| | - Fotios Drenos
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, WC1E 6BT, UK
- Department of Life Sciences, College of Health, Medicine, and Life Sciences, Brunel University London, Uxbridge, UK
| | - Sandesh Chopade
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, WC1E 6BT, UK
- UCL British Heart Foundation Research Accelerator, London, UK
| | - Tina Shah
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, WC1E 6BT, UK
- UCL British Heart Foundation Research Accelerator, London, UK
| | - Jorgen Engmann
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, WC1E 6BT, UK
- UCL British Heart Foundation Research Accelerator, London, UK
| | - Nishi Chaturvedi
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, WC1E 6BT, UK
- MRC Unit for Lifelong Health and Ageing, University College London, London, WC1E 7HB, UK
| | - Olia Papacosta
- Primary Care and Population Health, University College London, London, NW3 2PF, UK
| | - Goya Wannamethee
- Primary Care and Population Health, University College London, London, NW3 2PF, UK
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing, University College London, London, WC1E 7HB, UK
| | - Reecha Sofat
- Institute of Health Informatics, University College London, London, WC1E 6BT, UK
| | - Mika Kivimaki
- Department of Epidemiology and Public Health, University College London, London, WC1E 6BT, UK
| | - Jackie F Price
- Usher Institute, University of Edinburgh, Edinburgh, EH8 9AG, UK
| | - Alun D Hughes
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, WC1E 6BT, UK
- UCL British Heart Foundation Research Accelerator, London, UK
- MRC Unit for Lifelong Health and Ageing, University College London, London, WC1E 7HB, UK
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, BS8 2BN, UK
- Population Health, Bristol Medical School, University of Bristol, Bristol, BS8 2PS, UK
- Bristol NIHR Bristol Biomedical Research Centre, University Hospitals Bristol National Health Service Foundation Trust and University of Bristol, Bristol, BS8 2BN, UK
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, BS8 2BN, UK
- Population Health, Bristol Medical School, University of Bristol, Bristol, BS8 2PS, UK
- Bristol NIHR Bristol Biomedical Research Centre, University Hospitals Bristol National Health Service Foundation Trust and University of Bristol, Bristol, BS8 2BN, UK
| | - Anna Gaulton
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Aroon D Hingorani
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, WC1E 6BT, UK
- UCL British Heart Foundation Research Accelerator, London, UK
| | - Amand F Schmidt
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, WC1E 6BT, UK
- UCL British Heart Foundation Research Accelerator, London, UK
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Chris Finan
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, WC1E 6BT, UK
- UCL British Heart Foundation Research Accelerator, London, UK
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
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6
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Schmidt AF, Hunt NB, Gordillo-Marañón M, Charoen P, Drenos F, Kivimaki M, Lawlor DA, Giambartolomei C, Papacosta O, Chaturvedi N, Bis JC, O'Donnell CJ, Wannamethee G, Wong A, Price JF, Hughes AD, Gaunt TR, Franceschini N, Mook-Kanamori DO, Zwierzyna M, Sofat R, Hingorani AD, Finan C. Cholesteryl ester transfer protein (CETP) as a drug target for cardiovascular disease. Nat Commun 2021; 12:5640. [PMID: 34561430 PMCID: PMC8463530 DOI: 10.1038/s41467-021-25703-3] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 08/23/2021] [Indexed: 02/08/2023] Open
Abstract
Development of cholesteryl ester transfer protein (CETP) inhibitors for coronary heart disease (CHD) has yet to deliver licensed medicines. To distinguish compound from drug target failure, we compared evidence from clinical trials and drug target Mendelian randomization of CETP protein concentration, comparing this to Mendelian randomization of proprotein convertase subtilisin/kexin type 9 (PCSK9). We show that previous failures of CETP inhibitors are likely compound related, as illustrated by significant degrees of between-compound heterogeneity in effects on lipids, blood pressure, and clinical outcomes observed in trials. On-target CETP inhibition, assessed through Mendelian randomization, is expected to reduce the risk of CHD, heart failure, diabetes, and chronic kidney disease, while increasing the risk of age-related macular degeneration. In contrast, lower PCSK9 concentration is anticipated to decrease the risk of CHD, heart failure, atrial fibrillation, chronic kidney disease, multiple sclerosis, and stroke, while potentially increasing the risk of Alzheimer's disease and asthma. Due to distinct effects on lipoprotein metabolite profiles, joint inhibition of CETP and PCSK9 may provide added benefit. In conclusion, we provide genetic evidence that CETP is an effective target for CHD prevention but with a potential on-target adverse effect on age-related macular degeneration.
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Affiliation(s)
- Amand F Schmidt
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK.
- UCL British Heart Foundation Research Accelerator, London, UK.
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Nicholas B Hunt
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences (UIPS), Utrecht University, Utrecht, The Netherlands
| | - Maria Gordillo-Marañón
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- UCL British Heart Foundation Research Accelerator, London, UK
| | - Pimphen Charoen
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
- Integrative Computational BioScience (ICBS) Center, Mahidol University, Bangkok, Thailand
| | - Fotios Drenos
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- Department of Life Sciences, College of Health, Medicine, and Life Sciences, Brunel University London, Uxbridge, UK
| | - Mika Kivimaki
- Department of Epidemiology and Public Health, University College London, London, UK
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health, Bristol Medical School, University of Bristol, Bristol, UK
- Bristol NIHR Bristol Biomedical Research Centre, University Hospitals Bristol National Health Service Foundation Trust and University of Bristol, Bristol, UK
| | | | - Olia Papacosta
- Primary Care and Population Health, University College London, London, UK
| | - Nishi Chaturvedi
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- MRC Unit for Lifelong Health and Ageing at UCL, London, UK
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Christopher J O'Donnell
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Medicine, VA Boston Healthcare System, Boston, MA, USA
| | - Goya Wannamethee
- Primary Care and Population Health, University College London, London, UK
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, London, UK
| | | | - Alun D Hughes
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- UCL British Heart Foundation Research Accelerator, London, UK
- MRC Unit for Lifelong Health and Ageing at UCL, London, UK
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health, Bristol Medical School, University of Bristol, Bristol, UK
- Bristol NIHR Bristol Biomedical Research Centre, University Hospitals Bristol National Health Service Foundation Trust and University of Bristol, Bristol, UK
| | - Nora Franceschini
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Magdalena Zwierzyna
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- UCL British Heart Foundation Research Accelerator, London, UK
| | - Reecha Sofat
- Institute of Health Informatics, University College London, London, UK
| | - Aroon D Hingorani
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- UCL British Heart Foundation Research Accelerator, London, UK
- Health Data Research UK, London, UK
| | - Chris Finan
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- UCL British Heart Foundation Research Accelerator, London, UK
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht, The Netherlands
- Health Data Research UK, London, UK
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7
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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: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [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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8
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Lord J, Jermy B, Green R, Wong A, Xu J, Legido-Quigley C, Dobson R, Richards M, Proitsi P. Mendelian randomization identifies blood metabolites previously linked to midlife cognition as causal candidates in Alzheimer's disease. Proc Natl Acad Sci U S A 2021; 118:e2009808118. [PMID: 33879569 PMCID: PMC8072203 DOI: 10.1073/pnas.2009808118] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 02/23/2021] [Indexed: 12/29/2022] Open
Abstract
There are currently no disease-modifying treatments for Alzheimer's disease (AD), and an understanding of preclinical causal biomarkers to help target disease pathogenesis in the earliest phases remains elusive. Here, we investigated whether 19 metabolites previously associated with midlife cognition-a preclinical predictor of AD-translate to later clinical risk, using Mendelian randomization (MR) to tease out AD-specific causal relationships. Summary statistics from the largest genome-wide association studies (GWASs) for AD and metabolites were used to perform bidirectional univariable MR. Bayesian model averaging (BMA) was additionally performed to address high correlation between metabolites and identify metabolite combinations that may be on the AD causal pathway. Univariable MR indicated four extra-large high-density lipoproteins (XL.HDL) on the causal pathway to AD: free cholesterol (XL.HDL.FC: 95% CI = 0.78 to 0.94), total lipids (XL.HDL.L: 95% CI = 0.80 to 0.97), phospholipids (XL.HDL.PL: 95% CI = 0.81 to 0.97), and concentration of XL.HDL particles (95% CI = 0.79 to 0.96), significant at an adjusted P < 0.009. MR-BMA corroborated XL.HDL.FC to be among the top three causal metabolites, in addition to total cholesterol in XL.HDL (XL.HDL.C) and glycoprotein acetyls (GP). Both XL.HDL.C and GP demonstrated suggestive univariable evidence of causality (P < 0.05), and GP successfully replicated within an independent dataset. This study offers insight into the causal relationship between metabolites demonstrating association with midlife cognition and AD. It highlights GP in addition to several XL.HDLs-particularly XL.HDL.FC-as causal candidates warranting further investigation. As AD pathology is thought to develop decades prior to symptom onset, expanding on these findings could inform risk reduction strategies.
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Affiliation(s)
- Jodie Lord
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 5AF, United Kingdom
| | - Bradley Jermy
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, United Kingdom
- National Institute for Health Research Maudsley Biomedical Research Centre, South London and Maudsley National Health Service (NHS) Foundation Trust, London, SE5 8AF, United Kingdom
| | - Rebecca Green
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 5AF, United Kingdom
- National Institute for Health Research Maudsley Biomedical Research Centre, South London and Maudsley National Health Service (NHS) Foundation Trust, London, SE5 8AF, United Kingdom
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, WC1E 7HB, United Kingdom
| | - Jin Xu
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 5AF, United Kingdom
- Institute of Pharmaceutical Science, King's College London, London, SE1 9NH, United Kingdom
| | - Cristina Legido-Quigley
- Institute of Pharmaceutical Science, King's College London, London, SE1 9NH, United Kingdom
- Systems Medicine, Steno Diabetes Centre Copenhagen, 2820 Gentofte, Denmark
| | - Richard Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AF, United Kingdom
- National Institute for Health Research Biomedical Research at South London and Maudsley NHS Foundation Trust and King's College London, London, SE5 8AF, United Kingdom
- Health Data Research UK London, University College London, London, NW1 2DA, United Kingdom
- Institute of Health Informatics, University College London, London, NW1 2DA, United Kingdom
- National Institute for Health Research Biomedical Research Centre at University College London Hospitals NHS Foundation Trust, London, NW1 2DA, United Kingdom
| | - Marcus Richards
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, WC1E 7HB, United Kingdom;
| | - Petroula Proitsi
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 5AF, United Kingdom;
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9
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Joshi R, Wannamethee G, Engmann J, Gaunt T, Lawlor DA, Price J, Papacosta O, Shah T, Tillin T, Whincup P, Chaturvedi N, Kivimaki M, Kuh D, Kumari M, Hughes AD, Casas JP, Humphries SE, Hingorani AD, Schmidt AF. Establishing reference intervals for triglyceride-containing lipoprotein subfraction metabolites measured using nuclear magnetic resonance spectroscopy in a UK population. Ann Clin Biochem 2020; 58:47-53. [PMID: 32936666 PMCID: PMC7791273 DOI: 10.1177/0004563220961753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Nuclear magnetic resonance (NMR) spectroscopy allows triglycerides to be subclassified into 14 different classes based on particle size and lipid content. We recently showed that these subfractions have differential associations with cardiovascular disease events. Here we report the distributions and define reference interval ranges for 14 triglyceride-containing lipoprotein subfraction metabolites. METHODS Lipoprotein subfractions using the Nightingale NMR platform were measured in 9073 participants from four cohort studies contributing to the UCL-Edinburgh-Bristol consortium. The distribution of each metabolite was assessed, and reference interval ranges were calculated for a disease-free population, by sex and age group (<55, 55-65, >65 years), and in a subgroup population of participants with cardiovascular disease or type 2 diabetes. We also determined the distribution across body mass index and smoking status. RESULTS The largest reference interval range was observed in the medium very-low density lipoprotein subclass (2.5th 97.5th percentile; 0.08 to 0.68 mmol/L). The reference intervals were comparable among male and female participants, with the exception of triglyceride in high-density lipoprotein. Triglyceride subfraction concentrations in very-low density lipoprotein, intermediate-density lipoprotein, low-density lipoprotein and high-density lipoprotein subclasses increased with increasing age and increasing body mass index. Triglyceride subfraction concentrations were significantly higher in ever smokers compared to never smokers, among those with clinical chemistry measured total triglyceride greater than 1.7 mmol/L, and in those with cardiovascular disease, and type 2 diabetes as compared to disease-free subjects. CONCLUSION This is the first study to establish reference interval ranges for 14 triglyceride-containing lipoprotein subfractions in samples from the general population measured using the nuclear magnetic resonance platform. The utility of nuclear magnetic resonance lipid measures may lead to greater insights for the role of triglyceride in cardiovascular disease, emphasizing the importance of appropriate reference interval ranges for future clinical decision making.
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Affiliation(s)
- Roshni Joshi
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
| | - Goya Wannamethee
- Department of Primary Care & Population Health, Faculty of Population Health, University College London, London, UK
| | - Jorgen Engmann
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
| | - Tom Gaunt
- Department of Primary Care & Population Health, Faculty of Population Health, University College London, London, UK
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.,Bristol NIHR Biomedical Research Centre, Bristol, UK.,Population Health Science, Bristol Medical School, Bristol, UK
| | - Jackie Price
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Olia Papacosta
- Department of Primary Care & Population Health, Faculty of Population Health, University College London, London, UK
| | - Tina Shah
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
| | - Therese Tillin
- Department of Epidemiology and Public Health, University College London, London, UK
| | - Peter Whincup
- Population Health Research Institute, St George's, University of London, London, UK
| | - Nishi Chaturvedi
- MRC Unit for Lifelong Health and Ageing, Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK
| | - Mika Kivimaki
- Department of Epidemiology and Public Health, University College London, London, UK
| | - Diana Kuh
- MRC Unit for Lifelong Health and Ageing, Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK
| | - Meena Kumari
- Institute for Social and Economic Research, University of Essex, Colchester, UK
| | - Alun D Hughes
- MRC Unit for Lifelong Health and Ageing, Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK
| | - Juan P Casas
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare, MA, USA.,Division of Aging, Department of Medicine, Brigham and Women's Hospital and Harvard School of Medicine, Boston, MA, USA
| | - Steve E Humphries
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
| | - Aroon D Hingorani
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
| | - A Floriaan Schmidt
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK.,Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht, the Netherlands
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10
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Borges MC, Schmidt AF, Jefferis B, Wannamethee SG, Lawlor DA, Kivimaki M, Kumari M, Gaunt TR, Ben-Shlomo Y, Tillin T, Menon U, Providencia R, Dale C, Gentry-Maharaj A, Hughes A, Chaturvedi N, Casas JP, Hingorani AD. Circulating Fatty Acids and Risk of Coronary Heart Disease and Stroke: Individual Participant Data Meta-Analysis in Up to 16 126 Participants. J Am Heart Assoc 2020; 9:e013131. [PMID: 32114887 PMCID: PMC7335585 DOI: 10.1161/jaha.119.013131] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Background We aimed at investigating the association of circulating fatty acids with coronary heart disease (CHD) and stroke risk. Methods and Results We conducted an individual-participant data meta-analysis of 5 UK-based cohorts and 1 matched case-control study. Fatty acids (ie, omega-3 docosahexaenoic acid, omega-6 linoleic acid, monounsaturated and saturated fatty acids) were measured at baseline using an automated high-throughput serum nuclear magnetic resonance metabolomics platform. Data from 3022 incident CHD cases (13 104 controls) and 1606 incident stroke cases (13 369 controls) were included. Logistic regression was used to model the relation between fatty acids and odds of CHD and stroke, adjusting for demographic and lifestyle variables only (ie, minimally adjusted model) or with further adjustment for other fatty acids (ie, fully adjusted model). Although circulating docosahexaenoic acid, but not linoleic acid, was related to lower CHD risk in the fully adjusted model (odds ratio, 0.85; 95% CI, 0.76-0.95 per standard unit of docosahexaenoic acid), there was evidence of high between-study heterogeneity and effect modification by study design. Stroke risk was consistently lower with increasing circulating linoleic acid (odds ratio for fully adjusted model, 0.82; 95% CI, 0.75-0.90). Circulating monounsaturated fatty acids were associated with higher CHD risk across all models and with stroke risk in the fully adjusted model (odds ratio, 1.22; 95% CI, 1.03-1.44). Saturated fatty acids were not related to increased CHD risk in the fully adjusted model (odds ratio, 0.94; 95% CI, 0.82-1.09), or stroke risk. Conclusions We found consistent evidence that linoleic acid was associated with decreased risk of stroke and that monounsaturated fatty acids were associated with increased risk of CHD. The different pattern between CHD and stroke in terms of fatty acids risk profile suggests future studies should be cautious about using composite events. Different study designs are needed to assess which, if any, of the associations observed is causal.
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Affiliation(s)
- Maria Carolina Borges
- MRC Integrative Epidemiology Unit at the University of Bristol United Kingdom.,Population Health Sciences Bristol Medical School University of Bristol United Kingdom
| | - Amand Floriaan Schmidt
- Institute of Cardiovascular Science University College London London United Kingdom.,Groningen Research Institute of Pharmacy University of Groningen the Netherlands.,Division Heart and Lungs Department of Cardiology University Medical Center Utrecht Utrecht The Netherlands
| | - Barbara Jefferis
- UCL Department of Primary Care & Population Health UCL Medical School London United Kingdom
| | - S Goya Wannamethee
- UCL Department of Primary Care & Population Health UCL Medical School London United Kingdom
| | - Debbie A Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol United Kingdom.,Population Health Sciences Bristol Medical School University of Bristol United Kingdom
| | - Mika Kivimaki
- Department of Epidemiology and Public Health University College London London United Kingdom
| | - Meena Kumari
- Department of Epidemiology and Public Health University College London London United Kingdom.,Institute for Social and Economic Research University of Essex United Kingdom
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit at the University of Bristol United Kingdom.,Population Health Sciences Bristol Medical School University of Bristol United Kingdom
| | - Yoav Ben-Shlomo
- Population Health Sciences Bristol Medical School University of Bristol United Kingdom
| | - Therese Tillin
- Cardiometabolic Phenotyping Group Institute of Cardiovascular Science University College London London United Kingdom
| | - Usha Menon
- MRC Clinical Trials Unit at UCL Institute of Clinical Trials & MethodologyUniversity College London London United Kingdom
| | - Rui Providencia
- Farr Institute of Health Informatics University College London London United Kingdom.,Barts Heart Centre St Bartholomew's Hospital Barts Health NHS Trust London United Kingdom
| | - Caroline Dale
- Farr Institute of Health Informatics University College London London United Kingdom
| | - Aleksandra Gentry-Maharaj
- MRC Clinical Trials Unit at UCL Institute of Clinical Trials & MethodologyUniversity College London London United Kingdom
| | - Alun Hughes
- Institute of Cardiovascular Science University College London London United Kingdom
| | - Nish Chaturvedi
- Institute of Cardiovascular Science University College London London United Kingdom
| | - Juan Pablo Casas
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC) VA Boston Healthcare System Boston MA USA
| | - Aroon D Hingorani
- Institute of Cardiovascular Science University College London London United Kingdom.,Farr Institute of Health Informatics University College London London United Kingdom
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11
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Joshi R, Wannamethee SG, Engmann J, Gaunt T, Lawlor DA, Price J, Papacosta O, Shah T, Tillin T, Chaturvedi N, Kivimaki M, Kuh D, Kumari M, Hughes AD, Casas JP, Humphries S, Hingorani AD, Schmidt AF. Triglyceride-containing lipoprotein sub-fractions and risk of coronary heart disease and stroke: A prospective analysis in 11,560 adults. Eur J Prev Cardiol 2020; 27:1617-1626. [PMID: 31996015 PMCID: PMC7707881 DOI: 10.1177/2047487319899621] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
AIMS Elevated low-density lipoprotein cholesterol (LDL-C) is a risk factor for cardiovascular disease; however, there is uncertainty about the role of total triglycerides and the individual triglyceride-containing lipoprotein sub-fractions. We measured 14 triglyceride-containing lipoprotein sub-fractions using nuclear magnetic resonance and examined associations with coronary heart disease and stroke. METHODS Triglyceride-containing sub-fraction measures were available in 11,560 participants from the three UK cohorts free of coronary heart disease and stroke at baseline. Multivariable logistic regression was used to estimate the association of each sub-fraction with coronary heart disease and stroke expressed as the odds ratio per standard deviation increment in the corresponding measure. RESULTS The 14 triglyceride-containing sub-fractions were positively correlated with one another and with total triglycerides, and inversely correlated with high-density lipoprotein cholesterol (HDL-C). Thirteen sub-fractions were positively associated with coronary heart disease (odds ratio in the range 1.12 to 1.22), with the effect estimates for coronary heart disease being comparable in subgroup analysis of participants with and without type 2 diabetes, and were attenuated after adjustment for HDL-C and LDL-C. There was no evidence for a clear association of any triglyceride lipoprotein sub-fraction with stroke. CONCLUSIONS Triglyceride sub-fractions are associated with increased risk of coronary heart disease but not stroke, with attenuation of effects on adjustment for HDL-C and LDL-C.
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Affiliation(s)
- Roshni Joshi
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, UK
| | - S Goya Wannamethee
- Department of Primary Care & Population Health, Faculty of Population Health, University College London, UK
| | - Jorgen Engmann
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, UK
| | - Tom Gaunt
- MRC Integrative Epidemiology Unit at the University of Bristol, UK
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, UK.,Bristol NIHR Biomedical Research Centre, UK.,Population Health Science, Bristol Medical School, UK
| | - Jackie Price
- The Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, UK
| | - Olia Papacosta
- Department of Primary Care & Population Health, Faculty of Population Health, University College London, UK
| | - Tina Shah
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, UK
| | - Therese Tillin
- Department of Epidemiology and Public Health, University College London, UK
| | - Nishi Chaturvedi
- Department of Epidemiology and Public Health, University College London, UK
| | - Mika Kivimaki
- Department of Epidemiology and Public Health, University College London, UK
| | - Diana Kuh
- MRC Unit for Lifelong Health and Ageing, UK
| | - Meena Kumari
- Institute for Social and Economic Research, University of Essex, UK
| | - Alun D Hughes
- Department of Epidemiology and Public Health, University College London, UK
| | - Juan P Casas
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare, USA
| | - Steve Humphries
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, UK
| | - Aroon D Hingorani
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, UK
| | - A Floriaan Schmidt
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, UK.,Department of Cardiology, Division Heart and Lungs, University Medical Centre Utrecht, The Netherlands
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12
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Tsui A, Richards M, Singh-Manoux A, Udeh-Momoh C, Davis D. Longitudinal associations between diurnal cortisol variation and later-life cognitive impairment. Neurology 2020; 94:e133-e141. [PMID: 31831603 PMCID: PMC6988984 DOI: 10.1212/wnl.0000000000008729] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2018] [Accepted: 07/09/2019] [Indexed: 02/05/2023] Open
Abstract
OBJECTIVE To determine whether hypothalamus-pituitary-adrenal axis (HPAA) dysfunction is prospectively associated with global cognitive impairment in later life. METHODS This cross-cohort study integrates 2 large longitudinal datasets, Whitehall II and the National Survey for Health and Development (NSHD), on data collected in the Whitehall II study between 2002-2004, 2007-2009, and 2012-2013; and for NSHD between 2006-2010 and in 2015. Serial salivary cortisol samples were collected multiple times within a 24-hour period at mean ages 61.2 and 65.9 years in Whitehall II and at age 60-64 years from NSHD participants. Cortisol profile is defined using cortisol awakening response and am:pm ratio. Cognitive function was measured using the Mini-Mental State Examination in Whitehall II and Addenbrooke's Cognitive Examination, third version, in NSHD, harmonized into a 30-point score. Models were adjusted for age, sex, diagnoses of hypertension and diabetes, body mass index (BMI), educational attainment, and interval between HPAA and cognitive assessments. RESULTS In fully adjusted models, increased am:pm cortisol ratio was prospectively associated with better later-life cognitive function years later (0.02 fewer errors per SD increase in am:pm cortisol ratio, p < 0.01) and verbal fluency (0.03 SD increase in verbal fluency per SD increase in am:pm ratio, p < 0.01). Increasing age, lower educational attainment, diagnosis of hypertension, diagnosis of diabetes, and increased BMI were associated with worse cognitive function and poorer verbal fluency. There were no associations between depression and later-life cognition or reverse associations between cognition and later-life cortisol profiles. CONCLUSIONS Loss of diurnal HPAA variation is evident in individuals subsequently experiencing more cognitive impairment. It may serve as an early preclinical marker of cognitive decline.
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Affiliation(s)
- Alex Tsui
- From the MRC Unit for Lifelong Health and Ageing at UCL (A.T., M.R., D.D.) and Department of Epidemiology and Public Health (A.S.-M.), University College London, UK; Epidemiology of Ageing & Neurodegenerative Diseases (A.S.-M.), INSERM, U1153, Hotel Dieu, Paris, France; Neuroepidemiology and Ageing Research Unit (C.U.-M.), School of Public Health, Faculty of Medicine, The Imperial College of Science, Technology and Medicine, London; and Translational Health Sciences (C.U.-M.), Bristol Medical School, University of Bristol, UK.
| | - Marcus Richards
- From the MRC Unit for Lifelong Health and Ageing at UCL (A.T., M.R., D.D.) and Department of Epidemiology and Public Health (A.S.-M.), University College London, UK; Epidemiology of Ageing & Neurodegenerative Diseases (A.S.-M.), INSERM, U1153, Hotel Dieu, Paris, France; Neuroepidemiology and Ageing Research Unit (C.U.-M.), School of Public Health, Faculty of Medicine, The Imperial College of Science, Technology and Medicine, London; and Translational Health Sciences (C.U.-M.), Bristol Medical School, University of Bristol, UK
| | - Archana Singh-Manoux
- From the MRC Unit for Lifelong Health and Ageing at UCL (A.T., M.R., D.D.) and Department of Epidemiology and Public Health (A.S.-M.), University College London, UK; Epidemiology of Ageing & Neurodegenerative Diseases (A.S.-M.), INSERM, U1153, Hotel Dieu, Paris, France; Neuroepidemiology and Ageing Research Unit (C.U.-M.), School of Public Health, Faculty of Medicine, The Imperial College of Science, Technology and Medicine, London; and Translational Health Sciences (C.U.-M.), Bristol Medical School, University of Bristol, UK
| | - Chinedu Udeh-Momoh
- From the MRC Unit for Lifelong Health and Ageing at UCL (A.T., M.R., D.D.) and Department of Epidemiology and Public Health (A.S.-M.), University College London, UK; Epidemiology of Ageing & Neurodegenerative Diseases (A.S.-M.), INSERM, U1153, Hotel Dieu, Paris, France; Neuroepidemiology and Ageing Research Unit (C.U.-M.), School of Public Health, Faculty of Medicine, The Imperial College of Science, Technology and Medicine, London; and Translational Health Sciences (C.U.-M.), Bristol Medical School, University of Bristol, UK
| | - Daniel Davis
- From the MRC Unit for Lifelong Health and Ageing at UCL (A.T., M.R., D.D.) and Department of Epidemiology and Public Health (A.S.-M.), University College London, UK; Epidemiology of Ageing & Neurodegenerative Diseases (A.S.-M.), INSERM, U1153, Hotel Dieu, Paris, France; Neuroepidemiology and Ageing Research Unit (C.U.-M.), School of Public Health, Faculty of Medicine, The Imperial College of Science, Technology and Medicine, London; and Translational Health Sciences (C.U.-M.), Bristol Medical School, University of Bristol, UK
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13
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Axes of a revolution: challenges and promises of big data in healthcare. Nat Med 2020; 26:29-38. [PMID: 31932803 DOI: 10.1038/s41591-019-0727-5] [Citation(s) in RCA: 129] [Impact Index Per Article: 32.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 12/03/2019] [Indexed: 01/08/2023]
Abstract
Health data are increasingly being generated at a massive scale, at various levels of phenotyping and from different types of resources. Concurrent with recent technological advances in both data-generation infrastructure and data-analysis methodologies, there have been many claims that these events will revolutionize healthcare, but such claims are still a matter of debate. Addressing the potential and challenges of big data in healthcare requires an understanding of the characteristics of the data. Here we characterize various properties of medical data, which we refer to as 'axes' of data, describe the considerations and tradeoffs taken when such data are generated, and the types of analyses that may achieve the tasks at hand. We then broadly describe the potential and challenges of using big data in healthcare resources, aiming to contribute to the ongoing discussion of the potential of big data resources to advance the understanding of health and disease.
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14
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James SN, Wong A, Tillin T, Hardy R, Chaturvedi N, Richards M. The effect of mid-life insulin resistance and type 2 diabetes on older-age cognitive state: the explanatory role of early-life advantage. Diabetologia 2019; 62:1891-1900. [PMID: 31359084 PMCID: PMC6731197 DOI: 10.1007/s00125-019-4949-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Accepted: 05/28/2019] [Indexed: 12/20/2022]
Abstract
AIMS/HYPOTHESIS Type 2 diabetes, hyperglycaemia and insulin resistance are associated with cognitive impairment and dementia, but causal inference studies using Mendelian randomisation do not confirm this. We hypothesised that early-life cognition and social/educational advantage may confound the relationship. METHODS From the population-based British 1946 birth cohort, a maximum number of 1780 participants had metabolic variables (type 2 diabetes, insulin resistance [HOMA2-IR] and HbA1c) assessed at age 60-64 years, and cognitive state (Addenbrooke's Cognitive Examination III [ACE-III]) and verbal memory assessed at age 69 years. Earlier-life measures included socioeconomic position (SEP), cognition at age 8 years and educational attainment. Polygenic risk scores (PRSs) for type 2 diabetes were calculated. We first used a PRS approach with multivariable linear regression to estimate associations between PRSs and metabolic traits and later-life cognitive state. Second, using a path model approach, we estimated the interrelationships between earlier-life measures, features of mid-life type 2 diabetes and cognitive state at age 69 years. All models were adjusted for sex. RESULTS The externally weighted PRS for type 2 diabetes was associated with mid-life metabolic traits (e.g. HOMA2-IR β = 0.08 [95% CI 0.02, 0.16]), but not with ACE-III (β = 0.04 [-0.02, 0.90]) or other cognitive outcomes. While there was an association between HOMA2-IR and subsequent ACE-III (β = -0.09 [-0.15, -0.03]), path modelling showed no direct effect (β = -0.01 [-0.06, 0.03]) after accounting for the association between childhood SEP and education with HOMA2-IR. The same pattern was observed for later-life verbal memory. CONCLUSIONS/INTERPRETATION Associations between type 2 diabetes and mid-life metabolic traits with subsequent cognitive state do not appear causal, and instead they may be explained by SEP in early life, childhood cognition and educational attainment. Therefore, glucose-lowering medication may be unlikely to combat cognitive impairment in older age.
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Affiliation(s)
- Sarah-Naomi James
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK.
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK
| | - Therese Tillin
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK
| | - Rebecca Hardy
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK
| | - Nishi Chaturvedi
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK
| | - Marcus Richards
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK
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15
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Kia DA, Noyce AJ, White J, Speed D, Nicolas A, Burgess S, Lawlor DA, Davey Smith G, Singleton A, Nalls MA, Sofat R, Wood NW. Mendelian randomization study shows no causal relationship between circulating urate levels and Parkinson's disease. Ann Neurol 2019; 84:191-199. [PMID: 30014513 PMCID: PMC6481555 DOI: 10.1002/ana.25294] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Revised: 05/30/2018] [Accepted: 06/04/2018] [Indexed: 02/02/2023]
Abstract
Objective Observational studies have shown that increased plasma urate is associated with lower risk of Parkinson’s disease (PD), but these studies were not designed to test causality. If a causal relationship exists, then modulating plasma urate levels could be a potential preventive avenue for PD. We used a large two-sample Mendelian randomization (MR) design to assess for a causal relationship between plasma urate and PD risk. Methods We used a genetic instrument consisting of 31 independent loci for plasma urate on a case-control genome-wide association study data set, which included 13,708 PD cases and 95,282 controls. Individual effect estimates for each SNP were combined using the inverse-variance weighted (IVW) method. Two additional methods, MR-Egger and a penalized weighted median (PWM)-based approach, were used to assess potential bias attributed to pleiotropy or invalid instruments. Results We found no evidence for a causal relationship between urate and PD, with an effect estimate from the IVW method of odds ratio (OR) 1.03 (95% confidence interval [CI], 0.88–1.20) per 1-standard-deviation increase in plasma urate levels. MR Egger and PWM analyses yielded similar estimates (OR, 0.99 [95% CI, 0.83–1.17] and 0.99 [95% CI, 0.86−1.14], respectively). Interpretation We did not find evidence for a linear causal protective effect by urate on PD risk. The associations observed in previous observational studies may be, in part, attributed to confounding or reverse causality. In the context of the present findings, strategies to elevate circulating urate levels may not reduce overall PD risk.
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Affiliation(s)
- Demis A Kia
- Department of Molecular Neuroscience, UCL Institute of Neurology, London, United Kingdom
| | - Alastair J Noyce
- Department of Molecular Neuroscience, UCL Institute of Neurology, London, United Kingdom.,Preventive Neurology Unit, Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, United Kingdom
| | - Jon White
- UCL Genetics Institute, University College, London, United Kingdom
| | - Doug Speed
- UCL Genetics Institute, University College, London, United Kingdom
| | - Aude Nicolas
- Laboratory for Neurogenetics, National Institutes for Health, Bethesda, MD
| | | | - Stephen Burgess
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Debbie A Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom.,Population Health Science, Bristol Medical School of Bristol, Bristol, United Kingdom
| | - George Davey Smith
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom.,Population Health Science, Bristol Medical School of Bristol, Bristol, United Kingdom
| | - Andrew Singleton
- Laboratory for Neurogenetics, National Institutes for Health, Bethesda, MD
| | - Mike A Nalls
- Laboratory for Neurogenetics, National Institutes for Health, Bethesda, MD.,Data Tecnica International, Glen Echo, MD
| | - Reecha Sofat
- Institute of Health Informatics, University College London, London, United Kingdom
| | - Nicholas W Wood
- Department of Molecular Neuroscience, UCL Institute of Neurology, London, United Kingdom
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16
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De Silva NMG, Borges MC, Hingorani AD, Engmann J, Shah T, Zhang X, Luan J, Langenberg C, Wong A, Kuh D, Chambers JC, Zhang W, Jarvelin MR, Sebert S, Auvinen J, Gaunt TR, Lawlor DA. Liver Function and Risk of Type 2 Diabetes: Bidirectional Mendelian Randomization Study. Diabetes 2019; 68:1681-1691. [PMID: 31088856 PMCID: PMC7011195 DOI: 10.2337/db18-1048] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Accepted: 05/05/2019] [Indexed: 12/15/2022]
Abstract
Liver dysfunction and type 2 diabetes (T2D) are consistently associated. However, it is currently unknown whether liver dysfunction contributes to, results from, or is merely correlated with T2D due to confounding. We used Mendelian randomization to investigate the presence and direction of any causal relation between liver function and T2D risk including up to 64,094 T2D case and 607,012 control subjects. Several biomarkers were used as proxies of liver function (i.e., alanine aminotransferase [ALT], aspartate aminotransferase [AST], alkaline phosphatase [ALP], and γ-glutamyl transferase [GGT]). Genetic variants strongly associated with each liver function marker were used to investigate the effect of liver function on T2D risk. In addition, genetic variants strongly associated with T2D risk and with fasting insulin were used to investigate the effect of predisposition to T2D and insulin resistance, respectively, on liver function. Genetically predicted higher circulating ALT and AST were related to increased risk of T2D. There was a modest negative association of genetically predicted ALP with T2D risk and no evidence of association between GGT and T2D risk. Genetic predisposition to higher fasting insulin, but not to T2D, was related to increased circulating ALT. Since circulating ALT and AST are markers of nonalcoholic fatty liver disease (NAFLD), these findings provide some support for insulin resistance resulting in NAFLD, which in turn increases T2D risk.
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Affiliation(s)
- N Maneka G De Silva
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, U.K
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, U.K
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment & Health, School of Public Health, Imperial College London, London, U.K
| | - Maria Carolina Borges
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, U.K
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, U.K
| | - Aroon D Hingorani
- UCL Institute of Cardiovascular Science, Research Department of Population Science and Experimental Medicine, Centre for Translational Genomics, University College London, London, U.K
- Farr Institute, University College London, London, U.K
| | - Jorgen Engmann
- UCL Institute of Cardiovascular Science, Research Department of Population Science and Experimental Medicine, Centre for Translational Genomics, University College London, London, U.K
| | - Tina Shah
- UCL Institute of Cardiovascular Science, Research Department of Population Science and Experimental Medicine, Centre for Translational Genomics, University College London, London, U.K
| | - Xiaoshuai Zhang
- MRC Epidemiology Unit, University of Cambridge, Cambridge, U.K
- Department of Epidemiology and Biostatistics, School of Public Health and Management, Binzhou Medical University, Yantai, Shandong, China
- Department of Biostatistics, School of Public Health, Shandong University, Jinan, Shandong, China
| | - Jian'an Luan
- MRC Epidemiology Unit, University of Cambridge, Cambridge, U.K
| | | | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, U.K
| | - Diana Kuh
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, U.K
| | - John C Chambers
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment & Health, School of Public Health, Imperial College London, London, U.K
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- Department of Epidemiology and Biostatistics, Imperial College London, London, U.K
- Department of Cardiology, Ealing Hospital, Middlesex, U.K
- Imperial College Healthcare NHS Trust, Imperial College London, London, U.K
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, Imperial College London, London, U.K
- Department of Cardiology, Ealing Hospital, Middlesex, U.K
| | - Marjo-Ritta Jarvelin
- Department of Epidemiology and Biostatistics, Imperial College London, London, U.K
- Center for Life Course Health Research, University of Oulu, Oulu, Finland
- Biocenter Oulu, Oulu, Finland
| | - Sylvain Sebert
- Center for Life Course Health Research, University of Oulu, Oulu, Finland
| | - Juha Auvinen
- Center for Life Course Health Research, University of Oulu, Oulu, Finland
- Oulunkaari Health Center, Ii, Finland
| | | | - Tom R Gaunt
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, U.K
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, U.K
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, U.K.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, U.K
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17
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Santos Ferreira DL, Maple HJ, Goodwin M, Brand JS, Yip V, Min JL, Groom A, Lawlor DA, Ring S. The Effect of Pre-Analytical Conditions on Blood Metabolomics in Epidemiological Studies. Metabolites 2019; 9:metabo9040064. [PMID: 30987180 PMCID: PMC6523923 DOI: 10.3390/metabo9040064] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Revised: 03/22/2019] [Accepted: 03/27/2019] [Indexed: 11/16/2022] Open
Abstract
Serum and plasma are commonly used in metabolomic-epidemiology studies. Their metabolome is susceptible to differences in pre-analytical conditions and the impact of this is unclear. Participant-matched EDTA-plasma and serum samples were collected from 37 non-fasting volunteers and profiled using a targeted nuclear magnetic resonance (NMR) metabolomics platform (n = 151 traits). Correlations and differences in mean of metabolite concentrations were compared between reference (pre-storage: 4 °C, 1.5 h; post-storage: no buffer addition delay or NMR analysis delay) and four pre-storage blood processing conditions, where samples were incubated at (i) 4 °C, 24 h; (ii) 4 °C, 48 h; (iii) 21 °C, 24 h; and (iv) 21 °C, 48 h, before centrifugation; and two post-storage sample processing conditions in which samples thawed overnight (i) then left for 24 h before addition of sodium buffer followed by immediate NMR analysis; and (ii) addition of sodium buffer, then left for 24 h before NMR profiling. We used multilevel linear regression models and Spearman’s rank correlation coefficients to analyse the data. Most metabolic traits had high rank correlation and minimal differences in mean concentrations between samples subjected to reference and the different conditions tested, that may commonly occur in studies. However, glycolysis metabolites, histidine, acetate and diacylglycerol concentrations may be compromised and this could bias results in association/causal analyses.
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Affiliation(s)
- Diana L Santos Ferreira
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 2BN, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK.
| | - Hannah J Maple
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 2BN, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK.
| | - Matt Goodwin
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 2BN, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK.
| | - Judith S Brand
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 2BN, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK.
- Clinical Epidemiology and Biostatistics, School of Medical Sciences, Örebro University, 701 85 Örebro, Sweden.
| | - Vikki Yip
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK.
| | - Josine L Min
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 2BN, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK.
| | - Alix Groom
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 2BN, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK.
| | - Debbie A Lawlor
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 2BN, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK.
- Bristol National Institute of Health Research Biomedical Research Centre, Bristol BS1 3NU, UK.
| | - Susan Ring
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 2BN, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK.
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18
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Proitsi P, Kuh D, Wong A, Maddock J, Bendayan R, Wulaningsih W, Hardy R, Richards M. Lifetime cognition and late midlife blood metabolites: findings from a British birth cohort. Transl Psychiatry 2018; 8:203. [PMID: 30258059 PMCID: PMC6158182 DOI: 10.1038/s41398-018-0253-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Revised: 04/25/2018] [Accepted: 05/11/2018] [Indexed: 11/24/2022] Open
Abstract
Maintenance of healthy cognitive ageing is vital for independence and wellbeing in the older general population. We investigated the association between blood metabolites and cognitive function and decline. Participants from the MRC National Survey of Health and Development (NSHD, the British 1946 birth cohort) were studied; 233 nuclear magnetic resonance circulating metabolite measures were quantified in 909 men and women at ages 60-64. Short-term and delayed verbal memory and processing speed were concurrently assessed and these tests were repeated at age 69. Linear regression analyses tested associations between metabolites and cognitive function at ages 60-64, and changes in these measures by age 69, adjusting for childhood cognition, education, socio-economic status and lifestyle factors. In cross-sectional analyses, metabolite levels, particularly fatty acid composition and different lipid sub-classes, were associated with short-term verbal memory (4 measures in females and 11 measures in the whole sample), delayed verbal memory (2 measures in females) and processing speed (8 measures in males and 2 measures in the whole sample) (p < 0.002). One metabolite was associated with change in cognition in females. Most of the observed associations were attenuated after adjustment for childhood cognition and education. A life course perspective can improve the understanding of how peripheral metabolic processes underlie cognitive ageing.
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Affiliation(s)
| | - Diana Kuh
- 0000 0004 0427 2580grid.268922.5MRC Unit for Lifelong Health and Ageing at UCL, London, UK
| | - Andrew Wong
- 0000 0004 0427 2580grid.268922.5MRC Unit for Lifelong Health and Ageing at UCL, London, UK
| | - Jane Maddock
- 0000 0004 0427 2580grid.268922.5MRC Unit for Lifelong Health and Ageing at UCL, London, UK
| | - Rebecca Bendayan
- 0000 0004 0427 2580grid.268922.5MRC Unit for Lifelong Health and Ageing at UCL, London, UK
| | - Wahyu Wulaningsih
- 0000 0004 0427 2580grid.268922.5MRC Unit for Lifelong Health and Ageing at UCL, London, UK
| | - Rebecca Hardy
- 0000 0004 0427 2580grid.268922.5MRC Unit for Lifelong Health and Ageing at UCL, London, UK
| | - Marcus Richards
- 0000 0004 0427 2580grid.268922.5MRC Unit for Lifelong Health and Ageing at UCL, London, UK
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19
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Shatwan IM, Winther KH, Ellahi B, Elwood P, Ben-Shlomo Y, Givens I, Rayman MP, Lovegrove JA, Vimaleswaran KS. Association of apolipoprotein E gene polymorphisms with blood lipids and their interaction with dietary factors. Lipids Health Dis 2018; 17:98. [PMID: 29712557 PMCID: PMC5928585 DOI: 10.1186/s12944-018-0744-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2017] [Accepted: 04/13/2018] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Several candidate genes have been identified in relation to lipid metabolism, and among these, lipoprotein lipase (LPL) and apolipoprotein E (APOE) gene polymorphisms are major sources of genetically determined variation in lipid concentrations. This study investigated the association of two single nucleotide polymorphisms (SNPs) at LPL, seven tagging SNPs at the APOE gene, and a common APOE haplotype (two SNPs) with blood lipids, and examined the interaction of these SNPs with dietary factors. METHODS The population studied for this investigation included 660 individuals from the Prevention of Cancer by Intervention with Selenium (PRECISE) study who supplied baseline data. The findings of the PRECISE study were further replicated using 1238 individuals from the Caerphilly Prospective cohort (CaPS). Dietary intake was assessed using a validated food-frequency questionnaire (FFQ) in PRECISE and a validated semi-quantitative FFQ in the CaPS. Interaction analyses were performed by including the interaction term in the linear regression model adjusted for age, body mass index, sex and country. RESULTS There was no association between dietary factors and blood lipids after Bonferroni correction and adjustment for confounding factors in either cohort. In the PRECISE study, after correction for multiple testing, there was a statistically significant association of the APOE haplotype (rs7412 and rs429358; E2, E3, and E4) and APOE tagSNP rs445925 with total cholesterol (P = 4 × 10- 4 and P = 0.003, respectively). Carriers of the E2 allele had lower total cholesterol concentration (5.54 ± 0.97 mmol/L) than those with the E3 (5.98 ± 1.05 mmol/L) (P = 0.001) and E4 (6.09 ± 1.06 mmol/L) (P = 2 × 10- 4) alleles. The association of APOE haplotype (E2, E3, and E4) and APOE SNP rs445925 with total cholesterol (P = 2 × 10- 6 and P = 3 × 10- 4, respectively) was further replicated in the CaPS. Additionally, significant association was found between APOE haplotype and APOE SNP rs445925 with low density lipoprotein cholesterol in CaPS (P = 4 × 10- 4 and P = 0.001, respectively). After Bonferroni correction, none of the cohorts showed a statistically significant SNP-diet interaction on lipid outcomes. CONCLUSION In summary, our findings from the two cohorts confirm that genetic variations at the APOE locus influence plasma total cholesterol concentrations, however, the gene-diet interactions on lipids require further investigation in larger cohorts.
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Affiliation(s)
- Israa M Shatwan
- Hugh Sinclair Unit of Human Nutrition and Institute for Cardiovascular and Metabolic Research (ICMR), Department of Food and Nutritional Sciences, University of Reading, Whiteknights, PO Box 226, Reading, RG6 6AP, UK.,Food and Nutrition Department, Faculty of Home Economics, King Abdulaziz University, Jeddah, Saudi Arabia
| | | | - Basma Ellahi
- Faculty of Health and Social Care, University of Chester, Chester, CH1 1SL, UK
| | - Peter Elwood
- Department of Epidemiology, Statistics and Public Health, Cardiff University, University Hospital of Wales, Heath Park, Cardiff, CF14 4XW, UK
| | - Yoav Ben-Shlomo
- Population Health Sciences, University of Bristol, Bristol, BS8 2PS, UK
| | - Ian Givens
- Institute for Food, Nutrition and Health, University of Reading, Earley Gate, Reading, RG6 6AR, UK
| | - Margaret P Rayman
- Department of Nutritional Sciences Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7XH, UK
| | - Julie A Lovegrove
- Hugh Sinclair Unit of Human Nutrition and Institute for Cardiovascular and Metabolic Research (ICMR), Department of Food and Nutritional Sciences, University of Reading, Whiteknights, PO Box 226, Reading, RG6 6AP, UK
| | - Karani S Vimaleswaran
- Hugh Sinclair Unit of Human Nutrition and Institute for Cardiovascular and Metabolic Research (ICMR), Department of Food and Nutritional Sciences, University of Reading, Whiteknights, PO Box 226, Reading, RG6 6AP, UK.
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20
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Nüesch E, Dale C, Palmer TM, White J, Keating BJ, van Iperen EP, Goel A, Padmanabhan S, Asselbergs FW, Verschuren WM, Wijmenga C, Van der Schouw YT, Onland-Moret NC, Lange LA, Hovingh GK, Sivapalaratnam S, Morris RW, Whincup PH, Wannamethe GS, Gaunt TR, Ebrahim S, Steel L, Nair N, Reiner AP, Kooperberg C, Wilson JF, Bolton JL, McLachlan S, Price JF, Strachan MW, Robertson CM, Kleber ME, Delgado G, März W, Melander O, Dominiczak AF, Farrall M, Watkins H, Leusink M, Maitland-van der Zee AH, de Groot MC, Dudbridge F, Hingorani A, Ben-Shlomo Y, Lawlor DA, Amuzu A, Caufield M, Cavadino A, Cooper J, Davies TL, Drenos F, Engmann J, Finan C, Giambartolomei C, Hardy R, Humphries SE, Hypponen E, Kivimaki M, Kuh D, Kumari M, Ong K, Plagnol V, Power C, Richards M, Shah S, Shah T, Sofat R, Talmud PJ, Wareham N, Warren H, Whittaker JC, Wong A, Zabaneh D, Davey Smith G, Wells JC, Leon DA, Holmes MV, Casas JP. Adult height, coronary heart disease and stroke: a multi-locus Mendelian randomization meta-analysis. Int J Epidemiol 2018; 45:1927-1937. [PMID: 25979724 PMCID: PMC5841831 DOI: 10.1093/ije/dyv074] [Citation(s) in RCA: 81] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/15/2015] [Indexed: 11/12/2022] Open
Abstract
Background: We investigated causal effect of completed growth, measured by adult height, on coronary heart disease (CHD), stroke and cardiovascular traits, using instrumental variable (IV) Mendelian randomization meta-analysis. Methods: We developed an allele score based on 69 single nucleotide polymorphisms (SNPs) associated with adult height, identified by the IBCCardioChip, and used it for IV analysis against cardiovascular risk factors and events in 21 studies and 60 028 participants. IV analysis on CHD was supplemented by summary data from 180 height-SNPs from the GIANT consortium and their corresponding CHD estimates derived from CARDIoGRAMplusC4D. Results: IV estimates from IBCCardioChip and GIANT-CARDIoGRAMplusC4D showed that a 6.5-cm increase in height reduced the odds of CHD by 10% [odds ratios 0.90; 95% confidence intervals (CIs): 0.78 to 1.03 and 0.85 to 0.95, respectively],which agrees with the estimate from the Emerging Risk Factors Collaboration (hazard ratio 0.93; 95% CI: 0.91 to 0.94). IV analysis revealed no association with stroke (odds ratio 0.97; 95% CI: 0.79 to 1.19). IV analysis showed that a 6.5-cm increase in height resulted in lower levels of body mass index (P < 0.001), triglycerides (P < 0.001), non high-density (non-HDL) cholesterol (P < 0.001), C-reactive protein (P = 0.042), and systolic blood pressure (P = 0.064) and higher levels of forced expiratory volume in 1 s and forced vital capacity (P < 0.001 for both). Conclusions: Taller individuals have a lower risk of CHD with potential explanations being that taller people have a better lung function and lower levels of body mass index, cholesterol and blood pressure.
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Affiliation(s)
- Eveline Nüesch
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.,CTU Bern, Department of Clinical Research and Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland
| | - Caroline Dale
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Tom M Palmer
- Warwick Medical School, University of Warwick, Coventry, UK.,Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
| | - Jon White
- UCL Genetics Institute, Department of Genetics, Evolution and Environment, University College London, London, UK
| | - Brendan J Keating
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Surgery.,Division of Genetics, University of Pennsylvania, Philadelphia
| | - Erik Pa van Iperen
- Department of Biostatistics, Academic Medical Center Amsterdam, Amsterdam, The Netherlands.,Durrer Center for Cardiogenetic Research, ICIN-Netherlands Heart Institute, Utrecht, The Netherlands
| | - Anuj Goel
- Wellcome Trust Centre for Human Genetics and Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Sandosh Padmanabhan
- Institute of Cardiovascular and Medical Sciences, College of Medical Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Folkert W Asselbergs
- Department of Cardiology, Division Heart and Lungs, University Medical Centre Utrecht, Utrecht, The Netherlands.,Durrer Center for Cardiogenetic Research, ICIN-Netherlands Heart Institute, Utrecht, The Netherlands.,Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK
| | | | | | | | | | | | - Leslie A Lange
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - G K Hovingh
- Department of Vascular Medicine, Academic Medical Center Amsterdam, Amsterdam, The Netherlands
| | - Suthesh Sivapalaratnam
- Department of Vascular Medicine, Academic Medical Center Amsterdam, Amsterdam, The Netherlands
| | - Richard W Morris
- Department of Primary Care & Population Health, University College London, London, UK
| | - Peter H Whincup
- Population Health Research Institute, St George's, University of London, London, UK
| | - Goya S Wannamethe
- Department of Primary Care & Population Health, University College London, London, UK
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, UK.,School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Shah Ebrahim
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Laura Steel
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Nikhil Nair
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Alexander P Reiner
- Department of Epidemiology, University of Washington, Seattle, WA, USA / Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - James F Wilson
- Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK.,MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Jennifer L Bolton
- Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Stela McLachlan
- Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Jacqueline F Price
- Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | | | - Christine M Robertson
- Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Marcus E Kleber
- Fifth Department of Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Graciela Delgado
- Fifth Department of Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Winfried März
- Medical Clinic V (Nephrology, Hypertensiology, Endocrinology, Diabetolgy, and Rheumatology), Mannheim Medical Faculty, University of Heidelberg, Germany, Synlab Academy, Synlab Services GmbH, Mannheim and Augsburg, Germany, Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Austria
| | | | - Anna F Dominiczak
- Institute of Cardiovascular and Medical Sciences, College of Medical Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Martin Farrall
- Wellcome Trust Centre for Human Genetics and Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Hugh Watkins
- Wellcome Trust Centre for Human Genetics and Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Maarten Leusink
- Division of Pharmacoepidemiology & Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands.,Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Anke H Maitland-van der Zee
- Division of Pharmacoepidemiology & Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands
| | - Mark Ch de Groot
- Division of Pharmacoepidemiology & Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands.,Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Frank Dudbridge
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Aroon Hingorani
- Department of Epidemiology and Public Health, University College London Medical School, London, UK
| | - Yoav Ben-Shlomo
- School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Debbie A Lawlor
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, UK.,School of Social and Community Medicine, University of Bristol, Bristol, UK
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - D Zabaneh
- UCLEB, London, Edinburgh and Bristol, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, UK.,School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Jonathan C Wells
- Childhood Nutrition Research Centre, UCL Institute of Child Health, London, UK
| | - David A Leon
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.,Department of Community Medicine, Arctic University of Norway, UiT
| | - Michael V Holmes
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK.,Department of Surgery and Clinical Epidemiology Unit, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Juan P Casas
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.,Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK
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21
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Borges MC, Barros AJD, Ferreira DLS, Casas JP, Horta BL, Kivimaki M, Kumari M, Menon U, Gaunt TR, Ben-Shlomo Y, Freitas DF, Oliveira IO, Gentry-Maharaj A, Fourkala E, Lawlor DA, Hingorani AD. Metabolic Profiling of Adiponectin Levels in Adults: Mendelian Randomization Analysis. CIRCULATION. CARDIOVASCULAR GENETICS 2017; 10:e001837. [PMID: 29237687 PMCID: PMC5736126 DOI: 10.1161/circgenetics.117.001837] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/16/2017] [Accepted: 09/13/2017] [Indexed: 11/16/2022]
Abstract
BACKGROUND Adiponectin, a circulating adipocyte-derived protein, has insulin-sensitizing, anti-inflammatory, antiatherogenic, and cardiomyocyte-protective properties in animal models. However, the systemic effects of adiponectin in humans are unknown. Our aims were to define the metabolic profile associated with higher blood adiponectin concentration and investigate whether variation in adiponectin concentration affects the systemic metabolic profile. METHODS AND RESULTS We applied multivariable regression in ≤5909 adults and Mendelian randomization (using cis-acting genetic variants in the vicinity of the adiponectin gene as instrumental variables) for analyzing the causal effect of adiponectin in the metabolic profile of ≤37 545 adults. Participants were largely European from 6 longitudinal studies and 1 genome-wide association consortium. In the multivariable regression analyses, higher circulating adiponectin was associated with higher high-density lipoprotein lipids and lower very-low-density lipoprotein lipids, glucose levels, branched-chain amino acids, and inflammatory markers. However, these findings were not supported by Mendelian randomization analyses for most metabolites. Findings were consistent between sexes and after excluding high-risk groups (defined by age and occurrence of previous cardiovascular event) and 1 study with admixed population. CONCLUSIONS Our findings indicate that blood adiponectin concentration is more likely to be an epiphenomenon in the context of metabolic disease than a key determinant.
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Affiliation(s)
- Maria Carolina Borges
- From the Post-Graduate Program in Epidemiology, Federal University of Pelotas, Brazil (M.C.B., A.J.D.B., B.L.H., D.F.F., I.O.O.); MRC Integrative Epidemiology Unit (M.C.B., D.L.S.F., T.R.G., D.A.L.) and Population Health Sciences, Bristol Medical School (M.C.B., D.L.S.F., T.R.G., Y.B.-S., D.A.L.), University of Bristol, United Kingdom; Farr Institute of Health Informatics (J.P.C., A.D.H.), Department of Epidemiology and Public Health (M. Kivimaki, M. Kumari), Department of Women's Cancer, Institute for Women's Health, Faculty of Population Health Sciences (U.M., A.G.-M., E.F.), and Institute of Cardiovascular Science (A.D.H.), University College London, United Kingdom; Institute for Social and Economic Research, University of Essex, United Kingdom (M. Kumari); and Department of Physiology and Pharmacology, Institute of Biology, Federal University of Pelotas, Brazil (I.O.O.).
| | - Aluísio J D Barros
- From the Post-Graduate Program in Epidemiology, Federal University of Pelotas, Brazil (M.C.B., A.J.D.B., B.L.H., D.F.F., I.O.O.); MRC Integrative Epidemiology Unit (M.C.B., D.L.S.F., T.R.G., D.A.L.) and Population Health Sciences, Bristol Medical School (M.C.B., D.L.S.F., T.R.G., Y.B.-S., D.A.L.), University of Bristol, United Kingdom; Farr Institute of Health Informatics (J.P.C., A.D.H.), Department of Epidemiology and Public Health (M. Kivimaki, M. Kumari), Department of Women's Cancer, Institute for Women's Health, Faculty of Population Health Sciences (U.M., A.G.-M., E.F.), and Institute of Cardiovascular Science (A.D.H.), University College London, United Kingdom; Institute for Social and Economic Research, University of Essex, United Kingdom (M. Kumari); and Department of Physiology and Pharmacology, Institute of Biology, Federal University of Pelotas, Brazil (I.O.O.)
| | - Diana L Santos Ferreira
- From the Post-Graduate Program in Epidemiology, Federal University of Pelotas, Brazil (M.C.B., A.J.D.B., B.L.H., D.F.F., I.O.O.); MRC Integrative Epidemiology Unit (M.C.B., D.L.S.F., T.R.G., D.A.L.) and Population Health Sciences, Bristol Medical School (M.C.B., D.L.S.F., T.R.G., Y.B.-S., D.A.L.), University of Bristol, United Kingdom; Farr Institute of Health Informatics (J.P.C., A.D.H.), Department of Epidemiology and Public Health (M. Kivimaki, M. Kumari), Department of Women's Cancer, Institute for Women's Health, Faculty of Population Health Sciences (U.M., A.G.-M., E.F.), and Institute of Cardiovascular Science (A.D.H.), University College London, United Kingdom; Institute for Social and Economic Research, University of Essex, United Kingdom (M. Kumari); and Department of Physiology and Pharmacology, Institute of Biology, Federal University of Pelotas, Brazil (I.O.O.)
| | - Juan Pablo Casas
- From the Post-Graduate Program in Epidemiology, Federal University of Pelotas, Brazil (M.C.B., A.J.D.B., B.L.H., D.F.F., I.O.O.); MRC Integrative Epidemiology Unit (M.C.B., D.L.S.F., T.R.G., D.A.L.) and Population Health Sciences, Bristol Medical School (M.C.B., D.L.S.F., T.R.G., Y.B.-S., D.A.L.), University of Bristol, United Kingdom; Farr Institute of Health Informatics (J.P.C., A.D.H.), Department of Epidemiology and Public Health (M. Kivimaki, M. Kumari), Department of Women's Cancer, Institute for Women's Health, Faculty of Population Health Sciences (U.M., A.G.-M., E.F.), and Institute of Cardiovascular Science (A.D.H.), University College London, United Kingdom; Institute for Social and Economic Research, University of Essex, United Kingdom (M. Kumari); and Department of Physiology and Pharmacology, Institute of Biology, Federal University of Pelotas, Brazil (I.O.O.)
| | - Bernardo Lessa Horta
- From the Post-Graduate Program in Epidemiology, Federal University of Pelotas, Brazil (M.C.B., A.J.D.B., B.L.H., D.F.F., I.O.O.); MRC Integrative Epidemiology Unit (M.C.B., D.L.S.F., T.R.G., D.A.L.) and Population Health Sciences, Bristol Medical School (M.C.B., D.L.S.F., T.R.G., Y.B.-S., D.A.L.), University of Bristol, United Kingdom; Farr Institute of Health Informatics (J.P.C., A.D.H.), Department of Epidemiology and Public Health (M. Kivimaki, M. Kumari), Department of Women's Cancer, Institute for Women's Health, Faculty of Population Health Sciences (U.M., A.G.-M., E.F.), and Institute of Cardiovascular Science (A.D.H.), University College London, United Kingdom; Institute for Social and Economic Research, University of Essex, United Kingdom (M. Kumari); and Department of Physiology and Pharmacology, Institute of Biology, Federal University of Pelotas, Brazil (I.O.O.)
| | - Mika Kivimaki
- From the Post-Graduate Program in Epidemiology, Federal University of Pelotas, Brazil (M.C.B., A.J.D.B., B.L.H., D.F.F., I.O.O.); MRC Integrative Epidemiology Unit (M.C.B., D.L.S.F., T.R.G., D.A.L.) and Population Health Sciences, Bristol Medical School (M.C.B., D.L.S.F., T.R.G., Y.B.-S., D.A.L.), University of Bristol, United Kingdom; Farr Institute of Health Informatics (J.P.C., A.D.H.), Department of Epidemiology and Public Health (M. Kivimaki, M. Kumari), Department of Women's Cancer, Institute for Women's Health, Faculty of Population Health Sciences (U.M., A.G.-M., E.F.), and Institute of Cardiovascular Science (A.D.H.), University College London, United Kingdom; Institute for Social and Economic Research, University of Essex, United Kingdom (M. Kumari); and Department of Physiology and Pharmacology, Institute of Biology, Federal University of Pelotas, Brazil (I.O.O.)
| | - Meena Kumari
- From the Post-Graduate Program in Epidemiology, Federal University of Pelotas, Brazil (M.C.B., A.J.D.B., B.L.H., D.F.F., I.O.O.); MRC Integrative Epidemiology Unit (M.C.B., D.L.S.F., T.R.G., D.A.L.) and Population Health Sciences, Bristol Medical School (M.C.B., D.L.S.F., T.R.G., Y.B.-S., D.A.L.), University of Bristol, United Kingdom; Farr Institute of Health Informatics (J.P.C., A.D.H.), Department of Epidemiology and Public Health (M. Kivimaki, M. Kumari), Department of Women's Cancer, Institute for Women's Health, Faculty of Population Health Sciences (U.M., A.G.-M., E.F.), and Institute of Cardiovascular Science (A.D.H.), University College London, United Kingdom; Institute for Social and Economic Research, University of Essex, United Kingdom (M. Kumari); and Department of Physiology and Pharmacology, Institute of Biology, Federal University of Pelotas, Brazil (I.O.O.)
| | - Usha Menon
- From the Post-Graduate Program in Epidemiology, Federal University of Pelotas, Brazil (M.C.B., A.J.D.B., B.L.H., D.F.F., I.O.O.); MRC Integrative Epidemiology Unit (M.C.B., D.L.S.F., T.R.G., D.A.L.) and Population Health Sciences, Bristol Medical School (M.C.B., D.L.S.F., T.R.G., Y.B.-S., D.A.L.), University of Bristol, United Kingdom; Farr Institute of Health Informatics (J.P.C., A.D.H.), Department of Epidemiology and Public Health (M. Kivimaki, M. Kumari), Department of Women's Cancer, Institute for Women's Health, Faculty of Population Health Sciences (U.M., A.G.-M., E.F.), and Institute of Cardiovascular Science (A.D.H.), University College London, United Kingdom; Institute for Social and Economic Research, University of Essex, United Kingdom (M. Kumari); and Department of Physiology and Pharmacology, Institute of Biology, Federal University of Pelotas, Brazil (I.O.O.)
| | - Tom R Gaunt
- From the Post-Graduate Program in Epidemiology, Federal University of Pelotas, Brazil (M.C.B., A.J.D.B., B.L.H., D.F.F., I.O.O.); MRC Integrative Epidemiology Unit (M.C.B., D.L.S.F., T.R.G., D.A.L.) and Population Health Sciences, Bristol Medical School (M.C.B., D.L.S.F., T.R.G., Y.B.-S., D.A.L.), University of Bristol, United Kingdom; Farr Institute of Health Informatics (J.P.C., A.D.H.), Department of Epidemiology and Public Health (M. Kivimaki, M. Kumari), Department of Women's Cancer, Institute for Women's Health, Faculty of Population Health Sciences (U.M., A.G.-M., E.F.), and Institute of Cardiovascular Science (A.D.H.), University College London, United Kingdom; Institute for Social and Economic Research, University of Essex, United Kingdom (M. Kumari); and Department of Physiology and Pharmacology, Institute of Biology, Federal University of Pelotas, Brazil (I.O.O.)
| | - Yoav Ben-Shlomo
- From the Post-Graduate Program in Epidemiology, Federal University of Pelotas, Brazil (M.C.B., A.J.D.B., B.L.H., D.F.F., I.O.O.); MRC Integrative Epidemiology Unit (M.C.B., D.L.S.F., T.R.G., D.A.L.) and Population Health Sciences, Bristol Medical School (M.C.B., D.L.S.F., T.R.G., Y.B.-S., D.A.L.), University of Bristol, United Kingdom; Farr Institute of Health Informatics (J.P.C., A.D.H.), Department of Epidemiology and Public Health (M. Kivimaki, M. Kumari), Department of Women's Cancer, Institute for Women's Health, Faculty of Population Health Sciences (U.M., A.G.-M., E.F.), and Institute of Cardiovascular Science (A.D.H.), University College London, United Kingdom; Institute for Social and Economic Research, University of Essex, United Kingdom (M. Kumari); and Department of Physiology and Pharmacology, Institute of Biology, Federal University of Pelotas, Brazil (I.O.O.)
| | - Deise F Freitas
- From the Post-Graduate Program in Epidemiology, Federal University of Pelotas, Brazil (M.C.B., A.J.D.B., B.L.H., D.F.F., I.O.O.); MRC Integrative Epidemiology Unit (M.C.B., D.L.S.F., T.R.G., D.A.L.) and Population Health Sciences, Bristol Medical School (M.C.B., D.L.S.F., T.R.G., Y.B.-S., D.A.L.), University of Bristol, United Kingdom; Farr Institute of Health Informatics (J.P.C., A.D.H.), Department of Epidemiology and Public Health (M. Kivimaki, M. Kumari), Department of Women's Cancer, Institute for Women's Health, Faculty of Population Health Sciences (U.M., A.G.-M., E.F.), and Institute of Cardiovascular Science (A.D.H.), University College London, United Kingdom; Institute for Social and Economic Research, University of Essex, United Kingdom (M. Kumari); and Department of Physiology and Pharmacology, Institute of Biology, Federal University of Pelotas, Brazil (I.O.O.)
| | - Isabel O Oliveira
- From the Post-Graduate Program in Epidemiology, Federal University of Pelotas, Brazil (M.C.B., A.J.D.B., B.L.H., D.F.F., I.O.O.); MRC Integrative Epidemiology Unit (M.C.B., D.L.S.F., T.R.G., D.A.L.) and Population Health Sciences, Bristol Medical School (M.C.B., D.L.S.F., T.R.G., Y.B.-S., D.A.L.), University of Bristol, United Kingdom; Farr Institute of Health Informatics (J.P.C., A.D.H.), Department of Epidemiology and Public Health (M. Kivimaki, M. Kumari), Department of Women's Cancer, Institute for Women's Health, Faculty of Population Health Sciences (U.M., A.G.-M., E.F.), and Institute of Cardiovascular Science (A.D.H.), University College London, United Kingdom; Institute for Social and Economic Research, University of Essex, United Kingdom (M. Kumari); and Department of Physiology and Pharmacology, Institute of Biology, Federal University of Pelotas, Brazil (I.O.O.)
| | - Aleksandra Gentry-Maharaj
- From the Post-Graduate Program in Epidemiology, Federal University of Pelotas, Brazil (M.C.B., A.J.D.B., B.L.H., D.F.F., I.O.O.); MRC Integrative Epidemiology Unit (M.C.B., D.L.S.F., T.R.G., D.A.L.) and Population Health Sciences, Bristol Medical School (M.C.B., D.L.S.F., T.R.G., Y.B.-S., D.A.L.), University of Bristol, United Kingdom; Farr Institute of Health Informatics (J.P.C., A.D.H.), Department of Epidemiology and Public Health (M. Kivimaki, M. Kumari), Department of Women's Cancer, Institute for Women's Health, Faculty of Population Health Sciences (U.M., A.G.-M., E.F.), and Institute of Cardiovascular Science (A.D.H.), University College London, United Kingdom; Institute for Social and Economic Research, University of Essex, United Kingdom (M. Kumari); and Department of Physiology and Pharmacology, Institute of Biology, Federal University of Pelotas, Brazil (I.O.O.)
| | - Evangelia Fourkala
- From the Post-Graduate Program in Epidemiology, Federal University of Pelotas, Brazil (M.C.B., A.J.D.B., B.L.H., D.F.F., I.O.O.); MRC Integrative Epidemiology Unit (M.C.B., D.L.S.F., T.R.G., D.A.L.) and Population Health Sciences, Bristol Medical School (M.C.B., D.L.S.F., T.R.G., Y.B.-S., D.A.L.), University of Bristol, United Kingdom; Farr Institute of Health Informatics (J.P.C., A.D.H.), Department of Epidemiology and Public Health (M. Kivimaki, M. Kumari), Department of Women's Cancer, Institute for Women's Health, Faculty of Population Health Sciences (U.M., A.G.-M., E.F.), and Institute of Cardiovascular Science (A.D.H.), University College London, United Kingdom; Institute for Social and Economic Research, University of Essex, United Kingdom (M. Kumari); and Department of Physiology and Pharmacology, Institute of Biology, Federal University of Pelotas, Brazil (I.O.O.)
| | - Debbie A Lawlor
- From the Post-Graduate Program in Epidemiology, Federal University of Pelotas, Brazil (M.C.B., A.J.D.B., B.L.H., D.F.F., I.O.O.); MRC Integrative Epidemiology Unit (M.C.B., D.L.S.F., T.R.G., D.A.L.) and Population Health Sciences, Bristol Medical School (M.C.B., D.L.S.F., T.R.G., Y.B.-S., D.A.L.), University of Bristol, United Kingdom; Farr Institute of Health Informatics (J.P.C., A.D.H.), Department of Epidemiology and Public Health (M. Kivimaki, M. Kumari), Department of Women's Cancer, Institute for Women's Health, Faculty of Population Health Sciences (U.M., A.G.-M., E.F.), and Institute of Cardiovascular Science (A.D.H.), University College London, United Kingdom; Institute for Social and Economic Research, University of Essex, United Kingdom (M. Kumari); and Department of Physiology and Pharmacology, Institute of Biology, Federal University of Pelotas, Brazil (I.O.O.)
| | - Aroon D Hingorani
- From the Post-Graduate Program in Epidemiology, Federal University of Pelotas, Brazil (M.C.B., A.J.D.B., B.L.H., D.F.F., I.O.O.); MRC Integrative Epidemiology Unit (M.C.B., D.L.S.F., T.R.G., D.A.L.) and Population Health Sciences, Bristol Medical School (M.C.B., D.L.S.F., T.R.G., Y.B.-S., D.A.L.), University of Bristol, United Kingdom; Farr Institute of Health Informatics (J.P.C., A.D.H.), Department of Epidemiology and Public Health (M. Kivimaki, M. Kumari), Department of Women's Cancer, Institute for Women's Health, Faculty of Population Health Sciences (U.M., A.G.-M., E.F.), and Institute of Cardiovascular Science (A.D.H.), University College London, United Kingdom; Institute for Social and Economic Research, University of Essex, United Kingdom (M. Kumari); and Department of Physiology and Pharmacology, Institute of Biology, Federal University of Pelotas, Brazil (I.O.O.)
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22
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Speed D, Cai N, Johnson MR, Nejentsev S, Balding DJ. Reevaluation of SNP heritability in complex human traits. Nat Genet 2017; 49:986-992. [PMID: 28530675 PMCID: PMC5493198 DOI: 10.1038/ng.3865] [Citation(s) in RCA: 250] [Impact Index Per Article: 35.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Accepted: 04/18/2017] [Indexed: 12/15/2022]
Abstract
SNP heritability, the proportion of phenotypic variance explained by SNPs, has been reported for many hundreds of traits. Its estimation requires strong prior assumptions about the distribution of heritability across the genome, but the assumptions in current use have not been thoroughly tested. By analyzing imputed data for a large number of human traits, we empirically derive a model that more accurately describes how heritability varies with minor allele frequency, linkage disequilibrium and genotype certainty. Across 19 traits, our improved model leads to estimates of common SNP heritability on average 43% (standard deviation 3) higher than those obtained from the widely-used software GCTA, and 25% (standard deviation 2) higher than those from the recently-proposed extension GCTA-LDMS. Previously, DNaseI hypersensitivity sites were reported to explain 79% of SNP heritability; using our improved heritability model their estimated contribution is only 24%.
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Affiliation(s)
- Doug Speed
- UCL Genetics Institute, University College London, London, UK
| | - Na Cai
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, UK.,European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK
| | | | | | | | - David J Balding
- UCL Genetics Institute, University College London, London, UK.,Centre for Systems Genomics, School of BioSciences, and School of Mathematics and Statistics, University of Melbourne, Melbourne, Victoria, Australia
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23
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Lane CA, Parker TD, Cash DM, Macpherson K, Donnachie E, Murray-Smith H, Barnes A, Barker S, Beasley DG, Bras J, Brown D, Burgos N, Byford M, Jorge Cardoso M, Carvalho A, Collins J, De Vita E, Dickson JC, Epie N, Espak M, Henley SMD, Hoskote C, Hutel M, Klimova J, Malone IB, Markiewicz P, Melbourne A, Modat M, Schrag A, Shah S, Sharma N, Sudre CH, Thomas DL, Wong A, Zhang H, Hardy J, Zetterberg H, Ourselin S, Crutch SJ, Kuh D, Richards M, Fox NC, Schott JM. Study protocol: Insight 46 - a neuroscience sub-study of the MRC National Survey of Health and Development. BMC Neurol 2017; 17:75. [PMID: 28420323 PMCID: PMC5395844 DOI: 10.1186/s12883-017-0846-x] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2017] [Accepted: 03/21/2017] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Increasing age is the biggest risk factor for dementia, of which Alzheimer's disease is the commonest cause. The pathological changes underpinning Alzheimer's disease are thought to develop at least a decade prior to the onset of symptoms. Molecular positron emission tomography and multi-modal magnetic resonance imaging allow key pathological processes underpinning cognitive impairment - including β-amyloid depostion, vascular disease, network breakdown and atrophy - to be assessed repeatedly and non-invasively. This enables potential determinants of dementia to be delineated earlier, and therefore opens a pre-symptomatic window where intervention may prevent the onset of cognitive symptoms. METHODS/DESIGN This paper outlines the clinical, cognitive and imaging protocol of "Insight 46", a neuroscience sub-study of the MRC National Survey of Health and Development. This is one of the oldest British birth cohort studies and has followed 5362 individuals since their birth in England, Scotland and Wales during one week in March 1946. These individuals have been tracked in 24 waves of data collection incorporating a wide range of health and functional measures, including repeat measures of cognitive function. Now aged 71 years, a small fraction have overt dementia, but estimates suggest that ~1/3 of individuals in this age group may be in the preclinical stages of Alzheimer's disease. Insight 46 is recruiting 500 study members selected at random from those who attended a clinical visit at 60-64 years and on whom relevant lifecourse data are available. We describe the sub-study design and protocol which involves a prospective two time-point (0, 24 month) data collection covering clinical, neuropsychological, β-amyloid positron emission tomography and magnetic resonance imaging, biomarker and genetic information. Data collection started in 2015 (age 69) and aims to be completed in 2019 (age 73). DISCUSSION Through the integration of data on the socioeconomic environment and on physical, psychological and cognitive function from 0 to 69 years, coupled with genetics, structural and molecular imaging, and intensive cognitive and neurological phenotyping, Insight 46 aims to identify lifetime factors which influence brain health and cognitive ageing, with particular focus on Alzheimer's disease and cerebrovascular disease. This will provide an evidence base for the rational design of disease-modifying trials.
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Affiliation(s)
- Christopher A. Lane
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Thomas D. Parker
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Dave M. Cash
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Kirsty Macpherson
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Elizabeth Donnachie
- Leonard Wolfson Experimental Neurology Centre, Institute of Neurology, University College London, London, UK
| | - Heidi Murray-Smith
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Anna Barnes
- Institute of Nuclear Medicine, University College London Hospitals, London, UK
| | - Suzie Barker
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Daniel G. Beasley
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Jose Bras
- Department of Molecular Neuroscience, Institute of Neurology, University College London, London, UK
- Department of Medical Sciences and Institute of Biomedicine - iBiMED, University of Aveiro, Aveiro, Portugal
| | - David Brown
- Institute of Nuclear Medicine, University College London Hospitals, London, UK
| | - Ninon Burgos
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | | | - M. Jorge Cardoso
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Ana Carvalho
- Institute of Nuclear Medicine, University College London Hospitals, London, UK
| | - Jessica Collins
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Enrico De Vita
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London, UK
| | - John C. Dickson
- Institute of Nuclear Medicine, University College London Hospitals, London, UK
| | - Norah Epie
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Miklos Espak
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Susie M. D. Henley
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Chandrashekar Hoskote
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
| | - Michael Hutel
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Jana Klimova
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Ian B. Malone
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Pawel Markiewicz
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Andrew Melbourne
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Marc Modat
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Anette Schrag
- Department of Clinical Neuroscience, Institute of Neurology, University College London, London, UK
| | - Sachit Shah
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London, UK
| | - Nikhil Sharma
- MRC Unit for Lifelong Health and Ageing at UCL, London, UK
- National Hospital for Neurology and Neurosurgery, London, UK
| | - Carole H. Sudre
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - David L. Thomas
- Leonard Wolfson Experimental Neurology Centre, Institute of Neurology, University College London, London, UK
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London, UK
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, London, UK
| | - Hui Zhang
- Department of Computer Science and Centre for Medical Image Computing, University College London, London, UK
| | - John Hardy
- Reta Lila Weston Research Laboratories, Department of Molecular Neuroscience, Institute of Neurology, University College London, London, UK
| | - Henrik Zetterberg
- Department of Molecular Neuroscience, Institute of Neurology, University College London, London, UK
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Sebastien Ourselin
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Sebastian J. Crutch
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Diana Kuh
- MRC Unit for Lifelong Health and Ageing at UCL, London, UK
| | | | - Nick C. Fox
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Jonathan M. Schott
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
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24
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Functional Analysis of the Coronary Heart Disease Risk Locus on Chromosome 21q22. DISEASE MARKERS 2017; 2017:1096916. [PMID: 28458444 PMCID: PMC5387827 DOI: 10.1155/2017/1096916] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Accepted: 12/13/2016] [Indexed: 12/15/2022]
Abstract
Background. The coronary heart disease (CHD) risk locus on 21q22 (lead SNP rs9982601) lies within a “gene desert.” The aim of this study was to assess if this locus is associated with CHD risk factors and to identify the functional variant(s) and gene(s) involved. Methods. A phenome scan was performed with UCLEB Consortium data. Allele-specific protein binding was studied using electrophoretic mobility shift assays. Dual-reporter luciferase assays were used to assess the impact of genetic variation on expression. Expression quantitative trait analysis was performed with Advanced Study of Aortic Pathology (ASAP) and Genotype-Tissue Expression (GTEx) consortium data. Results. A suggestive association between QT interval and the locus was observed (rs9982601 p = 0.04). One variant at the locus, rs28451064, showed allele-specific protein binding and its minor allele showed 12% higher luciferase expression (p = 4.82 × 10−3) compared to the common allele. The minor allele of rs9982601 was associated with higher expression of the closest upstream genes (SLC5A3 1.30-fold increase p = 3.98 × 10−5; MRPS6 1.15-fold increase p = 9.60 × 10−4) in aortic intima media in ASAP. Both rs9982601 and rs28451064 showed a suggestive association with MRPS6 expression in relevant tissues in the GTEx data. Conclusions. A candidate functional variant, rs28451064, was identified. Future work should focus on identifying the pathway(s) involved.
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25
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Zheng J, Rodriguez S, Laurin C, Baird D, Trela-Larsen L, Erzurumluoglu MA, Zheng Y, White J, Giambartolomei C, Zabaneh D, Morris R, Kumari M, Casas JP, Hingorani AD, Evans DM, Gaunt TR, Day INM. HAPRAP: a haplotype-based iterative method for statistical fine mapping using GWAS summary statistics. Bioinformatics 2017; 33:79-86. [PMID: 27591082 PMCID: PMC5544112 DOI: 10.1093/bioinformatics/btw565] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2014] [Revised: 04/29/2016] [Accepted: 08/26/2016] [Indexed: 11/21/2022] Open
Abstract
MOTIVATION Fine mapping is a widely used approach for identifying the causal variant(s) at disease-associated loci. Standard methods (e.g. multiple regression) require individual level genotypes. Recent fine mapping methods using summary-level data require the pairwise correlation coefficients ([Formula: see text]) of the variants. However, haplotypes rather than pairwise [Formula: see text], are the true biological representation of linkage disequilibrium (LD) among multiple loci. In this article, we present an empirical iterative method, HAPlotype Regional Association analysis Program (HAPRAP), that enables fine mapping using summary statistics and haplotype information from an individual-level reference panel. RESULTS Simulations with individual-level genotypes show that the results of HAPRAP and multiple regression are highly consistent. In simulation with summary-level data, we demonstrate that HAPRAP is less sensitive to poor LD estimates. In a parametric simulation using Genetic Investigation of ANthropometric Traits height data, HAPRAP performs well with a small training sample size (N < 2000) while other methods become suboptimal. Moreover, HAPRAP's performance is not affected substantially by single nucleotide polymorphisms (SNPs) with low minor allele frequencies. We applied the method to existing quantitative trait and binary outcome meta-analyses (human height, QTc interval and gallbladder disease); all previous reported association signals were replicated and two additional variants were independently associated with human height. Due to the growing availability of summary level data, the value of HAPRAP is likely to increase markedly for future analyses (e.g. functional prediction and identification of instruments for Mendelian randomization). AVAILABILITY AND IMPLEMENTATION The HAPRAP package and documentation are available at http://apps.biocompute.org.uk/haprap/ CONTACT: : jie.zheng@bristol.ac.uk or tom.gaunt@bristol.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jie Zheng
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, Bristol, UK
- School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Santiago Rodriguez
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, Bristol, UK
- School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Charles Laurin
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, Bristol, UK
- School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Denis Baird
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, Bristol, UK
| | - Lea Trela-Larsen
- School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Mesut A Erzurumluoglu
- School of Social and Community Medicine, University of Bristol, Bristol, UK
- Department of Health Sciences, Genetic Epidemiology Group, University of Leicester, Leicester, UK
| | - Yi Zheng
- Dedman College of Humanities and Sciences, Southern Methodist University, Dallas, TX, USA
| | - Jon White
- Department of Genetics, Environment and Evolution, University College London Genetics Institute, London, UK
| | - Claudia Giambartolomei
- Department of Genetics, Environment and Evolution, University College London Genetics Institute, London, UK
| | - Delilah Zabaneh
- Department of Genetics, Environment and Evolution, University College London Genetics Institute, London, UK
| | - Richard Morris
- School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Meena Kumari
- Department of Genetics, Environment and Evolution, University College London Genetics Institute, London, UK
| | - Juan P Casas
- Department of Genetics, Environment and Evolution, University College London Genetics Institute, London, UK
- Department of Primary Care & Population Health, University College London, Royal Free Campus, London, UK
| | - Aroon D Hingorani
- Department of Genetics, Environment and Evolution, University College London Genetics Institute, London, UK
- Centre for Clinical Pharmacology, University College London, London, UK, Division of Medicine
| | | | - David M Evans
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, Bristol, UK
- University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Australia, QLD
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, Bristol, UK
- School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Ian N M Day
- School of Social and Community Medicine, University of Bristol, Bristol, UK
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26
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Beaney KE, Cooper JA, McLachlan S, Wannamethee SG, Jefferis BJ, Whincup P, Ben-Shlomo Y, Price JF, Kumari M, Wong A, Ong K, Hardy R, Kuh D, Kivimaki M, Kangas AJ, Soininen P, Ala-Korpela M, Drenos F, Humphries SE. Variant rs10911021 that associates with coronary heart disease in type 2 diabetes, is associated with lower concentrations of circulating HDL cholesterol and large HDL particles but not with amino acids. Cardiovasc Diabetol 2016; 15:115. [PMID: 27549350 PMCID: PMC4994200 DOI: 10.1186/s12933-016-0435-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2016] [Accepted: 08/10/2016] [Indexed: 12/18/2022] Open
Abstract
AIMS An intergenic locus on chromosome 1 (lead SNP rs10911021) was previously associated with coronary heart disease (CHD) in type 2 diabetes (T2D). Using data from the UCLEB consortium we investigated the relationship between rs10911021 and CHD in T2D, whether rs10911021 was associated with levels of amino acids involved in the γ-glutamyl cycle or any conventional risk factors (CRFs) for CHD in the T2D participants. METHODS Four UCLEB studies (n = 6531) had rs10911021 imputation, CHD in T2D, CRF and metabolomics data determined using a nuclear magnetic resonance based platform. RESULTS The expected direction of effect between rs10911021 and CHD in T2D was observed (1377 no CHD/160 CHD; minor allele OR 0.80, 95 % CI 0.60-1.06) although this was not statistically significant (p = 0.13). No association between rs10911021 and CHD was seen in non-T2D participants (11218 no CHD/1274 CHD; minor allele OR 1.00 95 % CIs 0.92-1.10). In T2D participants, while no associations were observed between rs10911021 and the nine amino acids measured, rs10911021 was associated with HDL-cholesterol (p = 0.0005) but the minor "protective" allele was associated with lower levels (-0.034 mmol/l per allele). Focusing more closely on the HDL-cholesterol subclasses measured, we observed that rs10911021 was associated with six large HDL particle measures in T2D (all p < 0.001). No significant associations were seen in non-T2D subjects. CONCLUSIONS Our findings are consistent with a true association between rs10911021 and CHD in T2D. The protective minor allele was associated with lower HDL-cholesterol and reductions in HDL particle traits. Our results indicate a complex relationship between rs10911021 and CHD in T2D.
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Affiliation(s)
- Katherine E Beaney
- Centre for Cardiovascular Genetics, British Heart Foundation Laboratories, Institute of Cardiovascular Science, University College London, University Street, London, UK
| | - Jackie A Cooper
- Centre for Cardiovascular Genetics, British Heart Foundation Laboratories, Institute of Cardiovascular Science, University College London, University Street, London, UK
| | - Stela McLachlan
- Centre for Population Health Sciences, The University of Edinburgh, Edinburgh, UK
| | - S Goya Wannamethee
- UCL Department of Primary Care & Population Health, UCL Institute of Epidemiology, University College London, London, UK
| | - Barbara J Jefferis
- UCL Department of Primary Care & Population Health, UCL Institute of Epidemiology, University College London, London, UK
| | - Peter Whincup
- Population Health Research Institute, St George's University of London, Cranmer Terrace, London, UK
| | - Yoav Ben-Shlomo
- School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Jacqueline F Price
- Centre for Population Health Sciences, The University of Edinburgh, Edinburgh, UK
| | - Meena Kumari
- Institute for Social and Economic Research, University of Essex, Colchester, UK.,Department of Epidemiology & Public Health, UCL Institute of Epidemiology & Health Care, University College London, London, UK
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing, London, UK
| | - Ken Ong
- MRC Unit for Lifelong Health and Ageing, London, UK.,MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, UK
| | | | - Diana Kuh
- MRC Unit for Lifelong Health and Ageing, London, UK
| | - Mika Kivimaki
- Department of Epidemiology & Public Health, UCL Institute of Epidemiology & Health Care, University College London, London, UK
| | - Antti J Kangas
- Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland
| | - Pasi Soininen
- 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
| | - Mika Ala-Korpela
- 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.,MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Fotios Drenos
- Centre for Cardiovascular Genetics, British Heart Foundation Laboratories, Institute of Cardiovascular Science, University College London, University Street, London, UK. .,MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, UK.
| | - Steve E Humphries
- Centre for Cardiovascular Genetics, British Heart Foundation Laboratories, Institute of Cardiovascular Science, University College London, University Street, London, UK
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27
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Morris RW, Cooper JA, Shah T, Wong A, Drenos F, Engmann J, McLachlan S, Jefferis B, Dale C, Hardy R, Kuh D, Ben-Shlomo Y, Wannamethee SG, Whincup PH, Casas JP, Kivimaki M, Kumari M, Talmud PJ, Price JF, Dudbridge F, Hingorani AD, Humphries SE. Marginal role for 53 common genetic variants in cardiovascular disease prediction. Heart 2016; 102:1640-7. [PMID: 27365493 PMCID: PMC5099215 DOI: 10.1136/heartjnl-2016-309298] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2016] [Accepted: 05/30/2016] [Indexed: 12/20/2022] Open
Abstract
Objective We investigated discrimination and calibration of cardiovascular disease (CVD) risk scores when genotypic was added to phenotypic information. The potential of genetic information for those at intermediate risk by a phenotype-based risk score was assessed. Methods Data were from seven prospective studies including 11 851 individuals initially free of CVD or diabetes, with 1444 incident CVD events over 10 years' follow-up. We calculated a score from 53 CVD-related single nucleotide polymorphisms and an established CVD risk equation ‘QRISK-2’ comprising phenotypic measures. The area under the receiver operating characteristic curve (AUROC), detection rate for given false-positive rate (FPR) and net reclassification improvement (NRI) index were estimated for gene scores alone and in addition to the QRISK-2 CVD risk score. We also evaluated use of genetic information only for those at intermediate risk according to QRISK-2. Results The AUROC was 0.635 for QRISK-2 alone and 0.623 with addition of the gene score. The detection rate for 5% FPR improved from 11.9% to 12.0% when the gene score was added. For a 10-year CVD risk cut-off point of 10%, the NRI was 0.25% when the gene score was added to QRISK-2. Applying the genetic risk score only to those with QRISK-2 risk of 10%–<20% and prescribing statins where risk exceeded 20% suggested that genetic information could prevent one additional event for every 462 people screened. Conclusion The gene score produced minimal incremental population-wide utility over phenotypic risk prediction of CVD. Tailored prediction using genetic information for those at intermediate risk may have clinical utility.
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Affiliation(s)
- Richard W Morris
- School of Social & Community Medicine, University of Bristol, Bristol, UK Department of Primary Care & Population Health, University College London, London, UK
| | - Jackie A Cooper
- Centre for Cardiovascular Genetics, Institute of Cardiovascular Science, University College London, London, UK
| | - Tina Shah
- Institute of Cardiovascular Science and Farr Institute, University College London, London, UK
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, London, UK
| | - Fotios Drenos
- Institute of Cardiovascular Science and Farr Institute, University College London, London, UK MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Jorgen Engmann
- Institute of Cardiovascular Science and Farr Institute, University College London, London, UK
| | - Stela McLachlan
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh, UK
| | - Barbara Jefferis
- Department of Primary Care & Population Health, University College London, London, UK
| | - Caroline Dale
- Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Rebecca Hardy
- MRC Unit for Lifelong Health and Ageing at UCL, London, UK
| | - Diana Kuh
- MRC Unit for Lifelong Health and Ageing at UCL, London, UK
| | - Yoav Ben-Shlomo
- School of Social & Community Medicine, University of Bristol, Bristol, UK
| | - S Goya Wannamethee
- Department of Primary Care & Population Health, University College London, London, UK
| | - Peter H Whincup
- Division of Population Health Sciences and Education, St George's, University of London, London, UK
| | - Juan-Pablo Casas
- Centre for Cardiovascular Genetics, Institute of Cardiovascular Science, University College London, London, UK
| | - Mika Kivimaki
- Department of Epidemiology & Public Health, UCL Institute of Epidemiology & Health Care, University College London, London, UK
| | - Meena Kumari
- Department of Epidemiology & Public Health, UCL Institute of Epidemiology & Health Care, University College London, London, UK Institute for Social and Economic Research, University of Essex, Colchester, UK
| | - Philippa J Talmud
- Centre for Cardiovascular Genetics, Institute of Cardiovascular Science, University College London, London, UK
| | - Jacqueline F Price
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh, UK
| | - Frank Dudbridge
- Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Aroon D Hingorani
- Institute of Cardiovascular Science and Farr Institute, University College London, London, UK
| | - Steve E Humphries
- Centre for Cardiovascular Genetics, Institute of Cardiovascular Science, University College London, London, UK
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Mendelian Randomisation study of the influence of eGFR on coronary heart disease. Sci Rep 2016; 6:28514. [PMID: 27338949 PMCID: PMC4919785 DOI: 10.1038/srep28514] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2015] [Accepted: 06/06/2016] [Indexed: 01/11/2023] Open
Abstract
Impaired kidney function, as measured by reduced estimated glomerular filtration rate (eGFR), has been associated with increased risk of coronary heart disease (CHD) in observational studies, but it is unclear whether this association is causal or the result of confounding or reverse causation. In this study we applied Mendelian randomisation analysis using 17 genetic variants previously associated with eGFR to investigate the causal role of kidney function on CHD. We used 13,145 participants from the UCL-LSHTM-Edinburgh-Bristol (UCLEB) Consortium and 194,427 participants from the Coronary ARtery DIsease Genome-wide Replication and Meta-analysis plus Coronary Artery Disease (CARDIoGRAMplusC4D) consortium. We observed significant association of an unweighted gene score with CHD risk (odds ratio = 0.983 per additional eGFR-increasing allele, 95% CI = 0.970–0.996, p = 0.008). However, using weights calculated from UCLEB, the gene score was not associated with disease risk (p = 0.11). These conflicting results could be explained by a single SNP, rs653178, which was not associated with eGFR in the UCLEB sample, but has known pleiotropic effects that prevent us from drawing a causal conclusion. The observational association between low eGFR and increased CHD risk was not explained by potential confounders, and there was no evidence of reverse causation, therefore leaving the remaining unexplained association as an open question.
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McLachlan S, Giambartolomei C, White J, Charoen P, Wong A, Finan C, Engmann J, Shah T, Hersch M, Podmore C, Cavadino A, Jefferis BJ, Dale CE, Hypponen E, Morris RW, Casas JP, Kumari M, Ben-Shlomo Y, Gaunt TR, Drenos F, Langenberg C, Kuh D, Kivimaki M, Rueedi R, Waeber G, Hingorani AD, Price JF, Walker AP. Replication and Characterization of Association between ABO SNPs and Red Blood Cell Traits by Meta-Analysis in Europeans. PLoS One 2016; 11:e0156914. [PMID: 27280446 PMCID: PMC4900668 DOI: 10.1371/journal.pone.0156914] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Accepted: 05/20/2016] [Indexed: 01/07/2023] Open
Abstract
Red blood cell (RBC) traits are routinely measured in clinical practice as important markers of health. Deviations from the physiological ranges are usually a sign of disease, although variation between healthy individuals also occurs, at least partly due to genetic factors. Recent large scale genetic studies identified loci associated with one or more of these traits; further characterization of known loci and identification of new loci is necessary to better understand their role in health and disease and to identify potential molecular mechanisms. We performed meta-analysis of Metabochip association results for six RBC traits—hemoglobin concentration (Hb), hematocrit (Hct), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), mean corpuscular volume (MCV) and red blood cell count (RCC)—in 11 093 Europeans from seven studies of the UCL-LSHTM-Edinburgh-Bristol (UCLEB) Consortium. We identified 394 non-overlapping SNPs in five loci at genome-wide significance: 6p22.1-6p21.33 (with HFE among others), 6q23.2 (with HBS1L among others), 6q23.3 (contains no genes), 9q34.3 (only ABO gene) and 22q13.1 (with TMPRSS6 among others), replicating previous findings of association with RBC traits at these loci and extending them by imputation to 1000 Genomes. We further characterized associations between ABO SNPs and three traits: hemoglobin, hematocrit and red blood cell count, replicating them in an independent cohort. Conditional analyses indicated the independent association of each of these traits with ABO SNPs and a role for blood group O in mediating the association. The 15 most significant RBC-associated ABO SNPs were also associated with five cardiometabolic traits, with discordance in the direction of effect between groups of traits, suggesting that ABO may act through more than one mechanism to influence cardiometabolic risk.
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Affiliation(s)
- Stela McLachlan
- Centre for Population Health Sciences, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, United Kingdom
- * E-mail:
| | - Claudia Giambartolomei
- Department of Psychiatry, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, The Leon and Norma Hess Center for Science and Medicine, New York, New York, United States of America
| | - Jon White
- University College London Genetics Institute, Department of Genetics, Environment and Evolution, London, United Kingdom
| | - Pimphen Charoen
- Department of Non-communicable Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, London, United Kingdom
| | - Chris Finan
- Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - Jorgen Engmann
- Genetic Epidemiology Group, Institute of Cardiovascular Science, University College London, London, United Kingdom
- Farr Institute of Health Informatics Research, Department of Epidemiology & Public Health, University College London, London, United Kingdom
| | - Tina Shah
- Genetic Epidemiology Group, Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - Micha Hersch
- Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Clara Podmore
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Alana Cavadino
- Centre for Environmental and Preventive Medicine, Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, United Kingdom
- Population, Policy and Practice, UCL Institute of Child Health, University College London, London, United Kingdom
| | - Barbara J. Jefferis
- Department of Primary Care & Population Health, UCL Institute of Epidemiology & Health Care, University College London, London, United Kingdom
| | - Caroline E. Dale
- Farr Institute of Health Informatics Research, Department of Epidemiology & Public Health, University College London, London, United Kingdom
| | - Elina Hypponen
- Population, Policy and Practice, UCL Institute of Child Health, University College London, London, United Kingdom
- Centre for Population Health Research, School of Health Sciences and Sansom Institute of Health Research, University of South Australia, Adelaide, Australia
- South Australian Health and Medical Research Institute, Adelaide, Australia
| | - Richard W. Morris
- Department of Primary Care & Population Health, UCL Institute of Epidemiology & Health Care, University College London, London, United Kingdom
- School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
| | - Juan P. Casas
- Institute of Health Informatics, University College London, London, United Kingdom
| | - Meena Kumari
- Institute for Social and Economic Research, University of Essex, Colchester, United Kingdom
- Department of Epidemiology & Public Health, UCL Institute of Epidemiology & Health Care, University College London, London, United Kingdom
| | - Yoav Ben-Shlomo
- School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
| | - Tom R. Gaunt
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
| | - Fotios Drenos
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
- Centre for Cardiovascular Genetics, Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
| | - Diana Kuh
- MRC Unit for Lifelong Health and Ageing at UCL, London, United Kingdom
| | - Mika Kivimaki
- Department of Epidemiology & Public Health, UCL Institute of Epidemiology & Health Care, University College London, London, United Kingdom
| | - Rico Rueedi
- Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Gerard Waeber
- Department of Internal Medicine, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
| | - Aroon D. Hingorani
- Institute of Cardiovascular Science, University College London, London, United Kingdom
- Farr Institute of Health Informatics Research, Department of Epidemiology & Public Health, University College London, London, United Kingdom
| | - Jacqueline F. Price
- Centre for Population Health Sciences, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, United Kingdom
| | - Ann P. Walker
- Centre for Cardiovascular Genetics, Institute of Cardiovascular Science, University College London, London, United Kingdom
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30
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Drenos F, Davey Smith G, Ala-Korpela M, Kettunen J, Würtz P, Soininen P, Kangas AJ, Dale C, Lawlor DA, Gaunt TR, Casas JP, Timpson NJ. Metabolic Characterization of a Rare Genetic Variation Within APOC3 and Its Lipoprotein Lipase-Independent Effects. CIRCULATION. CARDIOVASCULAR GENETICS 2016; 9:231-9. [PMID: 27114411 PMCID: PMC4920206 DOI: 10.1161/circgenetics.115.001302] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2015] [Accepted: 04/21/2016] [Indexed: 12/22/2022]
Abstract
BACKGROUND Plasma triglyceride levels have been implicated in atherosclerosis and coronary heart disease. Apolipoprotein C-III (APOC3) plays a key role in the hydrolysis of triglyceride-rich lipoproteins to remnant particles by lipoprotein lipase (LPL) and their uptake by the liver. A rare variant in APOC3(rs138326449) has been associated with triglyceride, very low-density lipoprotein, and high-density lipoprotein levels, as well as risk of coronary heart disease. We aimed to characterize the impact of this locus across a broad set of mainly lipids-focused metabolic measures. METHODS AND RESULTS A high-throughput serum nuclear magnetic resonance metabolomics platform was used to quantify 225 metabolic measures in 13 285 participants from 2 European population cohorts. We analyzed the effect of the APOC3 variant on the metabolic measures and used the common LPL(rs12678919) polymorphism to test for LPL-independent effects. Eighty-one metabolic measures showed evidence of association with APOC3(rs138326449). In addition to previously reported triglyceride and high-density lipoprotein associations, the variant was also associated with very low-density lipoprotein and high-density lipoprotein composition measures, other cholesterol measures, and fatty acids. Comparison of the APOC3 and LPL associations revealed that APOC3 association results for medium and very large very low-density lipoprotein composition are unlikely to be solely predictable by the action of APOC3 through LPL. CONCLUSIONS We characterized the effects of the rare APOC3(rs138326449) loss of function mutation in lipoprotein metabolism, as well as the effects of LPL(rs12678919). Our results improve our understanding of the role of APOC3 in triglyceride metabolism, its LPL independent action, and the complex and correlated nature of human metabolites.
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Affiliation(s)
- Fotios Drenos
- From the MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol (F.D., G.D.S., M.A.-K., D.A.L., T.R.G., N.J.T.); Institute of Cardiovascular Science, University College London, London, United Kingdom (F.D., C.D., J.-P.C.); Computational Medicine, Faculty of Medicine, University of Oulu & Biocenter Oulu, Oulu (M.A.-K., J.K., P.W., P.S., A.J.K.); NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio (M.A.-K., J.K., P.S.); Public Health Genomics Unit, Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland (J.K.); and Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom (C.D., J.-P.C.).
| | - George Davey Smith
- From the MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol (F.D., G.D.S., M.A.-K., D.A.L., T.R.G., N.J.T.); Institute of Cardiovascular Science, University College London, London, United Kingdom (F.D., C.D., J.-P.C.); Computational Medicine, Faculty of Medicine, University of Oulu & Biocenter Oulu, Oulu (M.A.-K., J.K., P.W., P.S., A.J.K.); NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio (M.A.-K., J.K., P.S.); Public Health Genomics Unit, Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland (J.K.); and Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom (C.D., J.-P.C.)
| | - Mika Ala-Korpela
- From the MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol (F.D., G.D.S., M.A.-K., D.A.L., T.R.G., N.J.T.); Institute of Cardiovascular Science, University College London, London, United Kingdom (F.D., C.D., J.-P.C.); Computational Medicine, Faculty of Medicine, University of Oulu & Biocenter Oulu, Oulu (M.A.-K., J.K., P.W., P.S., A.J.K.); NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio (M.A.-K., J.K., P.S.); Public Health Genomics Unit, Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland (J.K.); and Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom (C.D., J.-P.C.)
| | - Johannes Kettunen
- From the MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol (F.D., G.D.S., M.A.-K., D.A.L., T.R.G., N.J.T.); Institute of Cardiovascular Science, University College London, London, United Kingdom (F.D., C.D., J.-P.C.); Computational Medicine, Faculty of Medicine, University of Oulu & Biocenter Oulu, Oulu (M.A.-K., J.K., P.W., P.S., A.J.K.); NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio (M.A.-K., J.K., P.S.); Public Health Genomics Unit, Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland (J.K.); and Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom (C.D., J.-P.C.)
| | - Peter Würtz
- From the MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol (F.D., G.D.S., M.A.-K., D.A.L., T.R.G., N.J.T.); Institute of Cardiovascular Science, University College London, London, United Kingdom (F.D., C.D., J.-P.C.); Computational Medicine, Faculty of Medicine, University of Oulu & Biocenter Oulu, Oulu (M.A.-K., J.K., P.W., P.S., A.J.K.); NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio (M.A.-K., J.K., P.S.); Public Health Genomics Unit, Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland (J.K.); and Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom (C.D., J.-P.C.)
| | - Pasi Soininen
- From the MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol (F.D., G.D.S., M.A.-K., D.A.L., T.R.G., N.J.T.); Institute of Cardiovascular Science, University College London, London, United Kingdom (F.D., C.D., J.-P.C.); Computational Medicine, Faculty of Medicine, University of Oulu & Biocenter Oulu, Oulu (M.A.-K., J.K., P.W., P.S., A.J.K.); NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio (M.A.-K., J.K., P.S.); Public Health Genomics Unit, Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland (J.K.); and Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom (C.D., J.-P.C.)
| | - Antti J Kangas
- From the MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol (F.D., G.D.S., M.A.-K., D.A.L., T.R.G., N.J.T.); Institute of Cardiovascular Science, University College London, London, United Kingdom (F.D., C.D., J.-P.C.); Computational Medicine, Faculty of Medicine, University of Oulu & Biocenter Oulu, Oulu (M.A.-K., J.K., P.W., P.S., A.J.K.); NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio (M.A.-K., J.K., P.S.); Public Health Genomics Unit, Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland (J.K.); and Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom (C.D., J.-P.C.)
| | - Caroline Dale
- From the MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol (F.D., G.D.S., M.A.-K., D.A.L., T.R.G., N.J.T.); Institute of Cardiovascular Science, University College London, London, United Kingdom (F.D., C.D., J.-P.C.); Computational Medicine, Faculty of Medicine, University of Oulu & Biocenter Oulu, Oulu (M.A.-K., J.K., P.W., P.S., A.J.K.); NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio (M.A.-K., J.K., P.S.); Public Health Genomics Unit, Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland (J.K.); and Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom (C.D., J.-P.C.)
| | - Debbie A Lawlor
- From the MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol (F.D., G.D.S., M.A.-K., D.A.L., T.R.G., N.J.T.); Institute of Cardiovascular Science, University College London, London, United Kingdom (F.D., C.D., J.-P.C.); Computational Medicine, Faculty of Medicine, University of Oulu & Biocenter Oulu, Oulu (M.A.-K., J.K., P.W., P.S., A.J.K.); NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio (M.A.-K., J.K., P.S.); Public Health Genomics Unit, Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland (J.K.); and Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom (C.D., J.-P.C.)
| | - Tom R Gaunt
- From the MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol (F.D., G.D.S., M.A.-K., D.A.L., T.R.G., N.J.T.); Institute of Cardiovascular Science, University College London, London, United Kingdom (F.D., C.D., J.-P.C.); Computational Medicine, Faculty of Medicine, University of Oulu & Biocenter Oulu, Oulu (M.A.-K., J.K., P.W., P.S., A.J.K.); NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio (M.A.-K., J.K., P.S.); Public Health Genomics Unit, Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland (J.K.); and Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom (C.D., J.-P.C.)
| | - Juan-Pablo Casas
- From the MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol (F.D., G.D.S., M.A.-K., D.A.L., T.R.G., N.J.T.); Institute of Cardiovascular Science, University College London, London, United Kingdom (F.D., C.D., J.-P.C.); Computational Medicine, Faculty of Medicine, University of Oulu & Biocenter Oulu, Oulu (M.A.-K., J.K., P.W., P.S., A.J.K.); NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio (M.A.-K., J.K., P.S.); Public Health Genomics Unit, Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland (J.K.); and Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom (C.D., J.-P.C.)
| | - Nicholas J Timpson
- From the MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol (F.D., G.D.S., M.A.-K., D.A.L., T.R.G., N.J.T.); Institute of Cardiovascular Science, University College London, London, United Kingdom (F.D., C.D., J.-P.C.); Computational Medicine, Faculty of Medicine, University of Oulu & Biocenter Oulu, Oulu (M.A.-K., J.K., P.W., P.S., A.J.K.); NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio (M.A.-K., J.K., P.S.); Public Health Genomics Unit, Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland (J.K.); and Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom (C.D., J.-P.C.).
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White J, Sofat R, Hemani G, Shah T, Engmann J, Dale C, Shah S, Kruger FA, Giambartolomei C, Swerdlow DI, Palmer T, McLachlan S, Langenberg C, Zabaneh D, Lovering R, Cavadino A, Jefferis B, Finan C, Wong A, Amuzu A, Ong K, Gaunt TR, Warren H, Davies TL, Drenos F, Cooper J, Ebrahim S, Lawlor DA, Talmud PJ, Humphries SE, Power C, Hypponen E, Richards M, Hardy R, Kuh D, Wareham N, Ben-Shlomo Y, Day IN, Whincup P, Morris R, Strachan MWJ, Price J, Kumari M, Kivimaki M, Plagnol V, Whittaker JC, Smith GD, Dudbridge F, Casas JP, Holmes MV, Hingorani AD. Plasma urate concentration and risk of coronary heart disease: a Mendelian randomisation analysis. Lancet Diabetes Endocrinol 2016; 4:327-36. [PMID: 26781229 PMCID: PMC4805857 DOI: 10.1016/s2213-8587(15)00386-1] [Citation(s) in RCA: 93] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2015] [Revised: 10/02/2015] [Accepted: 10/06/2015] [Indexed: 12/20/2022]
Abstract
BACKGROUND Increased circulating plasma urate concentration is associated with an increased risk of coronary heart disease, but the extent of any causative effect of urate on risk of coronary heart disease is still unclear. In this study, we aimed to clarify any causal role of urate on coronary heart disease risk using Mendelian randomisation analysis. METHODS We first did a fixed-effects meta-analysis of the observational association of plasma urate and risk of coronary heart disease. We then used a conventional Mendelian randomisation approach to investigate the causal relevance using a genetic instrument based on 31 urate-associated single nucleotide polymorphisms (SNPs). To account for potential pleiotropic associations of certain SNPs with risk factors other than urate, we additionally did both a multivariable Mendelian randomisation analysis, in which the genetic associations of SNPs with systolic and diastolic blood pressure, HDL cholesterol, and triglycerides were included as covariates, and an Egger Mendelian randomisation (MR-Egger) analysis to estimate a causal effect accounting for unmeasured pleiotropy. FINDINGS In the meta-analysis of 17 prospective observational studies (166 486 individuals; 9784 coronary heart disease events) a 1 SD higher urate concentration was associated with an odds ratio (OR) for coronary heart disease of 1·07 (95% CI 1·04-1·10). The corresponding OR estimates from the conventional, multivariable adjusted, and Egger Mendelian randomisation analysis (58 studies; 198 598 individuals; 65 877 events) were 1·18 (95% CI 1·08-1·29), 1·10 (1·00-1·22), and 1·05 (0·92-1·20), respectively, per 1 SD increment in plasma urate. INTERPRETATION Conventional and multivariate Mendelian randomisation analysis implicates a causal role for urate in the development of coronary heart disease, but these estimates might be inflated by hidden pleiotropy. Egger Mendelian randomisation analysis, which accounts for pleiotropy but has less statistical power, suggests there might be no causal effect. These results might help investigators to determine the priority of trials of urate lowering for the prevention of coronary heart disease compared with other potential interventions. FUNDING UK National Institute for Health Research, British Heart Foundation, and UK Medical Research Council.
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Affiliation(s)
- Jon White
- UCL Genetics Institute, University College, London, UK.
| | - Reecha Sofat
- Centre for Clinical Pharmacology, University College London, London, UK
| | - Gibran Hemani
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Tina Shah
- Institute of Cardiovascular Science and Farr Institute, University College London, London, UK
| | - Jorgen Engmann
- Institute of Cardiovascular Science and Farr Institute, University College London, London, UK
| | - Caroline Dale
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Sonia Shah
- Queensland Brain Institute, University of Queensland, QLD, Australia
| | - Felix A Kruger
- Institute of Cardiovascular Science and Farr Institute, University College London, London, UK
| | | | - Daniel I Swerdlow
- Institute of Cardiovascular Science and Farr Institute, University College London, London, UK
| | - Tom Palmer
- Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK
| | - Stela McLachlan
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh, UK
| | - Claudia Langenberg
- Institute of Cardiovascular Science and Farr Institute, University College London, London, UK; MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | | | - Ruth Lovering
- Centre for Cardiovascular Genetics, British Heart Foundation Laboratories, Institute of Cardiovascular Science, University College London, London, UK
| | - Alana Cavadino
- Population, Policy and Practice, UCL Institute of Child Health, University College London, London, UnK
| | - Barbara Jefferis
- Department of Primary Care & Population Health, University College London, Royal Free Campus, London, UK
| | - Chris Finan
- Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, London, UK
| | - Antoinette Amuzu
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Ken Ong
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK; MRC Unit for Lifelong Health and Ageing at UCL, London, UK
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, UK; School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Helen Warren
- Department of Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Denstistry, Queen Mary University of London, London, UK; NIHR Barts Cardiovascular Biomedical Research Unit, Queen Mary University of London, London, UK
| | - Teri-Louise Davies
- School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Fotios Drenos
- Centre for Cardiovascular Genetics, British Heart Foundation Laboratories, Institute of Cardiovascular Science, University College London, London, UK; MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Jackie Cooper
- Centre for Cardiovascular Genetics, British Heart Foundation Laboratories, Institute of Cardiovascular Science, University College London, London, UK
| | - Shah Ebrahim
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Debbie A Lawlor
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Philippa J Talmud
- Centre for Cardiovascular Genetics, British Heart Foundation Laboratories, Institute of Cardiovascular Science, University College London, London, UK
| | - Steve E Humphries
- Centre for Cardiovascular Genetics, British Heart Foundation Laboratories, Institute of Cardiovascular Science, University College London, London, UK
| | - Christine Power
- Population, Policy and Practice, UCL Institute of Child Health, University College London, London, UnK
| | - Elina Hypponen
- Population, Policy and Practice, UCL Institute of Child Health, University College London, London, UnK; School of Population Health and Sansom Institute of Health Research, University of South Australia, Adelaide, SA, Australia; South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | | | - Rebecca Hardy
- MRC Unit for Lifelong Health and Ageing at UCL, London, UK
| | - Diana Kuh
- MRC Unit for Lifelong Health and Ageing at UCL, London, UK
| | - Nicholas Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Yoav Ben-Shlomo
- School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Ian N Day
- School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Peter Whincup
- Population Health Research Institute, St George's, University of London, London, UK
| | - Richard Morris
- Department of Primary Care & Population Health, University College London, Royal Free Campus, London, UK
| | | | - Jacqueline Price
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh, UK
| | - Meena Kumari
- Institute for Social and Economic Research, University of Essex, Colchester, UK
| | - Mika Kivimaki
- Department of Epidemiology & Public Health, UCL Institute of Epidemiology & Health Care, University College London, London, UK
| | | | - John C Whittaker
- Genetics Division, Research and Development, GlaxoSmithKline, Harlow, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Frank Dudbridge
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Juan P Casas
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK; Institute of Cardiovascular Science and Farr Institute, University College London, London, UK
| | - Michael V Holmes
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK; Department of Surgery and Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Aroon D Hingorani
- Institute of Cardiovascular Science and Farr Institute, University College London, London, UK
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Xu MK, Gaysina D, Barnett JH, Scoriels L, van de Lagemaat LN, Wong A, Richards M, Croudace TJ, Jones PB. Psychometric precision in phenotype definition is a useful step in molecular genetic investigation of psychiatric disorders. Transl Psychiatry 2015; 5:e593. [PMID: 26125156 PMCID: PMC4490295 DOI: 10.1038/tp.2015.86] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2014] [Revised: 05/08/2015] [Accepted: 05/21/2015] [Indexed: 12/20/2022] Open
Abstract
Affective disorders are highly heritable, but few genetic risk variants have been consistently replicated in molecular genetic association studies. The common method of defining psychiatric phenotypes in molecular genetic research is either a summation of symptom scores or binary threshold score representing the risk of diagnosis. Psychometric latent variable methods can improve the precision of psychiatric phenotypes, especially when the data structure is not straightforward. Using data from the British 1946 birth cohort, we compared summary scores with psychometric modeling based on the General Health Questionnaire (GHQ-28) scale for affective symptoms in an association analysis of 27 candidate genes (249 single-nucleotide polymorphisms (SNPs)). The psychometric method utilized a bi-factor model that partitioned the phenotype variances into five orthogonal latent variable factors, in accordance with the multidimensional data structure of the GHQ-28 involving somatic, social, anxiety and depression domains. Results showed that, compared with the summation approach, the affective symptoms defined by the bi-factor psychometric model had a higher number of associated SNPs of larger effect sizes. These results suggest that psychometrically defined mental health phenotypes can reflect the dimensions of complex phenotypes better than summation scores, and therefore offer a useful approach in genetic association investigations.
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Affiliation(s)
- M K Xu
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK,Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Worts Causeway, Cambridge CB1 8RN, UK. E-mail:
| | - D Gaysina
- Rudd Centre for Adoption Research and Practice, School of Psychology, University of Sussex, Brighton, UK
| | - J H Barnett
- Department of Psychiatry, University of Cambridge, Cambridge, UK,Cambridge Cognition, Cambridge, UK
| | - L Scoriels
- Department of Psychiatry, University of Cambridge, Cambridge, UK,Instituto de Psiquiatria, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | | | - A Wong
- MRC Unit for Lifelong Health and Ageing at University College London, London, UK
| | - M Richards
- MRC Unit for Lifelong Health and Ageing at University College London, London, UK
| | - T J Croudace
- School of Nursing and Midwifery, University of Dundee, Dundee, UK
| | - P B Jones
- Department of Psychiatry, University of Cambridge, Cambridge, UK
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Talmud PJ, Cooper JA, Morris RW, Dudbridge F, Shah T, Engmann J, Dale C, White J, McLachlan S, Zabaneh D, Wong A, Ong KK, Gaunt T, Holmes MV, Lawlor DA, Richards M, Hardy R, Kuh D, Wareham N, Langenberg C, Ben-Shlomo Y, Wannamethee SG, Strachan MWJ, Kumari M, Whittaker JC, Drenos F, Kivimaki M, Hingorani AD, Price JF, Humphries SE. Sixty-five common genetic variants and prediction of type 2 diabetes. Diabetes 2015; 64:1830-40. [PMID: 25475436 PMCID: PMC4407866 DOI: 10.2337/db14-1504] [Citation(s) in RCA: 76] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2014] [Accepted: 11/27/2014] [Indexed: 12/19/2022]
Abstract
We developed a 65 type 2 diabetes (T2D) variant-weighted gene score to examine the impact on T2D risk assessment in a U.K.-based consortium of prospective studies, with subjects initially free from T2D (N = 13,294; 37.3% women; mean age 58.5 [38-99] years). We compared the performance of the gene score with the phenotypically derived Framingham Offspring Study T2D risk model and then the two in combination. Over the median 10 years of follow-up, 804 participants developed T2D. The odds ratio for T2D (top vs. bottom quintiles of gene score) was 2.70 (95% CI 2.12-3.43). With a 10% false-positive rate, the genetic score alone detected 19.9% incident cases, the Framingham risk model 30.7%, and together 37.3%. The respective area under the receiver operator characteristic curves were 0.60 (95% CI 0.58-0.62), 0.75 (95% CI 0.73 to 0.77), and 0.76 (95% CI 0.75 to 0.78). The combined risk score net reclassification improvement (NRI) was 8.1% (5.0 to 11.2; P = 3.31 × 10(-7)). While BMI stratification into tertiles influenced the NRI (BMI ≤24.5 kg/m(2), 27.6% [95% CI 17.7-37.5], P = 4.82 × 10(-8); 24.5-27.5 kg/m(2), 11.6% [95% CI 5.8-17.4], P = 9.88 × 10(-5); >27.5 kg/m(2), 2.6% [95% CI -1.4 to 6.6], P = 0.20), age categories did not. The addition of the gene score to a phenotypic risk model leads to a potentially clinically important improvement in discrimination of incident T2D.
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Affiliation(s)
- Philippa J Talmud
- Centre for Cardiovascular Genetics, Institute of Cardiovascular Science, University College London, London, U.K.
| | - Jackie A Cooper
- Centre for Cardiovascular Genetics, Institute of Cardiovascular Science, University College London, London, U.K
| | - Richard W Morris
- Department of Primary Care and Population Health, University College London, Royal Free Campus, London, U.K
| | - Frank Dudbridge
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, U.K
| | - Tina Shah
- Department of Epidemiology and Public Health, University College London Institute of Epidemiology and Health Care, University College London, London, U.K
| | - Jorgen Engmann
- Department of Epidemiology and Public Health, University College London Institute of Epidemiology and Health Care, University College London, London, U.K
| | - Caroline Dale
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, U.K
| | - Jon White
- University College London Genetics Institute, Department of Genetics, Environment and Evolution, London, U.K
| | - Stela McLachlan
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh, U.K
| | - Delilah Zabaneh
- University College London Genetics Institute, Department of Genetics, Environment and Evolution, London, U.K
| | - Andrew Wong
- Medical Research Council Unit for Lifelong Health and Ageing at University College London, London, U.K
| | - Ken K Ong
- Medical Research Council Unit for Lifelong Health and Ageing at University College London, London, U.K. Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, U.K
| | - Tom Gaunt
- School of Social and Community Medicine, University of Bristol, Bristol, U.K. Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, U.K
| | - Michael V Holmes
- Department of Epidemiology and Public Health, University College London Institute of Epidemiology and Health Care, University College London, London, U.K. Division of Transplant Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Debbie A Lawlor
- School of Social and Community Medicine, University of Bristol, Bristol, U.K. Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, U.K
| | - Marcus Richards
- Medical Research Council Unit for Lifelong Health and Ageing at University College London, London, U.K
| | - Rebecca Hardy
- Medical Research Council Unit for Lifelong Health and Ageing at University College London, London, U.K
| | - Diana Kuh
- Medical Research Council Unit for Lifelong Health and Ageing at University College London, London, U.K
| | - Nicholas Wareham
- Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, U.K
| | - Claudia Langenberg
- Department of Epidemiology and Public Health, University College London Institute of Epidemiology and Health Care, University College London, London, U.K. Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, U.K
| | - Yoav Ben-Shlomo
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, U.K
| | - S Goya Wannamethee
- Department of Primary Care and Population Health, University College London, Royal Free Campus, London, U.K
| | | | - Meena Kumari
- Department of Epidemiology and Public Health, University College London Institute of Epidemiology and Health Care, University College London, London, U.K
| | - John C Whittaker
- Genetics Division, Research and Development, GlaxoSmithKline, Harlow, U.K
| | - Fotios Drenos
- Centre for Cardiovascular Genetics, Institute of Cardiovascular Science, University College London, London, U.K. Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, U.K
| | - Mika Kivimaki
- Department of Epidemiology and Public Health, University College London Institute of Epidemiology and Health Care, University College London, London, U.K
| | - Aroon D Hingorani
- Department of Epidemiology and Public Health, University College London Institute of Epidemiology and Health Care, University College London, London, U.K. Centre for Clinical Pharmacology, University College London, London, U.K
| | - Jacqueline F Price
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh, U.K
| | - Steve E Humphries
- Centre for Cardiovascular Genetics, Institute of Cardiovascular Science, University College London, London, U.K
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34
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Parihar A, Wood GC, Chu X, Jin Q, Argyropoulos G, Still CD, Shuldiner AR, Mitchell BD, Gerhard GS. Extension of GWAS results for lipid-related phenotypes to extreme obesity using electronic health record (EHR) data and the Metabochip. Front Genet 2014; 5:222. [PMID: 25147553 PMCID: PMC4123014 DOI: 10.3389/fgene.2014.00222] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2014] [Accepted: 06/26/2014] [Indexed: 12/18/2022] Open
Abstract
A variety of health-related data are commonly deposited into electronic health records (EHRs), including laboratory, diagnostic, and medication information. The digital nature of EHR data facilitates efficient extraction of these data for research studies, including genome-wide association studies (GWAS). Previous GWAS have identified numerous SNPs associated with variation in total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG). These findings have led to the development of specialized genotyping platforms that can be used for fine-mapping and replication in other populations. We have combined the efficiency of EHR data and the economic advantages of the Illumina Metabochip, a custom designed SNP chip targeted to traits related to coronary artery disease, myocardial infarction, and type 2 diabetes, to conduct an array-wide analysis of lipid traits in a population with extreme obesity. Our analyses identified associations with 12 of 21 previously identified lipid-associated SNPs with effect sizes similar to prior results. Association analysis using several approaches to account for lipid-lowering medication use resulted in fewer and less strongly associated SNPs. The availability of phenotype data from the EHR and the economic efficiency of the specialized Metabochip can be exploited to conduct multi-faceted genetic association analyses.
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Affiliation(s)
- Ankita Parihar
- Department of Medicine and Program for Personalized and Genomic Medicine, University of Maryland School of Medicine Baltimore, MD, USA
| | - G Craig Wood
- Geisinger Clinic, Geisinger Obesity Institute Danville, PA, USA
| | - Xin Chu
- Geisinger Clinic, Geisinger Obesity Institute Danville, PA, USA
| | - Qunjan Jin
- Department of Pathology and Laboratory Medicine, Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, Pennsylvania State University College of Medicine Hershey, PA, USA
| | | | | | - Alan R Shuldiner
- Department of Medicine and Program for Personalized and Genomic Medicine, University of Maryland School of Medicine Baltimore, MD, USA ; Geriatric Research and Education Clinical Center, Veterans Administration Medical Center Baltimore, MD, USA
| | - Braxton D Mitchell
- Department of Medicine and Program for Personalized and Genomic Medicine, University of Maryland School of Medicine Baltimore, MD, USA ; Geriatric Research and Education Clinical Center, Veterans Administration Medical Center Baltimore, MD, USA
| | - Glenn S Gerhard
- Department of Pathology and Laboratory Medicine, Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, Pennsylvania State University College of Medicine Hershey, PA, USA
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