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Roberts AL, Morea A, Amar A, West M, Karrar S, Lehane R, Tombleson P, Cunningham Grahman D, Reynolds JA, Wong CCY, Morris DL, Small KS, Vyse TJ. Haematopoietic stem cell-derived immune cells have reduced X chromosome inactivation skewing in systemic lupus erythematosus. Ann Rheum Dis 2024; 83:1315-1321. [PMID: 38937070 DOI: 10.1136/ard-2024-225585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 05/29/2024] [Indexed: 06/29/2024]
Abstract
OBJECTIVES Systemic lupus erythematosus (SLE) shows a marked female bias in prevalence. X chromosome inactivation (XCI) is the mechanism which randomly silences one X chromosome to equalise gene expression between 46, XX females and 46, XY males. Though XCI is expected to result in a random pattern of mosaicism across tissues, some females display a significantly skewed ratio in immune cells, termed XCI-skew. We tested whether XCI was abnormal in females with SLE and hence contributes to sexual dimorphism. METHODS We assayed XCI in whole blood DNA in 181 female SLE cases, 796 female healthy controls and 10 twin pairs discordant for SLE. Using regression modelling and intra-twin comparisons, we assessed the effect of SLE on XCI and combined clinical, cellular and genetic data via a polygenic score to explore underlying mechanisms. RESULTS Accommodating the powerful confounder of age, XCI-skew was reduced in females with SLE compared with controls (p=1.3×10-5), with the greatest effect seen in those with more severe disease. Applying an XCI threshold of >80%, we observed XCI-skew in 6.6% of SLE cases compared with 22% of controls. This difference was not explained by differential white cell counts, medication or genetic susceptibility to SLE. Instead, XCI-skew correlated with a biomarker for type I interferon-regulated gene expression. CONCLUSIONS These results refute current views on XCI-skew in autoimmunity and suggest, in lupus, XCI patterns of immune cells reflect the impact of disease state, specifically interferon signalling, on the haematopoietic stem cells from which they derive.
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Affiliation(s)
- Amy L Roberts
- Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Alessandro Morea
- Twin Research and Genetic Epidemiology, King's College London, London, UK
- Foundation Institute of Molecular Oncology, IFOM, Milano, Italy
| | - Ariella Amar
- Department of Medical and Molecular Genetics, King's College London, London, UK
| | - Magdalena West
- Department of Medical and Molecular Genetics, King's College London, London, UK
| | - Sarah Karrar
- Department of Medical and Molecular Genetics, King's College London, London, UK
| | - Rhiannon Lehane
- Department of Medical and Molecular Genetics, King's College London, London, UK
| | - Philip Tombleson
- Department of Medical and Molecular Genetics, King's College London, London, UK
| | | | - John A Reynolds
- Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK
| | - Chloe C Y Wong
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - David L Morris
- Medical and Molecular Genetics, King's College London, London, UK
| | - Kerrin S Small
- Twin Research and Genetic Epidemiology, King's College London, London, UK
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Shore CJ, Villicaña S, El-Sayed Moustafa JS, Roberts AL, Gunn DA, Bataille V, Deloukas P, Spector TD, Small KS, Bell JT. Genetic effects on the skin methylome in healthy older twins. Am J Hum Genet 2024; 111:1932-1952. [PMID: 39137780 PMCID: PMC11393713 DOI: 10.1016/j.ajhg.2024.07.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 05/22/2024] [Accepted: 07/15/2024] [Indexed: 08/15/2024] Open
Abstract
Whole-skin DNA methylation variation has been implicated in several diseases, including melanoma, but its genetic basis has not yet been fully characterized. Using bulk skin tissue samples from 414 healthy female UK twins, we performed twin-based heritability and methylation quantitative trait loci (meQTL) analyses for >400,000 DNA methylation sites. We find that the human skin DNA methylome is on average less heritable than previously estimated in blood and other tissues (mean heritability: 10.02%). meQTL analysis identified local genetic effects influencing DNA methylation at 18.8% (76,442) of tested CpG sites, as well as 1,775 CpG sites associated with at least one distal genetic variant. As a functional follow-up, we performed skin expression QTL (eQTL) analyses in a partially overlapping sample of 604 female twins. Colocalization analysis identified over 3,500 shared genetic effects affecting thousands of CpG sites (10,067) and genes (4,475). Mediation analysis of putative colocalized gene-CpG pairs identified 114 genes with evidence for eQTL effects being mediated by DNA methylation in skin, including in genes implicating skin disease such as ALOX12 and CSPG4. We further explored the relevance of skin meQTLs to skin disease and found that skin meQTLs and CpGs under genetic influence were enriched for multiple skin-related genome-wide and epigenome-wide association signals, including for melanoma and psoriasis. Our findings give insights into the regulatory landscape of epigenomic variation in skin.
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Affiliation(s)
- Christopher J Shore
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.
| | - Sergio Villicaña
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | | | - Amy L Roberts
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | | | - Veronique Bataille
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Panos Deloukas
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Kerrin S Small
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.
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3
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Rhee A, Granville Smith I, Compte R, Vehof J, Nessa A, Wadge S, Freidin MB, Bennett DL, Williams FMK. Quantitative sensory testing and chronic pain syndromes: a cross-sectional study from TwinsUK. BMJ Open 2024; 14:e085814. [PMID: 39231552 PMCID: PMC11407192 DOI: 10.1136/bmjopen-2024-085814] [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] [Indexed: 09/06/2024] Open
Abstract
OBJECTIVE The chronic pain syndromes (CPS) include syndromes such as chronic widespread pain (CWP), dry eye disease (DED) and irritable bowel syndrome (IBS). Highly prevalent and lacking pathognomonic biomarkers, the CPS are known to cluster in individuals in part due to their genetic overlap, but patient diagnosis can be difficult. The success of quantitative sensory testing (QST) and inflammatory biomarkers as phenotyping tools in conditions such as painful neuropathies warrant their investigation in CPS. We aimed to examine whether individual QST modalities and candidate inflammatory markers were associated with CWP, DED or IBS in a large, highly phenotyped population sample. DESIGN Cross-sectional study. SETTING Community-dwelling cohort. PARTICIPANTS Twins from the TwinsUK cohort PRIMARY AND SECONDARY OUTCOME MEASURES: We compared 10 QST modalities, measured in participants with and without a CWP diagnosis between 2007 and 2012. We investigated whether inflammatory markers measured by Olink were associated with CWP, including interleukin-6 (IL-6), IL-8, IL-10, monocyte chemoattractant protein-1 and tumour necrosis factor. All analyses were repeated in DED and IBS with correction for multiple testing. RESULTS In N=3022 twins (95.8% women), no association was identified between individual QST modalities and CPS diagnoses (CWP, DED and IBS). Analyses of candidate inflammatory marker levels and CPS diagnoses in n=1368 twins also failed to meet statistical significance. CONCLUSION Our findings in a large population cohort suggest a lack of true association between singular QST modalities or candidate inflammatory markers and CPS.
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Affiliation(s)
- Amber Rhee
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | | | - Roger Compte
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Jelle Vehof
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- Departments of Ophthalmology and Epidemiology, University of Groningen, Groningen, The Netherlands
- Department of Ophthalmology, Vestfold Hospital Trust, Tønsberg, Norway
| | - Ayrun Nessa
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Samuel Wadge
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Maxim B Freidin
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- Department of Biology, Queen Mary University of London, London, UK
| | - David L Bennett
- Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, UK
| | - Frances M K Williams
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
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4
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Visconti A, Rossi N, Bondt A, Ederveen AH, Thareja G, Koeleman CAM, Stephan N, Halama A, Lomax-Browne HJ, Pickering MC, Zhou XJ, Wuhrer M, Suhre K, Falchi M. The genetics and epidemiology of N- and O-immunoglobulin A glycomics. Genome Med 2024; 16:96. [PMID: 39123268 PMCID: PMC11312925 DOI: 10.1186/s13073-024-01369-6] [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: 01/13/2024] [Accepted: 07/26/2024] [Indexed: 08/12/2024] Open
Abstract
BACKGROUND Immunoglobulin (Ig) glycosylation modulates the immune response and plays a critical role in ageing and diseases. Studies have mainly focused on IgG glycosylation, and little is known about the genetics and epidemiology of IgA glycosylation. METHODS We generated, using a novel liquid chromatography-mass spectrometry method, the first large-scale IgA glycomics dataset in serum from 2423 twins, encompassing 71 N- and O-glycan species. RESULTS We showed that, despite the lack of a direct genetic template, glycosylation is highly heritable, and that glycopeptide structures are sex-specific, and undergo substantial changes with ageing. We observe extensive correlations between the IgA and IgG glycomes, and, exploiting the twin design, show that they are predominantly influenced by shared genetic factors. A genome-wide association study identified eight loci associated with both the IgA and IgG glycomes (ST6GAL1, ELL2, B4GALT1, ABCF2, TMEM121, SLC38A10, SMARCB1, and MGAT3) and two novel loci specifically modulating IgA O-glycosylation (C1GALT1 and ST3GAL1). Validation of our findings in an independent cohort of 320 individuals from Qatar showed that the underlying genetic architecture is conserved across ancestries. CONCLUSIONS Our study delineates the genetic landscape of IgA glycosylation and provides novel potential functional links with the aetiology of complex immune diseases, including genetic factors involved in IgA nephropathy risk.
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Affiliation(s)
- Alessia Visconti
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- Center for Biostatistics, Epidemiology and Public Health, Department of Clinical and Biological Sciences, University of Turin, Turin, Italy
| | - Niccolò Rossi
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Albert Bondt
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands
| | - Agnes Hipgrave Ederveen
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands
| | - Gaurav Thareja
- Department of Biophysics and Physiology, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Carolien A M Koeleman
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands
| | - Nisha Stephan
- Department of Biophysics and Physiology, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Anna Halama
- Department of Biophysics and Physiology, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Hannah J Lomax-Browne
- Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, UK
| | - Matthew C Pickering
- Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, UK
| | - Xu-Jie Zhou
- Renal Division, Peking University First Hospital, Beijing, China
- Peking University Institute of Nephrology, Beijing, China
- Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Ministry of Education, Peking University, Beijing, China
| | - Manfred Wuhrer
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands
| | - Karsten Suhre
- Department of Biophysics and Physiology, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Mario Falchi
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.
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5
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Valdes AM, Louca P, Visconti A, Asnicar F, Bermingham K, Nogal A, Wong K, Michelotti GA, Wolf J, Segata N, Spector TD, Berry SE, Falchi M, Menni C. Vitamin A carotenoids, but not retinoids, mediate the impact of a healthy diet on gut microbial diversity. BMC Med 2024; 22:321. [PMID: 39113058 PMCID: PMC11304618 DOI: 10.1186/s12916-024-03543-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Accepted: 07/28/2024] [Indexed: 08/11/2024] Open
Abstract
BACKGROUND Vitamin A is essential for physiological processes like vision and immunity. Vitamin A's effect on gut microbiome composition, which affects absorption and metabolism of other vitamins, is still unknown. Here we examined the relationship between gut metagenome composition and six vitamin A-related metabolites (two retinoid: -retinol, 4 oxoretinoic acid (oxoRA) and four carotenoid metabolites, including beta-cryptoxanthin and three carotene diols). METHODS We included 1053 individuals from the TwinsUK cohort with vitamin A-related metabolites measured in serum and faeces, diet history, and gut microbiome composition assessed by shotgun metagenome sequencing. Results were replicated in 327 women from the ZOE PREDICT-1 study. RESULTS Five vitamin A-related serum metabolites were positively correlated with microbiome alpha diversity (r = 0.15 to r = 0.20, p < 4 × 10-6). Carotenoid compounds were positively correlated with the short-chain fatty-acid-producing bacteria Faecalibacterium prausnitzii and Coprococcus eutactus. Retinol was not associated with any microbial species. We found that gut microbiome composition could predict circulating levels of carotenoids and oxoretinoic acid with AUCs ranging from 0.66 to 0.74 using random forest models, but not retinol (AUC = 0.52). The healthy eating index (HEI) was strongly associated with gut microbiome diversity and with all carotenoid compounds, but not retinoids. We investigated the mediating role of carotenoid compounds on the effect of a healthy diet (HEI) on gut microbiome diversity, finding that carotenoids significantly mediated between 18 and 25% of the effect of HEI on gut microbiome alpha diversity. CONCLUSIONS Our results show strong links between circulating carotene compounds and gut microbiome composition and potential links to a healthy diet pattern.
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Affiliation(s)
- Ana M Valdes
- Nottingham NIHR Biomedical Research Centre at the School of Medicine, University of Nottingham, Nottingham, NG5 1PB, UK.
- Inflammation, Recovery and Injury Sciences, School of Medicine, University of Nottingham, Nottingham, NG7 2UH, UK.
- Department of Twin Research & Genetic Epidemiology, King's College London, London, SE1 7EH, UK.
| | - Panayiotis Louca
- Department of Twin Research & Genetic Epidemiology, King's College London, London, SE1 7EH, UK
- Human Nutrition and Exercise Research Centre, University of Newcastle, Newcastle Upon Tyne, NE2 4HH, UK
| | - Alessia Visconti
- Department of Twin Research & Genetic Epidemiology, King's College London, London, SE1 7EH, UK
- Centre for Biostatistics, Epidemiology, and Public Health, Department of Clinical and Biological Sciences, University of Turin, 10124, Turin, Italy
| | - Francesco Asnicar
- Department CIBIO, University of Trento, Via Sommarive 9, 38123, Povo, Trento, Italy
| | - Kate Bermingham
- Department of Nutritional Sciences, King's College London, 150 Stamford Street, London, SE1 9NH, UK
- Zoe Limited, 164 Westminster Bridge Rd, London, SE1 7RW, UK
| | - Ana Nogal
- Department of Twin Research & Genetic Epidemiology, King's College London, London, SE1 7EH, UK
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Kari Wong
- Metabolon Inc, Research Triangle Park, Morrisville, NC, 27560, USA
| | | | - Jonathan Wolf
- Zoe Limited, 164 Westminster Bridge Rd, London, SE1 7RW, UK
| | - Nicola Segata
- Department CIBIO, University of Trento, Via Sommarive 9, 38123, Povo, Trento, Italy
| | - Tim D Spector
- Department of Twin Research & Genetic Epidemiology, King's College London, London, SE1 7EH, UK
- Zoe Limited, 164 Westminster Bridge Rd, London, SE1 7RW, UK
| | - Sarah E Berry
- Department of Nutritional Sciences, King's College London, 150 Stamford Street, London, SE1 9NH, UK
- Zoe Limited, 164 Westminster Bridge Rd, London, SE1 7RW, UK
| | - Mario Falchi
- Department of Twin Research & Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Cristina Menni
- Department of Twin Research & Genetic Epidemiology, King's College London, London, SE1 7EH, UK.
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Demiral ŞB, Volkow ND. Blink-induced changes in pupil dynamics are consistent and heritable. RESEARCH SQUARE 2024:rs.3.rs-4718613. [PMID: 39149500 PMCID: PMC11326410 DOI: 10.21203/rs.3.rs-4718613/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Pupil size and blink rates are heritable but the extent to which they interact with one another has not been properly investigated. Though changes in pupil size due to eye blinks have been reported, they are considered a pupillary artifact. In this study we used the HCP 7T fMRI dataset with resting state eye-tracking data obtained in monozygous and dizygous twins to assess their heritability and their interactions. For this purpose, we characterized the pupil dilation (positive peak) and constriction (negative peak) that followed blink events, which we describe as blink-induced pupillary response (BIPR). We show that the BIPR is highly consistent with a positive dilatory peak (D-peak) around 500ms and a negative constricting peak (C-peak) around 1s. These patterns were reproducible within- and between- subjects across two time points and differed by vigilance state (vigilant versus drowsy). By comparing BIPR between monozygous and dizygous twins we show that BIPR have a heritable component with significant additive genetic (A) and environmental (E) factors dominating the structural equation models, particularly in the time-domain for both D- and C-peaks and amplitude domain for the C-peak. (a2 between 42-49%). Blink duration, pupil size and blink rate were also found to be highly heritable (a2 up to 62% for pupil size). Our study documents an association between BIPR and wakefulness and indicates that BIPR should not be treated as a coincidental artefact, but part of a larger oculomotor system that we label here as Oculomotor Adaptive System, OAS, that is genetically determined.
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7
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Rossi N, Syed N, Visconti A, Aliyev E, Berry S, Bourbon M, Spector TD, Hysi PG, Fakhro KA, Falchi M. Rare variants at KCNJ2 are associated with LDL-cholesterol levels in a cross-population study. NPJ Genom Med 2024; 9:36. [PMID: 38942744 PMCID: PMC11213907 DOI: 10.1038/s41525-024-00417-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 05/03/2024] [Indexed: 06/30/2024] Open
Abstract
Leveraging whole genome sequencing data of 1751 individuals from the UK and 2587 Qatari subjects, we suggest here an association of rare variants mapping to the sour taste-associated gene KCNJ2 with reduced low-density lipoprotein cholesterol (LDL-C, P = 2.10 × 10-12) and with a 22% decreased dietary trans-fat intake. This study identifies a novel candidate rare locus for LDL-C, adding insights into the genetic architecture of a complex trait implicated in cardiovascular disease.
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Affiliation(s)
- Niccolò Rossi
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Najeeb Syed
- Department of Human Genetics, Sidra Medical and Research Center, Doha, Qatar
| | - Alessia Visconti
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
- Center for Biostatistics, Epidemiology and Public Health, Department of Clinical and Biological Sciences, University of Turin, Turin, Italy
| | - Elbay Aliyev
- Department of Human Genetics, Sidra Medical and Research Center, Doha, Qatar
| | - Sarah Berry
- Department of Nutritional Sciences, King's College London, London, UK
| | - Mafalda Bourbon
- Cardiovascular Research Group, Department of Health Promotion and Prevention of non-Communicable Diseases, Instituto Nacional de Saúde Dr. Ricardo Jorge, Lisbon, Portugal
| | - Tim D Spector
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Pirro G Hysi
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Khalid A Fakhro
- Department of Human Genetics, Sidra Medical and Research Center, Doha, Qatar
- Department of Genetic Medicine, Weill-Cornell Medical College, Doha, Qatar
| | - Mario Falchi
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK.
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Menaker Y, van den Munckhof I, Scarpa A, Placek K, Brandes-Leibovitz R, Glantzspiegel Y, Joosten LAB, Rutten JHW, Netea MG, Gat-Viks I, Riksen NP. Stratification of Atherosclerosis based on Plasma Metabolic States. J Clin Endocrinol Metab 2024; 109:1250-1262. [PMID: 38044551 DOI: 10.1210/clinem/dgad672] [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: 06/29/2023] [Indexed: 12/05/2023]
Abstract
CONTEXT Atherosclerosis is a dominant cause of cardiovascular disease (CVD), including myocardial infarction and stroke. OBJECTIVE To investigate metabolic states that are associated with the development of atherosclerosis. METHODS Cross-sectional cohort study at a university hospital in the Netherlands. A total of 302 adult subjects with a body mass index (BMI) ≥ 27 kg/m2 were included. We integrated plasma metabolomics with clinical metadata to quantify the "atherogenic state" of each individual, providing a continuous spectrum of atherogenic states that ranges between nonatherogenic states to highly atherogenic states. RESULTS Analysis of groups of individuals with different clinical conditions-such as metabolically healthy individuals with obesity, and individuals with metabolic syndrome-confirmed the generalizability of this spectrum; revealed a wide variation of atherogenic states within each condition; and allowed identification of metabolites that are associated with the atherogenic state regardless of the particular condition, such as gamma-glutamyl-glutamic acid and homovanillic acid sulfate. The analysis further highlighted metabolic pathways such as catabolism of phenylalanine and tyrosine and biosynthesis of estrogens and phenylpropanoids. Using validation cohorts, we confirmed variation in atherogenic states in healthy subjects (before atherosclerosis plaques become visible), and showed that metabolites associated with the atherogenic state were also associated with future CVD. CONCLUSION Our results provide a global view of atherosclerosis risk states using plasma metabolomics.
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Affiliation(s)
- Yuval Menaker
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Inge van den Munckhof
- Department of Internal Medicine, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Alice Scarpa
- Department of Immunology and Metabolism, Life and Medical Sciences Institute, University of Bonn, 53115 Bonn, Germany
| | - Katarzyna Placek
- Department of Immunology and Metabolism, Life and Medical Sciences Institute, University of Bonn, 53115 Bonn, Germany
| | - Rachel Brandes-Leibovitz
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Yossef Glantzspiegel
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Leo A B Joosten
- Department of Internal Medicine, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
- Department of Medical Genetics, Iuliu Hatieganu University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania
| | - Joost H W Rutten
- Department of Internal Medicine, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Mihai G Netea
- Department of Internal Medicine, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
- Department of Immunology and Metabolism, Life and Medical Sciences Institute, University of Bonn, 53115 Bonn, Germany
| | - Irit Gat-Viks
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Niels P Riksen
- Department of Internal Medicine, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
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9
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Abtahi H, Khoshnam-Rad N, Gholamzadeh M, Daraie M, Sabouri F. Conceptual framework for establishing twins prevention and continuous health promotion programme: a qualitative study. BMJ Open 2024; 14:e080443. [PMID: 38604635 PMCID: PMC11015185 DOI: 10.1136/bmjopen-2023-080443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 04/02/2024] [Indexed: 04/13/2024] Open
Abstract
BACKGROUND Twin registries and cohorts face numerous challenges, including significant resource allocation, twins' recruitment and retention. This study aimed to assess expert feedback on a proposed pragmatic idea for launching a continuous health promotion and prevention programme (HPPP) to establish and maintain twin cohorts. DESIGN A qualitative study incorporating an inductive thematic analysis. SETTING Tehran University of Medical Sciences. PARTICIPANTS Researchers with expertise in twin studies participated in our study. ANALYSIS AND DESIGN Expert opinions were gathered through focus group discussions (FGDs). Thematic analysis was employed to analyse the findings and develop a model for designing a comprehensive, long-term health promotion programme using ATLAS.ti software. Additionally, a standardised framework was developed to represent the conceptual model of the twin HPPP. RESULTS Eight FGDs were conducted, involving 16 experts. Thematic analysis identified eight themes and seven subthemes that encompassed the critical aspects of a continuous monitoring programme for twin health. Based on these identified themes, a conceptual framework was developed for the implementation of an HPPP tailored for twins. CONCLUSION This study presented the initial endeavour to establish a comprehensive and practical solution in the form of a continuous HPPP designed to tackle the obstacles of twins' cohorts.
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Affiliation(s)
- Hamidreza Abtahi
- Pulmonary and Critical Care Medicine Department, Tehran University of Medical Sciences, Tehran, Iran (the Islamic Republic of)
- Thoracic Research Center, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Tehran, Iran (the Islamic Republic of)
| | - Niloofar Khoshnam-Rad
- Thoracic Research Center, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Tehran, Iran (the Islamic Republic of)
| | - Marsa Gholamzadeh
- Health Information Management and Medical Informatics Department, Tehran University of Medical Sciences, Tehran, Iran (the Islamic Republic of)
| | - Morteza Daraie
- Department of Internal Medicine, Tehran University of Medical Sciences, Tehran, Iran (the Islamic Republic of)
| | - Fatemeh Sabouri
- Thoracic Research Center, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Tehran, Iran (the Islamic Republic of)
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10
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Attaye I, Beynon-Cobb B, Louca P, Nogal A, Visconti A, Tettamanzi F, Wong K, Michellotti G, Spector TD, Falchi M, Bell JT, Menni C. Cross-sectional analyses of metabolites across biological samples mediating dietary acid load and chronic kidney disease. iScience 2024; 27:109132. [PMID: 38433906 PMCID: PMC10907771 DOI: 10.1016/j.isci.2024.109132] [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: 10/12/2023] [Revised: 12/14/2023] [Accepted: 02/01/2024] [Indexed: 03/05/2024] Open
Abstract
Chronic kidney disease (CKD) is a major public health burden, with dietary acid load (DAL) and gut microbiota playing crucial roles. As DAL can affect the host metabolome, potentially via the gut microbiota, we cross-sectionally investigated the interplay between DAL, host metabolome, gut microbiota, and early-stage CKD (TwinsUK, n = 1,453). DAL was positively associated with CKD stage G1-G2 (Beta (95% confidence interval) = 0.34 (0.007; 0.7), p = 0.046). After adjusting for covariates and multiple testing, we identified 15 serum, 14 urine, 8 stool, and 7 saliva metabolites, primarily lipids and amino acids, associated with both DAL and CKD progression. Of these, 8 serum, 2 urine, and one stool metabolites were found to mediate the DAL-CKD association. Furthermore, the stool metabolite 5-methylhexanoate (i7:0) correlated with 26 gut microbial species. Our findings emphasize the gut microbiota's therapeutic potential in countering DAL's impact on CKD through the host metabolome. Interventional and longitudinal studies are needed to establish causality.
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Affiliation(s)
- Ilias Attaye
- Department of Twin Research, King’s College London, St Thomas' Hospital Campus, London SE1 7EH, UK
- Amsterdam Cardiovascular Sciences, Diabetes & Metabolism, Amsterdam, the Netherlands
| | - Beverley Beynon-Cobb
- Department of Twin Research, King’s College London, St Thomas' Hospital Campus, London SE1 7EH, UK
- Department of Nutrition & Dietetics, University Hospitals Coventry & Warwickshire NHS Trust, Coventry CV2 2DX, UK
| | - Panayiotis Louca
- Department of Twin Research, King’s College London, St Thomas' Hospital Campus, London SE1 7EH, UK
| | - Ana Nogal
- Department of Twin Research, King’s College London, St Thomas' Hospital Campus, London SE1 7EH, UK
| | - Alessia Visconti
- Department of Twin Research, King’s College London, St Thomas' Hospital Campus, London SE1 7EH, UK
| | - Francesca Tettamanzi
- Department of Twin Research, King’s College London, St Thomas' Hospital Campus, London SE1 7EH, UK
| | - Kari Wong
- Metabolon, Research Triangle Park, Morrisville, NC 27560, USA
| | | | - Tim D. Spector
- Department of Twin Research, King’s College London, St Thomas' Hospital Campus, London SE1 7EH, UK
| | - Mario Falchi
- Department of Twin Research, King’s College London, St Thomas' Hospital Campus, London SE1 7EH, UK
| | - Jordana T. Bell
- Department of Twin Research, King’s College London, St Thomas' Hospital Campus, London SE1 7EH, UK
| | - Cristina Menni
- Department of Twin Research, King’s College London, St Thomas' Hospital Campus, London SE1 7EH, UK
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11
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Compte R, Freidin MB, Granville Smith I, Le Maitre CL, Vaitkute D, Nessa A, Lachance G, Williams FMK. No evidence of association between either Modic change or disc degeneration and five circulating inflammatory proteins. JOR Spine 2024; 7:e1323. [PMID: 38529326 PMCID: PMC10961713 DOI: 10.1002/jsp2.1323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 01/31/2024] [Accepted: 03/02/2024] [Indexed: 03/27/2024] Open
Abstract
Introduction Intervertebral disc degeneration and Modic change are the main spinal structural changes associated with chronic low back pain (LBP). Both conditions are thought to manifest local inflammation and if inflammatory proteins translocate to the blood circulation could be detected systemically. The work here assesses whether the presence of disc degeneration is associated with detectable blood level changes of five inflammatory markers and whether chronic LBP is associated with these changes. Materials and Methods Two hundred and forty TwinsUK cohort participants with both MRI disc degeneration grade and Modic change extent, and IL-6, IL-8, IL-8 TNF, and CX3CL1 protein blood concentration measurements were included in this work. Linear mixed effects models were used to test the association of blood cytokine concentration with disc degeneration score and Modic change volumetric score. Association of chronic LBP status from questionnaires with disc degeneration, Modic change, and cytokine blood concentration was also tested. Results No statistically significant association between disc degeneration or Modic change with cytokine blood concentration was found. Instead, regression analysis pointed strong association between cytokine blood concentration with body mass index for IL-6 and with age for IL-6 and TNF. Mild association was found between IL-8 blood concentration and body mass index. Additionally, LBP status was associated with Modic change volumetric score but not associated with any cytokine concentration. Conclusions We found no evidence that Modic change and disc degeneration are able to produce changes in tested blood cytokine concentration. However, age and body mass index have strong influence on cytokine concentration and both are associated with the conditions studied which may confound associations found in the literature. It is then unlikely that cytokines produced in the disc or vertebral bone marrow induce chronic LBP.
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Affiliation(s)
- Roger Compte
- Department of Twin Research and Genetic EpidemiologyKing's College LondonLondonUK
| | - Maxim B. Freidin
- Department of Biology, School of Biological and Behavioural SciencesQueen Mary University of LondonLondonUK
| | | | - Christine L. Le Maitre
- Division of Clinical Medicine, School of Medicine and Population HealthUniversity of SheffieldSheffieldUK
| | - Dovile Vaitkute
- Department of Twin Research and Genetic EpidemiologyKing's College LondonLondonUK
| | - Ayrun Nessa
- Department of Twin Research and Genetic EpidemiologyKing's College LondonLondonUK
| | - Genevieve Lachance
- Department of Twin Research and Genetic EpidemiologyKing's College LondonLondonUK
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12
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Ni Lochlainn M, Bowyer RCE, Moll JM, García MP, Wadge S, Baleanu AF, Nessa A, Sheedy A, Akdag G, Hart D, Raffaele G, Seed PT, Murphy C, Harridge SDR, Welch AA, Greig C, Whelan K, Steves CJ. Effect of gut microbiome modulation on muscle function and cognition: the PROMOTe randomised controlled trial. Nat Commun 2024; 15:1859. [PMID: 38424099 PMCID: PMC10904794 DOI: 10.1038/s41467-024-46116-y] [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: 08/04/2023] [Accepted: 02/12/2024] [Indexed: 03/02/2024] Open
Abstract
Studies suggest that inducing gut microbiota changes may alter both muscle physiology and cognitive behaviour. Gut microbiota may play a role in both anabolic resistance of older muscle, and cognition. In this placebo controlled double blinded randomised controlled trial of 36 twin pairs (72 individuals), aged ≥60, each twin pair are block randomised to receive either placebo or prebiotic daily for 12 weeks. Resistance exercise and branched chain amino acid (BCAA) supplementation is prescribed to all participants. Outcomes are physical function and cognition. The trial is carried out remotely using video visits, online questionnaires and cognitive testing, and posting of equipment and biological samples. The prebiotic supplement is well tolerated and results in a changed gut microbiome [e.g., increased relative Bifidobacterium abundance]. There is no significant difference between prebiotic and placebo for the primary outcome of chair rise time (β = 0.579; 95% CI -1.080-2.239 p = 0.494). The prebiotic improves cognition (factor score versus placebo (β = -0.482; 95% CI,-0.813, -0.141; p = 0.014)). Our results demonstrate that cheap and readily available gut microbiome interventions may improve cognition in our ageing population. We illustrate the feasibility of remotely delivered trials for older people, which could reduce under-representation of older people in clinical trials. ClinicalTrials.gov registration: NCT04309292.
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Affiliation(s)
- Mary Ni Lochlainn
- King's College London, Department of Twin Research and Genetic Epidemiology, London, SE1 7EH, UK.
| | - Ruth C E Bowyer
- King's College London, Department of Twin Research and Genetic Epidemiology, London, SE1 7EH, UK
- The Alan Turing Institute, London, NW1 2DB, UK
| | | | - María Paz García
- King's College London, Department of Twin Research and Genetic Epidemiology, London, SE1 7EH, UK
| | - Samuel Wadge
- King's College London, Department of Twin Research and Genetic Epidemiology, London, SE1 7EH, UK
| | - Andrei-Florin Baleanu
- King's College London, Department of Twin Research and Genetic Epidemiology, London, SE1 7EH, UK
| | - Ayrun Nessa
- King's College London, Department of Twin Research and Genetic Epidemiology, London, SE1 7EH, UK
| | - Alyce Sheedy
- King's College London, Department of Twin Research and Genetic Epidemiology, London, SE1 7EH, UK
| | - Gulsah Akdag
- King's College London, Department of Twin Research and Genetic Epidemiology, London, SE1 7EH, UK
| | - Deborah Hart
- King's College London, Department of Twin Research and Genetic Epidemiology, London, SE1 7EH, UK
| | - Giulia Raffaele
- GKT School of Medical Education, King's College London, London, UK
| | - Paul T Seed
- Unit for Medical Statistics/Department for Women and Children's Health, School of Life Course and Population Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Caroline Murphy
- King's Clinical Trials Unit, Research Management and Innovation Directorate, King's College London, London, UK
| | - Stephen D R Harridge
- Centre for Human & Applied Physiological Sciences, King's College London, London, UK
| | - Ailsa A Welch
- Department of Epidemiology and Public Health, Norwich Medical School, University of East Anglia, Norwich, NR4 7TJ, UK
| | - Carolyn Greig
- School of Sport, Exercise, and Rehabilitation Sciences, University of Birmingham, Birmingham, UK
- MRC-Versus Arthritis Centre for Musculoskeletal Ageing Research, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
| | - Kevin Whelan
- King's College London, Department of Nutritional Sciences, Franklin Wilkins Building, SE1 9NH, London, UK
| | - Claire J Steves
- King's College London, Department of Twin Research and Genetic Epidemiology, London, SE1 7EH, UK.
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13
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Jarrar ZA, Al-Nosairy KO, Jiang X, Lamin A, Wong D, Ansari AS, Williams KM, Sivaprasad S, Hoffmann MB, Hysi PG, Hammond CJ, Mahroo OA. Temporal-to-Nasal Macular Ganglion Cell and Inner Plexiform Layer Ratios in a Large Adult Twin Cohort: Correlations With Age and Heritability. Invest Ophthalmol Vis Sci 2024; 65:26. [PMID: 38349786 PMCID: PMC10868632 DOI: 10.1167/iovs.65.2.26] [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: 08/01/2023] [Accepted: 01/31/2024] [Indexed: 02/15/2024] Open
Abstract
Purpose Temporal-to-nasal macular ganglion cell layer thickness ratios are reduced in albinism. We explored similar ratios in a large twin cohort to investigate ranges in healthy adults, correlations with age, and heritability. Methods More than 1000 twin pairs from TwinsUK underwent macular optical coherence tomography (OCT) scans. Automated segmentation yielded thicknesses for the combined ganglion cell and inner plexiform layer (GCIPL) in Early Treatment of Diabetic Retinopathy Study subfields. Participants with diseases likely to affect these layers or segmentation accuracy were excluded. Inner and outer ratios were defined as the ratio of temporal-to-nasal GCIPL thickness for inner and outer subfields respectively. Corresponding ratios were obtained from a smaller cohort undergoing OCTs with a different device (three-dimensional (3D)-OCT, Topcon, Japan). Results Scans from 2300 twins (1150 pairs) were included (mean [SD] age, 53.9 (16.5) years). Mean (SD) inner and outer ratios were 0.89 (0.09) and 0.84 (0.11), correlating negatively with age (coefficients, -0.17 and -0.21, respectively). In males (150 pairs) ratios were higher and did not correlate significantly with age. Intrapair correlation coefficients were higher in monozygotic than dizygotic pairs; age-adjusted heritability estimates were 0.20 and 0.23 for inner and outer ratios, respectively. For the second cohort (n = 166), mean (SD) ratios were 0.93 (0.08) and 0.91 (0.09), significantly greater than for the larger cohort. Conclusions Our study gives reference values for temporal-to-nasal macular GCIPL subfield ratios. Weak negative correlations with age emerged. Genetic factors may contribute to ∼20% to 23% of the variance in healthy individuals. The ratios differ according to the OCT platform used.
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Affiliation(s)
- Zakariya A. Jarrar
- Section of Ophthalmology, King's College London, St. Thomas’ Hospital Campus, London, United Kingdom
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
| | - Khaldoon O. Al-Nosairy
- Department of Ophthalmology, Faculty of Medicine, Otto-von-Guericke University, Magdeburg, Germany
| | - Xiaofan Jiang
- Section of Ophthalmology, King's College London, St. Thomas’ Hospital Campus, London, United Kingdom
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
- NIHR Biomedical Research Centre at Moorfields Eye Hospital and the UCL Institute of Ophthalmology, London, United Kingdom
- Institute of Ophthalmology, University College London, London, United Kingdom
| | - Ali Lamin
- NIHR Biomedical Research Centre at Moorfields Eye Hospital and the UCL Institute of Ophthalmology, London, United Kingdom
| | - Dominic Wong
- Section of Ophthalmology, King's College London, St. Thomas’ Hospital Campus, London, United Kingdom
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
| | - Abdus S. Ansari
- Section of Ophthalmology, King's College London, St. Thomas’ Hospital Campus, London, United Kingdom
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
| | - Katie M. Williams
- Section of Ophthalmology, King's College London, St. Thomas’ Hospital Campus, London, United Kingdom
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
- NIHR Biomedical Research Centre at Moorfields Eye Hospital and the UCL Institute of Ophthalmology, London, United Kingdom
- Institute of Ophthalmology, University College London, London, United Kingdom
| | - Sobha Sivaprasad
- NIHR Biomedical Research Centre at Moorfields Eye Hospital and the UCL Institute of Ophthalmology, London, United Kingdom
- Institute of Ophthalmology, University College London, London, United Kingdom
| | - Michael B. Hoffmann
- Department of Ophthalmology, Faculty of Medicine, Otto-von-Guericke University, Magdeburg, Germany
- Center for Behavioral Brain Sciences, Magdeburg, Sachsen-Anhalt, Germany
| | - Pirro G. Hysi
- Section of Ophthalmology, King's College London, St. Thomas’ Hospital Campus, London, United Kingdom
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
| | - Christopher J. Hammond
- Section of Ophthalmology, King's College London, St. Thomas’ Hospital Campus, London, United Kingdom
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
| | - Omar A. Mahroo
- Section of Ophthalmology, King's College London, St. Thomas’ Hospital Campus, London, United Kingdom
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
- NIHR Biomedical Research Centre at Moorfields Eye Hospital and the UCL Institute of Ophthalmology, London, United Kingdom
- Institute of Ophthalmology, University College London, London, United Kingdom
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14
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Thompson EJ, Krebs G, Zavos HMS, Steves CJ, Eley TC. The relationship between weight-related indicators and depressive symptoms during adolescence and adulthood: results from two twin studies. Psychol Med 2024; 54:527-538. [PMID: 37650294 DOI: 10.1017/s0033291723002155] [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] [Indexed: 09/01/2023]
Abstract
BACKGROUND The association between weight and depressive symptoms is well established, but the direction of effects remains unclear. Most studies rely on body mass index (BMI) as the sole weight indicator, with few examining the aetiology of the association between weight indicators and depressive symptoms. METHODS We analysed data from the Twins Early Development Study (TEDS) and UK Adult Twin Registry (TwinsUK) (7658 and 2775 twin pairs, respectively). A phenotypic cross-lagged panel model assessed the directionality between BMI and depressive symptoms at ages 12, 16, and 21 years in TEDS. Bivariate correlations tested the phenotypic association between a range of weight indicators and depressive symptoms in TwinsUK. In both samples, structural equation modelling of twin data investigated genetic and environmental influences between weight indicators and depression. Sensitivity analyses included two-wave phenotypic cross-lagged panel models and the exclusion of those with a BMI <18.5. RESULTS Within TEDS, the relationship between BMI and depression was bidirectional between ages 12 and 16 with a stronger influence of earlier BMI on later depression. The associations were unidirectional thereafter with depression at 16 influencing BMI at 21. Small genetic correlations were found between BMI and depression at ages 16 and 21, but not at 12. Within TwinsUK, depression was weakly correlated with weight indicators; therefore, it was not possible to generate precise estimates of genetic or environmental correlations. CONCLUSIONS The directionality of the relationship between BMI and depression appears to be developmentally sensitive. Further research with larger genetically informative samples is needed to estimate the aetiological influence on these associations.
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Affiliation(s)
- Ellen J Thompson
- MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Department of Twin Research and Genetic Epidemiology, School of Life Course & Population Sciences, Faculty of Life Sciences & Medicine, King's College London, London, UK
| | - Georgina Krebs
- MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Department of Clinical, Educational and Health Psychology, University College London, 1-19 Torrington Place, London, UK
| | - Helena M S Zavos
- Department of Psychology, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Claire J Steves
- Department of Twin Research and Genetic Epidemiology, School of Life Course & Population Sciences, Faculty of Life Sciences & Medicine, King's College London, London, UK
| | - Thalia C Eley
- MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
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15
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Jiang X, Bhatti T, Tariq A, Leo SM, Aychoua N, Webster AR, Hysi PG, Hammond CJ, Mahroo OA. Cone-driven strong flash electroretinograms in healthy adults: Prevalence of negative waveforms. Doc Ophthalmol 2024; 148:25-36. [PMID: 37924416 PMCID: PMC10879345 DOI: 10.1007/s10633-023-09957-4] [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: 12/05/2022] [Accepted: 10/05/2023] [Indexed: 11/06/2023]
Abstract
PURPOSE Both rod and cone-driven signals contribute to the electroretinogram (ERG) elicited by a standard strong flash in the dark. Negative ERGs usually reflect inner retinal dysfunction. However, in diseases where rod photoreceptor function is selectively lost, a negative waveform might represent the response of the dark-adapted cone system. To investigate the dark-adapted cone-driven waveform in healthy individuals, we delivered flashes on a dim blue background, designed to saturate the rods, but minimally adapt the cones. METHODS ERGs were recorded, using conductive fibre electrodes, in adults from the TwinsUK cohort. Responses to 13 cd m-2 s white xenon flashes (similar to the standard DA 10 flash), delivered on a blue background, were analysed. Photopic and scotopic strengths of the background were 1.3 and 30 cd m-2, respectively; through a dilated pupil, this is expected to largely saturate the rods, but adapt the cones much less than the standard ISCEV background. RESULTS Mean (SD) participant age was 62.5 (11.3) years (93% female). ERGs from 203 right and 204 left eyes were included, with mean (SD) b/a ratios of 1.22 (0.28) and 1.18 (0.28), respectively (medians, 1.19 and 1.17). Proportions with negative waveforms were 23 and 26%, respectively. Right and left eye b/a ratios were strongly correlated (correlation coefficient 0.74, p < 0.0001). We found no significant correlation of b/a ratio with age. CONCLUSIONS Over 20% of eyes showed b/a ratios less than 1, consistent with the notion that dark-adapted cone-driven responses to standard bright flashes can have negative waveforms. The majority had ratios greater than 1. Thus, whilst selective loss of rod function can yield a negative waveform (with reduced a-wave) in some, our findings also suggest that loss of rod function can occur without necessarily yielding a negative ERG. One potential limitation is possible mild cone system adaptation by the background.
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Affiliation(s)
- Xiaofan Jiang
- Institute of Ophthalmology, University College London, Bath Street, London, UK
- Department of Ophthalmology, King's College London, St Thomas' Hospital Campus, Westminster Bridge Road, London, UK
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Hospital Campus, Westminster Bridge Road, London, UK
| | - Taha Bhatti
- Department of Ophthalmology, King's College London, St Thomas' Hospital Campus, Westminster Bridge Road, London, UK
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Hospital Campus, Westminster Bridge Road, London, UK
| | - Ambreen Tariq
- Department of Ophthalmology, King's College London, St Thomas' Hospital Campus, Westminster Bridge Road, London, UK
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Hospital Campus, Westminster Bridge Road, London, UK
| | - Shaun M Leo
- NIHR Biomedical Research Centre at Moorfields Eye Hospital and the UCL Institute of Ophthalmology, 162 City Road, London, UK
| | - Nancy Aychoua
- Institute of Ophthalmology, University College London, Bath Street, London, UK
- NIHR Biomedical Research Centre at Moorfields Eye Hospital and the UCL Institute of Ophthalmology, 162 City Road, London, UK
| | - Andrew R Webster
- Institute of Ophthalmology, University College London, Bath Street, London, UK
- NIHR Biomedical Research Centre at Moorfields Eye Hospital and the UCL Institute of Ophthalmology, 162 City Road, London, UK
| | - Pirro G Hysi
- Department of Ophthalmology, King's College London, St Thomas' Hospital Campus, Westminster Bridge Road, London, UK
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Hospital Campus, Westminster Bridge Road, London, UK
| | - Christopher J Hammond
- Department of Ophthalmology, King's College London, St Thomas' Hospital Campus, Westminster Bridge Road, London, UK
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Hospital Campus, Westminster Bridge Road, London, UK
| | - Omar A Mahroo
- Institute of Ophthalmology, University College London, Bath Street, London, UK.
- Department of Ophthalmology, King's College London, St Thomas' Hospital Campus, Westminster Bridge Road, London, UK.
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Hospital Campus, Westminster Bridge Road, London, UK.
- NIHR Biomedical Research Centre at Moorfields Eye Hospital and the UCL Institute of Ophthalmology, 162 City Road, London, UK.
- Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK.
- Department of Translational Ophthalmology, Wills Eye Hospital, Philadelphia, PA, USA.
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16
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Mbarek H, Gordon SD, Duffy DL, Hubers N, Mortlock S, Beck JJ, Hottenga JJ, Pool R, Dolan CV, Actkins KV, Gerring ZF, Van Dongen J, Ehli EA, Iacono WG, Mcgue M, Chasman DI, Gallagher CS, Schilit SLP, Morton CC, Paré G, Willemsen G, Whiteman DC, Olsen CM, Derom C, Vlietinck R, Gudbjartsson D, Cannon-Albright L, Krapohl E, Plomin R, Magnusson PKE, Pedersen NL, Hysi P, Mangino M, Spector TD, Palviainen T, Milaneschi Y, Penninnx BW, Campos AI, Ong KK, Perry JRB, Lambalk CB, Kaprio J, Ólafsson Í, Duroure K, Revenu C, Rentería ME, Yengo L, Davis L, Derks EM, Medland SE, Stefansson H, Stefansson K, Del Bene F, Reversade B, Montgomery GW, Boomsma DI, Martin NG. Genome-wide association study meta-analysis of dizygotic twinning illuminates genetic regulation of female fecundity. Hum Reprod 2024; 39:240-257. [PMID: 38052102 PMCID: PMC10767824 DOI: 10.1093/humrep/dead247] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 09/14/2023] [Indexed: 12/07/2023] Open
Abstract
STUDY QUESTION Which genetic factors regulate female propensity for giving birth to spontaneous dizygotic (DZ) twins? SUMMARY ANSWER We identified four new loci, GNRH1, FSHR, ZFPM1, and IPO8, in addition to previously identified loci, FSHB and SMAD3. WHAT IS KNOWN ALREADY The propensity to give birth to DZ twins runs in families. Earlier, we reported that FSHB and SMAD3 as associated with DZ twinning and female fertility measures. STUDY DESIGN, SIZE, DURATION We conducted a genome-wide association meta-analysis (GWAMA) of mothers of spontaneous dizygotic (DZ) twins (8265 cases, 264 567 controls) and of independent DZ twin offspring (26 252 cases, 417 433 controls). PARTICIPANTS/MATERIALS, SETTING, METHODS Over 700 000 mothers of DZ twins, twin individuals and singletons from large cohorts in Australia/New Zealand, Europe, and the USA were carefully screened to exclude twins born after use of ARTs. Genetic association analyses by cohort were followed by meta-analysis, phenome wide association studies (PheWAS), in silico and in vivo annotations, and Zebrafish functional validation. MAIN RESULTS AND THE ROLE OF CHANCE This study enlarges the sample size considerably from previous efforts, finding four genome-wide significant loci, including two novel signals and a further two novel genes that are implicated by gene level enrichment analyses. The novel loci, GNRH1 and FSHR, have well-established roles in female reproduction whereas ZFPM1 and IPO8 have not previously been implicated in female fertility. We found significant genetic correlations with multiple aspects of female reproduction and body size as well as evidence for significant selection against DZ twinning during human evolution. The 26 top single nucleotide polymorphisms (SNPs) from our GWAMA in European-origin participants weakly predicted the crude twinning rates in 47 non-European populations (r = 0.23 between risk score and population prevalence, s.e. 0.11, 1-tail P = 0.058) indicating that genome-wide association studies (GWAS) are needed in African and Asian populations to explore the causes of their respectively high and low DZ twinning rates. In vivo functional tests in zebrafish for IPO8 validated its essential role in female, but not male, fertility. In most regions, risk SNPs linked to known expression quantitative trait loci (eQTLs). Top SNPs were associated with in vivo reproductive hormone levels with the top pathways including hormone ligand binding receptors and the ovulation cycle. LARGE SCALE DATA The full DZT GWAS summary statistics will made available after publication through the GWAS catalog (https://www.ebi.ac.uk/gwas/). LIMITATIONS, REASONS FOR CAUTION Our study only included European ancestry cohorts. Inclusion of data from Africa (with the highest twining rate) and Asia (with the lowest rate) would illuminate further the biology of twinning and female fertility. WIDER IMPLICATIONS OF THE FINDINGS About one in 40 babies born in the world is a twin and there is much speculation on why twinning runs in families. We hope our results will inform investigations of ovarian response in new and existing ARTs and the causes of female infertility. STUDY FUNDING/COMPETING INTEREST(S) Support for the Netherlands Twin Register came from the Netherlands Organization for Scientific Research (NWO) and The Netherlands Organization for Health Research and Development (ZonMW) grants, 904-61-193, 480-04-004, 400-05-717, Addiction-31160008, 911-09-032, Biobanking and Biomolecular Resources Research Infrastructure (BBMRI.NL, 184.021.007), Royal Netherlands Academy of Science Professor Award (PAH/6635) to DIB, European Research Council (ERC-230374), Rutgers University Cell and DNA Repository (NIMH U24 MH068457-06), the Avera Institute, Sioux Falls, South Dakota (USA) and the National Institutes of Health (NIH R01 HD042157-01A1) and the Genetic Association Information Network (GAIN) of the Foundation for the National Institutes of Health and Grand Opportunity grants 1RC2 MH089951. The QIMR Berghofer Medical Research Institute (QIMR) study was supported by grants from the National Health and Medical Research Council (NHMRC) of Australia (241944, 339462, 389927, 389875, 389891, 389892, 389938, 443036, 442915, 442981, 496610, 496739, 552485, 552498, 1050208, 1075175). L.Y. is funded by Australian Research Council (Grant number DE200100425). The Minnesota Center for Twin and Family Research (MCTFR) was supported in part by USPHS Grants from the National Institute on Alcohol Abuse and Alcoholism (AA09367 and AA11886) and the National Institute on Drug Abuse (DA05147, DA13240, and DA024417). The Women's Genome Health Study (WGHS) was funded by the National Heart, Lung, and Blood Institute (HL043851 and HL080467) and the National Cancer Institute (CA047988 and UM1CA182913), with support for genotyping provided by Amgen. Data collection in the Finnish Twin Registry has been supported by the Wellcome Trust Sanger Institute, the Broad Institute, ENGAGE-European Network for Genetic and Genomic Epidemiology, FP7-HEALTH-F4-2007, grant agreement number 201413, National Institute of Alcohol Abuse and Alcoholism (grants AA-12502, AA-00145, AA-09203, AA15416, and K02AA018755) and the Academy of Finland (grants 100499, 205585, 118555, 141054, 264146, 308248, 312073 and 336823 to J. Kaprio). TwinsUK is funded by the Wellcome Trust, Medical Research Council, Versus Arthritis, European Union Horizon 2020, Chronic Disease Research Foundation (CDRF), Zoe Ltd and the National Institute for Health Research (NIHR) Clinical Research Network (CRN) and Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust in partnership with King's College London. For NESDA, funding was obtained from the Netherlands Organization for Scientific Research (Geestkracht program grant 10000-1002), the Center for Medical Systems Biology (CSMB, NVVO Genomics), Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-NL), VU University's Institutes for Health and Care Research (EMGO+) and Neuroscience Campus Amsterdam, University Medical Center Groningen, Leiden University Medical Center, National Institutes of Health (NIH, ROI D0042157-01A, MH081802, Grand Opportunity grants 1 RC2 Ml-1089951 and IRC2 MH089995). Part of the genotyping and analyses were funded by the Genetic Association Information Network (GAIN) of the Foundation for the National Institutes of Health. Computing was supported by BiG Grid, the Dutch e-Science Grid, which is financially supported by NWO. Work in the Del Bene lab was supported by the Programme Investissements d'Avenir IHU FOReSIGHT (ANR-18-IAHU-01). C.R. was supported by an EU Horizon 2020 Marie Skłodowska-Curie Action fellowship (H2020-MSCA-IF-2014 #661527). H.S. and K.S. are employees of deCODE Genetics/Amgen. The other authors declare no competing financial interests. TRIAL REGISTRATION NUMBER N/A.
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Affiliation(s)
- Hamdi Mbarek
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, The Netherlands
- Qatar Genome Program, Qatar Foundation, Doha, Qatar
- Amsterdam Reproduction and Development Institute, Amsterdam, The Netherlands
| | - Scott D Gordon
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - David L Duffy
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Nikki Hubers
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development Institute, Amsterdam, The Netherlands
| | - Sally Mortlock
- Institute of Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Jeffrey J Beck
- Avera Institute for Human Genetics, Avera McKennan Hospital and University Health Center, Sioux Falls, SD, USA
| | - Jouke-Jan Hottenga
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, The Netherlands
| | - René Pool
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, The Netherlands
| | - Conor V Dolan
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, The Netherlands
| | - Ky’Era V Actkins
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA
| | | | - Jenny Van Dongen
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development Institute, Amsterdam, The Netherlands
| | - Erik A Ehli
- Avera Institute for Human Genetics, Avera McKennan Hospital and University Health Center, Sioux Falls, SD, USA
| | - William G Iacono
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Matt Mcgue
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Daniel I Chasman
- Harvard Medical School, Harvard University, Boston, MA, USA
- Brigham and Women’s Hospital, Boston, MA, USA
| | | | - Samantha L P Schilit
- Harvard Medical School, Harvard University, Boston, MA, USA
- Brigham and Women’s Hospital, Boston, MA, USA
| | - Cynthia C Morton
- Harvard Medical School, Harvard University, Boston, MA, USA
- Brigham and Women’s Hospital, Boston, MA, USA
| | - Guillaume Paré
- Population Health Research Institute, McMaster University, Hamilton, ON, Canada
| | - Gonneke Willemsen
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, The Netherlands
| | | | | | | | | | | | | | - Eva Krapohl
- Medical Research Council Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
- Statistical Sciences & Innovation, UCB Biosciences GmbH, Monheim, Germany
| | - Robert Plomin
- Medical Research Council Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
| | - Patrik K E Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Nancy L Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Pirro Hysi
- Department of Twin Research & Genetic Epidemiology, King’s College London, London, UK
| | - Massimo Mangino
- Department of Twin Research & Genetic Epidemiology, King’s College London, London, UK
- NIHR Biomedical Research Centre at Guy’s and St Thomas’ Foundation Trust, London, UK
| | - Timothy D Spector
- Department of Twin Research & Genetic Epidemiology, King’s College London, London, UK
| | - Teemu Palviainen
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
| | - Yuri Milaneschi
- Department of Psychiatry, EMGO Institute for Health and Care Research, Vrije Universiteit, Amsterdam, The Netherlands
| | - Brenda W Penninnx
- Department of Psychiatry, EMGO Institute for Health and Care Research, Vrije Universiteit, Amsterdam, The Netherlands
| | - Adrian I Campos
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- Institute of Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Ken K Ong
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - John R B Perry
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Cornelis B Lambalk
- Amsterdam Reproduction and Development Institute, Amsterdam, The Netherlands
- Amsterdam University Medical Centers Location VU Medical Center, Amsterdam, The Netherlands
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
| | - Ísleifur Ólafsson
- Department of Clinical Biochemistry, National University Hospital of Iceland, Reykjavik, Iceland
| | - Karine Duroure
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France
| | - Céline Revenu
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France
| | | | - Loic Yengo
- Institute of Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Lea Davis
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA
| | - Eske M Derks
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | | | | | - Filippo Del Bene
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France
| | - Bruno Reversade
- Genome Institute of Singapore, Laboratory of Human Genetics & Therapeutics, A*STAR, Singapore, Singapore
- Smart-Health Initiative, BESE, KAUST, Thuwal, Saudi Arabia
| | - Grant W Montgomery
- Institute of Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Dorret I Boomsma
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development Institute, Amsterdam, The Netherlands
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17
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Nogal A, Alkis T, Lee Y, Kifer D, Hu J, Murphy RA, Huang Z, Wang-Sattler R, Kastenmüler G, Linkohr B, Barrios C, Crespo M, Gieger C, Peters A, Price J, Rexrode KM, Yu B, Menni C. Predictive metabolites for incident myocardial infarction: a two-step meta-analysis of individual patient data from six cohorts comprising 7897 individuals from the COnsortium of METabolomics Studies. Cardiovasc Res 2023; 119:2743-2754. [PMID: 37706562 PMCID: PMC10757581 DOI: 10.1093/cvr/cvad147] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 06/28/2023] [Accepted: 07/18/2023] [Indexed: 09/15/2023] Open
Abstract
AIMS Myocardial infarction (MI) is a major cause of death and disability worldwide. Most metabolomics studies investigating metabolites predicting MI are limited by the participant number and/or the demographic diversity. We sought to identify biomarkers of incident MI in the COnsortium of METabolomics Studies. METHODS AND RESULTS We included 7897 individuals aged on average 66 years from six intercontinental cohorts with blood metabolomic profiling (n = 1428 metabolites, of which 168 were present in at least three cohorts with over 80% prevalence) and MI information (1373 cases). We performed a two-stage individual patient data meta-analysis. We first assessed the associations between circulating metabolites and incident MI for each cohort adjusting for traditional risk factors and then performed a fixed effect inverse variance meta-analysis to pull the results together. Finally, we conducted a pathway enrichment analysis to identify potential pathways linked to MI. On meta-analysis, 56 metabolites including 21 lipids and 17 amino acids were associated with incident MI after adjusting for multiple testing (false discovery rate < 0.05), and 10 were novel. The largest increased risk was observed for the carbohydrate mannitol/sorbitol {hazard ratio [HR] [95% confidence interval (CI)] = 1.40 [1.26-1.56], P < 0.001}, whereas the largest decrease in risk was found for glutamine [HR (95% CI) = 0.74 (0.67-0.82), P < 0.001]. Moreover, the identified metabolites were significantly enriched (corrected P < 0.05) in pathways previously linked with cardiovascular diseases, including aminoacyl-tRNA biosynthesis. CONCLUSIONS In the most comprehensive metabolomic study of incident MI to date, 10 novel metabolites were associated with MI. Metabolite profiles might help to identify high-risk individuals before disease onset. Further research is needed to fully understand the mechanisms of action and elaborate pathway findings.
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Affiliation(s)
- Ana Nogal
- Department of Twin Research, King’s College London, St Thomas’ Hospital Campus, Westminster Bridge Road, SE1 7EH London, UK
| | - Taryn Alkis
- Department of Epidemiology, Human Genetics and Environmental Sciences, University of Texas Health Science Center at Houston School of Public Health, 1200 Pressler St, Suite E407, Houston, 77030 TX, USA
| | - Yura Lee
- Department of Epidemiology, Human Genetics and Environmental Sciences, University of Texas Health Science Center at Houston School of Public Health, 1200 Pressler St, Suite E407, Houston, 77030 TX, USA
| | - Domagoj Kifer
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, Croatia
| | - Jie Hu
- Division of Women’s Health, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Rachel A Murphy
- Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
- Cancer Control Research, BC Cancer, Vancouver, BC, Canada
| | - Zhe Huang
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Rui Wang-Sattler
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Gabi Kastenmüler
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Birgit Linkohr
- Institute of Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Clara Barrios
- Department of Nephrology, Hospital del Mar, Institut Hospital del Mar d´Investigacions Mediques, Barcelona, Spain
| | - Marta Crespo
- Department of Nephrology, Hospital del Mar, Institut Hospital del Mar d´Investigacions Mediques, Barcelona, Spain
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Jackie Price
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Kathryn M Rexrode
- Division of Women’s Health, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Bing Yu
- Department of Epidemiology, Human Genetics and Environmental Sciences, University of Texas Health Science Center at Houston School of Public Health, 1200 Pressler St, Suite E407, Houston, 77030 TX, USA
| | - Cristina Menni
- Department of Twin Research, King’s College London, St Thomas’ Hospital Campus, Westminster Bridge Road, SE1 7EH London, UK
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18
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Nogal A, Tettamanzi F, Dong Q, Louca P, Visconti A, Christiansen C, Breuninger T, Linseisen J, Grallert H, Wawro N, Asnicar F, Wong K, Baleanu AF, Michelotti GA, Segata N, Falchi M, Peters A, Franks PW, Bagnardi V, Spector TD, Bell JT, Gieger C, Valdes AM, Menni C. A Fecal Metabolite Signature of Impaired Fasting Glucose: Results From Two Independent Population-Based Cohorts. Diabetes 2023; 72:1870-1880. [PMID: 37699401 PMCID: PMC10658071 DOI: 10.2337/db23-0170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 08/30/2023] [Indexed: 09/14/2023]
Abstract
Prediabetes is a metabolic condition associated with gut microbiome composition, although mechanisms remain elusive. We searched for fecal metabolites, a readout of gut microbiome function, associated with impaired fasting glucose (IFG) in 142 individuals with IFG and 1,105 healthy individuals from the UK Adult Twin Registry (TwinsUK). We used the Cooperative Health Research in the Region of Augsburg (KORA) cohort (318 IFG individuals, 689 healthy individuals) to replicate our findings. We linearly combined eight IFG-positively associated metabolites (1-methylxantine, nicotinate, glucuronate, uridine, cholesterol, serine, caffeine, and protoporphyrin IX) into an IFG-metabolite score, which was significantly associated with higher odds ratios (ORs) for IFG (TwinsUK: OR 3.9 [95% CI 3.02-5.02], P < 0.0001, KORA: OR 1.3 [95% CI 1.16-1.52], P < 0.0001) and incident type 2 diabetes (T2D; TwinsUK: hazard ratio 4 [95% CI 1.97-8], P = 0.0002). Although these are host-produced metabolites, we found that the gut microbiome is strongly associated with their fecal levels (area under the curve >70%). Abundances of Faecalibacillus intestinalis, Dorea formicigenerans, Ruminococcus torques, and Dorea sp. AF24-7LB were positively associated with IFG, and such associations were partially mediated by 1-methylxanthine and nicotinate (variance accounted for mean 14.4% [SD 5.1], P < 0.05). Our results suggest that the gut microbiome is linked to prediabetes not only via the production of microbial metabolites but also by affecting intestinal absorption/excretion of host-produced metabolites and xenobiotics, which are correlated with the risk of IFG. Fecal metabolites enable modeling of another mechanism of gut microbiome effect on prediabetes and T2D onset. ARTICLE HIGHLIGHTS Prediabetes is a metabolic condition associated with gut microbiome composition, although mechanisms remain elusive. We investigated whether there is a fecal metabolite signature of impaired fasting glucose (IFG) and the possible underlying mechanisms of action. We identified a fecal metabolite signature of IFG associated with prevalent IFG in two independent cohorts and incident type 2 diabetes in a subanalysis. Although the signature consists of metabolites of nonmicrobial origin, it is strongly correlated with gut microbiome composition. Fecal metabolites enable modeling of another mechanism of gut microbiome effect on prediabetes by affecting intestinal absorption or excretion of host compounds and xenobiotics.
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Affiliation(s)
- Ana Nogal
- Department of Twin Research, King’s College London, St Thomas’ Hospital Campus, London, U.K
| | - Francesca Tettamanzi
- Department of Twin Research, King’s College London, St Thomas’ Hospital Campus, London, U.K
- Humanitas Clinical and Research Centre, IRCCS, Rozzano (Milan), Italy
| | - Qiuling Dong
- Institute of Epidemiology, Helmholtz Zentrum München, Research Unit of Molecular Epidemiology, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Panayiotis Louca
- Department of Twin Research, King’s College London, St Thomas’ Hospital Campus, London, U.K
| | - Alessia Visconti
- Department of Twin Research, King’s College London, St Thomas’ Hospital Campus, London, U.K
| | - Colette Christiansen
- Department of Twin Research, King’s College London, St Thomas’ Hospital Campus, London, U.K
- School of Mathematics and Statistics, The Open University, Milton Keynes, U.K
| | - Taylor Breuninger
- Epidemiology, University Hospital Augsburg, University of Augsburg, Augsburg, Germany
| | - Jakob Linseisen
- Epidemiology, University Hospital Augsburg, University of Augsburg, Augsburg, Germany
- ZIEL-Institute for Food & Health, Technische Universität München, Freising, Germany
- Institute for Medical Information Processing, Biometry, and Epidemiology, Medical Faculty, Ludwig-Maximilian University Munich, Munich, Germany
| | - Harald Grallert
- Institute of Epidemiology, Helmholtz Zentrum München, Research Unit of Molecular Epidemiology, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Nina Wawro
- Epidemiology, University Hospital Augsburg, University of Augsburg, Augsburg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Francesco Asnicar
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | | | - Andrei-Florin Baleanu
- Department of Twin Research, King’s College London, St Thomas’ Hospital Campus, London, U.K
| | | | - Nicola Segata
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Mario Falchi
- Department of Twin Research, King’s College London, St Thomas’ Hospital Campus, London, U.K
| | - Annette Peters
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- Munich Heart Alliance, German Center for Cardiovascular Research (DZHK e.V., Partner-Site Munich), Munich, Germany
| | - Paul W. Franks
- Lund University Diabetes Center, Lund University, Malmö, Sweden
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Vincenzo Bagnardi
- Department of Statistics and Quantitative Methods, University of Milan-Bicocca, Milan, Italy
| | - Tim D. Spector
- Department of Twin Research, King’s College London, St Thomas’ Hospital Campus, London, U.K
| | - Jordana T. Bell
- Department of Twin Research, King’s College London, St Thomas’ Hospital Campus, London, U.K
| | - Christian Gieger
- Institute of Epidemiology, Helmholtz Zentrum München, Research Unit of Molecular Epidemiology, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Ana M. Valdes
- Academic Rheumatology Clinical Sciences Building, Nottingham City Hospital, University of Nottingham, U.K
| | - Cristina Menni
- Department of Twin Research, King’s College London, St Thomas’ Hospital Campus, London, U.K
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19
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Cederroth CR, Hong MG, Freydin MB, Edvall NK, Trpchevska N, Jarach C, Schlee W, Schwenk JM, Lopez-Escamez JA, Gallus S, Canlon B, Bulla J, Williams FMK. Screening for Circulating Inflammatory Proteins Does Not Reveal Plasma Biomarkers of Constant Tinnitus. J Assoc Res Otolaryngol 2023; 24:593-606. [PMID: 38079022 PMCID: PMC10752855 DOI: 10.1007/s10162-023-00920-3] [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: 09/02/2022] [Accepted: 11/22/2023] [Indexed: 12/29/2023] Open
Abstract
BACKGROUND AND OBJECTIVE Tinnitus would benefit from an objective biomarker. The goal of this study is to identify plasma biomarkers of constant and chronic tinnitus among selected circulating inflammatory proteins. METHODS A case-control retrospective study on 548 cases with constant tinnitus and 548 matched controls from the Swedish Tinnitus Outreach Project (STOP), whose plasma samples were examined using Olink's Inflammatory panel. Replication and meta-analysis were performed using the same method on samples from the TwinsUK cohort. Participants from LifeGene, whose blood was collected in Stockholm and Umeå, were recruited to STOP for a tinnitus subtyping study. An age and sex matching was performed at the individual level. TwinsUK participants (n = 928) were selected based on self-reported tinnitus status over 2 to 10 years. Primary outcomes include normalized levels for 96 circulating proteins, which were used as an index test. No reference standard was available in this study. RESULTS After adjustment for age, sex, BMI, smoking, hearing loss, and laboratory site, the top proteins identified were FGF-21, MCP4, GDNF, CXCL9, and MCP-1; however, these were no longer statistically significant after correction for multiple testing. Stratification by sex did not yield any significant associations. Similarly, associations with hearing loss or other tinnitus-related comorbidities such as stress, anxiety, depression, hyperacusis, temporomandibular joint disorders, and headache did not yield any significant associations. Analysis in the TwinsUK failed in replicating the top candidates. Meta-analysis of STOP and TwinsUK did not reveal any significant association. Using elastic net regularization, models exhibited poor predictive capacity tinnitus based on inflammatory markers [sensitivity = 0.52 (95% CI 0.47-0.57), specificity = 0.53 (0.48-0.58), positive predictive value = 0.52 (0.47-0.56), negative predictive values = 0.53 (0.49-0.58), and AUC = 0.53 (0.49-0.56)]. DISCUSSION Our results did not identify significant associations of the selected inflammatory proteins with constant tinnitus. Future studies examining longitudinal relations among those with more severe tinnitus and using more recent expanded proteomics platforms and sampling of cerebrospinal fluid could increase the likelihood of identifying relevant molecular biomarkers.
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Affiliation(s)
- Christopher R Cederroth
- Section of Experimental Audiology, Department of Physiology and Pharmacology, Karolinska Institute, Stockholm, Sweden.
- National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre, Nottingham University Hospitals NHS Trust, Ropewalk House, Nottingham, UK.
- Department of Otolaryngology, Head and Neck Surgery, Translational Hearing Research, Tübingen Hearing Research Center, University of Tübingen, Tubingen, Germany.
| | - Mun-Gwan Hong
- Affinity Proteomics, Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, Sweden
- Science for Life Laboratory, Department of Biochemistry and Biophysics, National Bioinformatics Infrastructure Sweden, Stockholm University, Stockholm, Sweden
| | - Maxim B Freydin
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK
| | - Niklas K Edvall
- Section of Experimental Audiology, Department of Physiology and Pharmacology, Karolinska Institute, Stockholm, Sweden
| | - Natalia Trpchevska
- Section of Experimental Audiology, Department of Physiology and Pharmacology, Karolinska Institute, Stockholm, Sweden
| | - Carlotta Jarach
- Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Winfried Schlee
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
| | - Jochen M Schwenk
- Science for Life Laboratory, Department of Biochemistry and Biophysics, National Bioinformatics Infrastructure Sweden, Stockholm University, Stockholm, Sweden
| | - Jose-Antonio Lopez-Escamez
- Faculty of Medicine & Health, School of Medical Sciences, Meniere's Disease Neuroscience Research Program, The Kolling Institute, University of Sydney, Sydney, NSW, Australia
- Otology and Neurotology Group CTS495, Department of Genomic Medicine, GENYO - Centre for Genomics and Oncological Research - Pfizer, University of Granada, PTS, Junta de Andalucía, Granada, Spain
- Division of Otolaryngology, Department of Surgery, Instituto de Investigación Biosanitaria, ibs.GRANADA, Universidad de Granada, GranadaGranada, Spain
| | - Silvano Gallus
- Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Barbara Canlon
- Section of Experimental Audiology, Department of Physiology and Pharmacology, Karolinska Institute, Stockholm, Sweden
| | - Jan Bulla
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
- Department of Mathematics, University of Bergen, Bergen, Norway
| | - Frances M K Williams
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK
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20
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Luo H, Zhang P, Zhang W, Zheng Y, Hao D, Shi Y, Niu Y, Song T, Li Y, Zhao S, Chen H, Xu T, He S. Recent positive selection signatures reveal phenotypic evolution in the Han Chinese population. Sci Bull (Beijing) 2023; 68:2391-2404. [PMID: 37661541 DOI: 10.1016/j.scib.2023.08.027] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 05/08/2023] [Accepted: 08/10/2023] [Indexed: 09/05/2023]
Abstract
Characterizing natural selection signatures and relationships with phenotype spectra is important for understanding human evolution and both biological and pathological mechanisms. Here, we identified 24 genetic loci under recent selection by analyzing rare singletons in 3946 high-depth whole-genome sequencing data of Han Chinese. The loci include immune-related gene regions (MHC cluster, IGH cluster, STING1, and PSG), alcohol metabolism-related gene regions (ADH1B, ALDH2, and ALDH3B2), and the olfactory perception gene OR4C16, in which the MHC cluster, ADH1B, and ALDH2 were also identified by TOPMed and WestLake Biobank. Among the signals, the IGH cluster is particularly interesting, in which the favored allele of variant 14_105737776_C_T (rs117518546, IgG1-G396R) promotes immune response, but also increases the risk of an autoimmune disease systemic lupus erythematosus (SLE). It is also surprising that our newly discovered ALDH3B2 evolved in the opposite direction to ALDH2 for alcohol metabolism. Besides monogenic traits, we found that multiple complex traits experienced polygenic adaptation. Particularly, multi-methods consistently revealed that lower blood pressure was favored in natural selection. Finally, we built a database named RePoS (recent positive selection, http://bigdata.ibp.ac.cn/RePoS/) to integrate and display multi-population selection signals. Our study extended our understanding of natural evolution and phenotype adaptation in Han Chinese as well as other populations.
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Affiliation(s)
- Huaxia Luo
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; Department of Pediatrics, Peking University First Hospital, Beijing 100034, China
| | - Peng Zhang
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Wanyu Zhang
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Yu Zheng
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Di Hao
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Yirong Shi
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yiwei Niu
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tingrui Song
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Yanyan Li
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Shilei Zhao
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; China National Center for Bioinformation, Beijing 100101, China
| | - Hua Chen
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; China National Center for Bioinformation, Beijing 100101, China.
| | - Tao Xu
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; Shandong First Medical University & Shandong Academy of Medical Sciences, Taian 271016, China.
| | - Shunmin He
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
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21
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Costeira R, Evangelista L, Wilson R, Yan X, Hellbach F, Sinke L, Christiansen C, Villicaña S, Masachs OM, Tsai PC, Mangino M, Menni C, Berry SE, Beekman M, van Heemst D, Slagboom PE, Heijmans BT, Suhre K, Kastenmüller G, Gieger C, Peters A, Small KS, Linseisen J, Waldenberger M, Bell JT. Metabolomic biomarkers of habitual B vitamin intakes unveil novel differentially methylated positions in the human epigenome. Clin Epigenetics 2023; 15:166. [PMID: 37858220 PMCID: PMC10588110 DOI: 10.1186/s13148-023-01578-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 10/04/2023] [Indexed: 10/21/2023] Open
Abstract
BACKGROUND B vitamins such as folate (B9), B6, and B12 are key in one carbon metabolism, which generates methyl donors for DNA methylation. Several studies have linked differential methylation to self-reported intakes of folate and B12, but these estimates can be imprecise, while metabolomic biomarkers can offer an objective assessment of dietary intakes. We explored blood metabolomic biomarkers of folate and vitamins B6 and B12, to carry out epigenome-wide analyses across up to three European cohorts. Associations between self-reported habitual daily B vitamin intakes and 756 metabolites (Metabolon Inc.) were assessed in serum samples from 1064 UK participants from the TwinsUK cohort. The identified B vitamin metabolomic biomarkers were then used in epigenome-wide association tests with fasting blood DNA methylation levels at 430,768 sites from the Infinium HumanMethylation450 BeadChip in blood samples from 2182 European participants from the TwinsUK and KORA cohorts. Candidate signals were explored for metabolite associations with gene expression levels in a subset of the TwinsUK sample (n = 297). Metabolomic biomarker epigenetic associations were also compared with epigenetic associations of self-reported habitual B vitamin intakes in samples from 2294 European participants. RESULTS Eighteen metabolites were associated with B vitamin intakes after correction for multiple testing (Bonferroni-adj. p < 0.05), of which 7 metabolites were available in both cohorts and tested for epigenome-wide association. Three metabolites - pipecolate (metabolomic biomarker of B6 and folate intakes), pyridoxate (marker of B6 and folate) and docosahexaenoate (DHA, marker of B6) - were associated with 10, 3 and 1 differentially methylated positions (DMPs), respectively. The strongest association was observed between DHA and DMP cg03440556 in the SCD gene (effect = 0.093 ± 0.016, p = 4.07E-09). Pyridoxate, a catabolic product of vitamin B6, was inversely associated with CpG methylation near the SLC1A5 gene promoter region (cg02711608 and cg22304262) and with SLC7A11 (cg06690548), but not with corresponding changes in gene expression levels. The self-reported intake of folate and vitamin B6 had consistent but non-significant associations with the epigenetic signals. CONCLUSION Metabolomic biomarkers are a valuable approach to investigate the effects of dietary B vitamin intake on the human epigenome.
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Affiliation(s)
- Ricardo Costeira
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK.
| | - Laila Evangelista
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Rory Wilson
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany
| | - Xinyu Yan
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Fabian Hellbach
- Epidemiology, Medical Faculty, University Augsburg, University Hospital Augsburg, 86156, Augsburg, Germany
| | - Lucy Sinke
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZC, Leiden, The Netherlands
| | - Colette Christiansen
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Sergio Villicaña
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Olatz M Masachs
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Pei-Chien Tsai
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
- Department of Biomedical Sciences, Chang Gung University, Taoyuan City, Taiwan
- Division of Pediatric Infectious Diseases, Department of Pediatrics, Chang Gung Memorial Hospital, Taoyuan City, Taiwan
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Cristina Menni
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Sarah E Berry
- Department of Nutritional Sciences, King's College London, London, SE1 9NH, UK
| | - Marian Beekman
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZC, Leiden, The Netherlands
| | - Diana van Heemst
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, 2300RC, Leiden, The Netherlands
| | - P Eline Slagboom
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZC, Leiden, The Netherlands
| | - Bastiaan T Heijmans
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZC, Leiden, The Netherlands
| | - Karsten Suhre
- Department of Biophysics and Physiology, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany
| | - Christian Gieger
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany
- German Research Center for Cardiovascular Disease (DZHK), Partner Site Munich Heart Alliance, 80802, Munich, Germany
| | - Annette Peters
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany
- German Research Center for Cardiovascular Disease (DZHK), Partner Site Munich Heart Alliance, 80802, Munich, Germany
- Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Ludwig-Maximilians-Universität München, 81377, Munich, Germany
| | - Kerrin S Small
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Jakob Linseisen
- Epidemiology, Medical Faculty, University Augsburg, University Hospital Augsburg, 86156, Augsburg, Germany
- Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Ludwig-Maximilians-Universität München, 81377, Munich, Germany
| | - Melanie Waldenberger
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany
- German Research Center for Cardiovascular Disease (DZHK), Partner Site Munich Heart Alliance, 80802, Munich, Germany
| | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK.
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22
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Gualdi F, Smith IG, Boixader RC, Williams FMK. Modic change is associated with increased BMI but not autoimmune diseases in TwinsUK. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2023; 32:3379-3386. [PMID: 37555954 DOI: 10.1007/s00586-023-07870-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 06/24/2023] [Accepted: 07/22/2023] [Indexed: 08/10/2023]
Abstract
PURPOSE Low back pain (LBP) is one of the largest causes of morbidity worldwide. The aetiology of LBP is complex, and many factors contribute to the onset. Bone marrow lesions within the vertebra adjacent to an intervertebral degenerate disc named Modic change (MC) have been suggested as a diagnostic subgroup of LBP. Autoimmune response has been proposed to be one of the causes that promote the development of MC. The aim of the current investigation is to assess prevalence and severity of MC and LBP in participants with an autoimmune disease diagnosis in a well-documented cohort of adult twin volunteers. METHODS Multivariate generalized mixed linear models (GLMM) were implemented in order to calculate the association between having an autoimmune disorder and MC prevalence, width and severe and disabling LBP. The model was corrected for family structure as well as for covariates such as age, BMI and smoking. RESULTS No association was found between diagnosis of autoimmune disorder and MC. Interestingly, BMI was independently associated with MC width but not to MC prevalence. These results help to shed light on the relationship between MC and autoimmunity as well as the role of BMI in the development of the lesions. CONCLUSION This study is the first to examine autoimmune disorders and MC prevalence in a large, population-based female cohort. The study was well powered to detect a small effect. No association was found between having a diagnosis of one or more autoimmune conditions and MC prevalence, width or LBP.
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Affiliation(s)
- Francesco Gualdi
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Pompeu Fabra University (UPF), Barcelona, Spain.
- Structural Bioinformatics Lab (GRIB-IMIM), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, 08005, Barcelona, Catalonia, Spain.
- Department Twin Research and Genetic Epidemiology, King's College London, St Thomas Hospital, Westminster Bridge Road, London, SE1 7EH, UK.
| | - Isabelle Granville Smith
- Department Twin Research and Genetic Epidemiology, King's College London, St Thomas Hospital, Westminster Bridge Road, London, SE1 7EH, UK
| | - Roger Compte Boixader
- Department Twin Research and Genetic Epidemiology, King's College London, St Thomas Hospital, Westminster Bridge Road, London, SE1 7EH, UK
| | - Frances M K Williams
- Department Twin Research and Genetic Epidemiology, King's College London, St Thomas Hospital, Westminster Bridge Road, London, SE1 7EH, UK
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23
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Murray A, Gough G, Cindrić A, Vučković F, Koschut D, Borelli V, Petrović DJ, Bekavac A, Plećaš A, Hribljan V, Brunmeir R, Jurić J, Pučić-Baković M, Slana A, Deriš H, Frkatović A, Groet J, O'Brien NL, Chen HY, Yeap YJ, Delom F, Havlicek S, Gammon L, Hamburg S, Startin C, D'Souza H, Mitrečić D, Kero M, Odak L, Krušlin B, Krsnik Ž, Kostović I, Foo JN, Loh YH, Dunn NR, de la Luna S, Spector T, Barišić I, Thomas MSC, Strydom A, Franceschi C, Lauc G, Krištić J, Alić I, Nižetić D. Dose imbalance of DYRK1A kinase causes systemic progeroid status in Down syndrome by increasing the un-repaired DNA damage and reducing LaminB1 levels. EBioMedicine 2023; 94:104692. [PMID: 37451904 PMCID: PMC10435767 DOI: 10.1016/j.ebiom.2023.104692] [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: 02/02/2023] [Revised: 06/15/2023] [Accepted: 06/20/2023] [Indexed: 07/18/2023] Open
Abstract
BACKGROUND People with Down syndrome (DS) show clinical signs of accelerated ageing. Causative mechanisms remain unknown and hypotheses range from the (essentially untreatable) amplified-chromosomal-instability explanation, to potential actions of individual supernumerary chromosome-21 genes. The latter explanation could open a route to therapeutic amelioration if the specific over-acting genes could be identified and their action toned-down. METHODS Biological age was estimated through patterns of sugar molecules attached to plasma immunoglobulin-G (IgG-glycans, an established "biological-ageing-clock") in n = 246 individuals with DS from three European populations, clinically characterised for the presence of co-morbidities, and compared to n = 256 age-, sex- and demography-matched healthy controls. Isogenic human induced pluripotent stem cell (hiPSCs) models of full and partial trisomy-21 with CRISPR-Cas9 gene editing and two kinase inhibitors were studied prior and after differentiation to cerebral organoids. FINDINGS Biological age in adults with DS is (on average) 18.4-19.1 years older than in chronological-age-matched controls independent of co-morbidities, and this shift remains constant throughout lifespan. Changes are detectable from early childhood, and do not require a supernumerary chromosome, but are seen in segmental duplication of only 31 genes, along with increased DNA damage and decreased levels of LaminB1 in nucleated blood cells. We demonstrate that these cell-autonomous phenotypes can be gene-dose-modelled and pharmacologically corrected in hiPSCs and derived cerebral organoids. Using isogenic hiPSC models we show that chromosome-21 gene DYRK1A overdose is sufficient and necessary to cause excess unrepaired DNA damage. INTERPRETATION Explanation of hitherto observed accelerated ageing in DS as a developmental progeroid syndrome driven by DYRK1A overdose provides a target for early pharmacological preventative intervention strategies. FUNDING Main funding came from the "Research Cooperability" Program of the Croatian Science Foundation funded by the European Union from the European Social Fund under the Operational Programme Efficient Human Resources 2014-2020, Project PZS-2019-02-4277, and the Wellcome Trust Grants 098330/Z/12/Z and 217199/Z/19/Z (UK). All other funding is described in details in the "Acknowledgements".
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Affiliation(s)
- Aoife Murray
- Faculty of Medicine and Dentistry, Blizard Institute, Queen Mary University of London, London, UK; The London Down Syndrome Consortium (LonDownS), London, UK.
| | - Gillian Gough
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Ana Cindrić
- Glycoscience Research Laboratory, Genos Ltd., Zagreb, Croatia
| | - Frano Vučković
- Glycoscience Research Laboratory, Genos Ltd., Zagreb, Croatia
| | - David Koschut
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; Disease Intervention Technology Laboratory (DITL), Institute of Molecular and Cellular Biology (IMCB), Agency for Science, Technology and Research (A∗STAR), Singapore
| | - Vincenzo Borelli
- Department of Medical and Surgical Sciences, Alma Mater Studiorum University of Bologna, Italy
| | - Dražen J Petrović
- Glycoscience Research Laboratory, Genos Ltd., Zagreb, Croatia; Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Ana Bekavac
- Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Ante Plećaš
- Faculty of Veterinary Medicine, Department of Anatomy, Histology and Embryology, University of Zagreb, Zagreb, Croatia
| | - Valentina Hribljan
- Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Reinhard Brunmeir
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Julija Jurić
- Glycoscience Research Laboratory, Genos Ltd., Zagreb, Croatia
| | | | - Anita Slana
- Glycoscience Research Laboratory, Genos Ltd., Zagreb, Croatia
| | - Helena Deriš
- Glycoscience Research Laboratory, Genos Ltd., Zagreb, Croatia
| | - Azra Frkatović
- Glycoscience Research Laboratory, Genos Ltd., Zagreb, Croatia
| | - Jűrgen Groet
- Faculty of Medicine and Dentistry, Blizard Institute, Queen Mary University of London, London, UK; The London Down Syndrome Consortium (LonDownS), London, UK
| | - Niamh L O'Brien
- Faculty of Medicine and Dentistry, Blizard Institute, Queen Mary University of London, London, UK; The London Down Syndrome Consortium (LonDownS), London, UK
| | - Hong Yu Chen
- Institute of Molecular and Cell Biology (IMCB), A∗STAR, Singapore
| | - Yee Jie Yeap
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Frederic Delom
- Faculty of Medicine and Dentistry, Blizard Institute, Queen Mary University of London, London, UK
| | - Steven Havlicek
- Laboratory of Neurogenetics, Genome Institute of Singapore, A∗STAR, Singapore
| | - Luke Gammon
- Faculty of Medicine and Dentistry, Blizard Institute, Queen Mary University of London, London, UK
| | - Sarah Hamburg
- The London Down Syndrome Consortium (LonDownS), London, UK
| | - Carla Startin
- The London Down Syndrome Consortium (LonDownS), London, UK; Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Division of Psychiatry, University College London, London, UK; School of Psychology, University of Roehampton, London, UK
| | - Hana D'Souza
- The London Down Syndrome Consortium (LonDownS), London, UK; Centre for Brain and Cognitive Development, Birkbeck, University of London, London, UK
| | - Dinko Mitrečić
- Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Mijana Kero
- Department of Medical Genetics, Children's Hospital Zagreb, Centre of Excellence for Reproductive and Regenerative Medicine, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Ljubica Odak
- Department of Medical Genetics, Children's Hospital Zagreb, Centre of Excellence for Reproductive and Regenerative Medicine, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Božo Krušlin
- Department of Pathology, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Željka Krsnik
- Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Ivica Kostović
- Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Jia Nee Foo
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; Laboratory of Neurogenetics, Genome Institute of Singapore, A∗STAR, Singapore
| | - Yuin-Han Loh
- Institute of Molecular and Cell Biology (IMCB), A∗STAR, Singapore
| | - Norris Ray Dunn
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; Institute of Molecular and Cell Biology (IMCB), A∗STAR, Singapore
| | - Susana de la Luna
- ICREA, Genome Biology Programme (CRG), Universitat Pompeu Fabra (UPF), CIBER of Rare Diseases, Barcelona, Spain
| | - Tim Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Ingeborg Barišić
- Department of Medical Genetics, Children's Hospital Zagreb, Centre of Excellence for Reproductive and Regenerative Medicine, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Michael S C Thomas
- The London Down Syndrome Consortium (LonDownS), London, UK; Centre for Brain and Cognitive Development, Birkbeck, University of London, London, UK
| | - Andre Strydom
- The London Down Syndrome Consortium (LonDownS), London, UK; Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Division of Psychiatry, University College London, London, UK
| | - Claudio Franceschi
- Department of Medical and Surgical Sciences, Alma Mater Studiorum University of Bologna, Italy; Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, Nizhny Novgorod 603022, Russia
| | - Gordan Lauc
- Glycoscience Research Laboratory, Genos Ltd., Zagreb, Croatia; Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, Croatia
| | | | - Ivan Alić
- Faculty of Medicine and Dentistry, Blizard Institute, Queen Mary University of London, London, UK; Faculty of Veterinary Medicine, Department of Anatomy, Histology and Embryology, University of Zagreb, Zagreb, Croatia.
| | - Dean Nižetić
- Faculty of Medicine and Dentistry, Blizard Institute, Queen Mary University of London, London, UK; The London Down Syndrome Consortium (LonDownS), London, UK; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore.
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24
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Villicaña S, Castillo-Fernandez J, Hannon E, Christiansen C, Tsai PC, Maddock J, Kuh D, Suderman M, Power C, Relton C, Ploubidis G, Wong A, Hardy R, Goodman A, Ong KK, Bell JT. Genetic impacts on DNA methylation help elucidate regulatory genomic processes. Genome Biol 2023; 24:176. [PMID: 37525248 PMCID: PMC10391992 DOI: 10.1186/s13059-023-03011-x] [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: 10/06/2022] [Accepted: 07/10/2023] [Indexed: 08/02/2023] Open
Abstract
BACKGROUND Pinpointing genetic impacts on DNA methylation can improve our understanding of pathways that underlie gene regulation and disease risk. RESULTS We report heritability and methylation quantitative trait locus (meQTL) analysis at 724,499 CpGs profiled with the Illumina Infinium MethylationEPIC array in 2358 blood samples from three UK cohorts. Methylation levels at 34.2% of CpGs are affected by SNPs, and 98% of effects are cis-acting or within 1 Mbp of the tested CpG. Our results are consistent with meQTL analyses based on the former Illumina Infinium HumanMethylation450 array. Both SNPs and CpGs with meQTLs are overrepresented in enhancers, which have improved coverage on this platform compared to previous approaches. Co-localisation analyses across genetic effects on DNA methylation and 56 human traits identify 1520 co-localisations across 1325 unique CpGs and 34 phenotypes, including in disease-relevant genes, such as USP1 and DOCK7 (total cholesterol levels), and ICOSLG (inflammatory bowel disease). Enrichment analysis of meQTLs and integration with expression QTLs give insights into mechanisms underlying cis-meQTLs (e.g. through disruption of transcription factor binding sites for CTCF and SMC3) and trans-meQTLs (e.g. through regulating the expression of ACD and SENP7 which can modulate DNA methylation at distal sites). CONCLUSIONS Our findings improve the characterisation of the mechanisms underlying DNA methylation variability and are informative for prioritisation of GWAS variants for functional follow-ups. The MeQTL EPIC Database and viewer are available online at https://epicmeqtl.kcl.ac.uk .
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Affiliation(s)
- Sergio Villicaña
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.
| | | | | | - Colette Christiansen
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Pei-Chien Tsai
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Jane Maddock
- MRC Unit for Lifelong Health and Ageing, Institute of Cardiovascular Science, University College London, London, UK
| | - Diana Kuh
- MRC Unit for Lifelong Health and Ageing, Institute of Cardiovascular Science, University College London, London, UK
| | - Matthew Suderman
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Christine Power
- Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Caroline Relton
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - George Ploubidis
- Centre for Longitudinal Studies, Institute of Education, University College London, London, UK
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing, Institute of Cardiovascular Science, University College London, London, UK
| | - Rebecca Hardy
- School of Sport, Exercise & Health Sciences, Loughborough University, Loughborough, UK
- UCL Social Research Institute, University College London, London, UK
| | - Alissa Goodman
- Centre for Longitudinal Studies, Institute of Education, University College London, London, UK
| | - Ken K Ong
- MRC Epidemiology Unit and Department of Paediatrics, Wellcome Trust-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.
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25
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Louca P, Štambuk T, Frkatović-Hodžić A, Nogal A, Mangino M, Berry SE, Deriš H, Hadjigeorgiou G, Wolf J, Vinicki M, Franks PW, Valdes AM, Spector TD, Lauc G, Menni C. Plasma protein N-glycome composition associates with postprandial lipaemic response. BMC Med 2023; 21:231. [PMID: 37400796 PMCID: PMC10318725 DOI: 10.1186/s12916-023-02938-z] [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: 01/27/2023] [Accepted: 06/13/2023] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND A dysregulated postprandial metabolic response is a risk factor for chronic diseases, including type 2 diabetes mellitus (T2DM). The plasma protein N-glycome is implicated in both lipid metabolism and T2DM risk. Hence, we first investigate the relationship between the N-glycome and postprandial metabolism and then explore the mediatory role of the plasma N-glycome in the relationship between postprandial lipaemia and T2DM. METHODS We included 995 individuals from the ZOE-PREDICT 1 study with plasma N-glycans measured by ultra-performance liquid chromatography at fasting and triglyceride, insulin, and glucose levels measured at fasting and following a mixed-meal challenge. Linear mixed models were used to investigate the associations between plasma protein N-glycosylation and metabolic response (fasting, postprandial (Cmax), or change from fasting). A mediation analysis was used to further explore the relationship of the N-glycome in the prediabetes (HbA1c = 39-47 mmol/mol (5.7-6.5%))-postprandial lipaemia association. RESULTS We identified 36 out of 55 glycans significantly associated with postprandial triglycerides (Cmax β ranging from -0.28 for low-branched glycans to 0.30 for GP26) after adjusting for covariates and multiple testing (padjusted < 0.05). N-glycome composition explained 12.6% of the variance in postprandial triglycerides not already explained by traditional risk factors. Twenty-seven glycans were also associated with postprandial glucose and 12 with postprandial insulin. Additionally, 3 of the postprandial triglyceride-associated glycans (GP9, GP11, and GP32) also correlate with prediabetes and partially mediate the relationship between prediabetes and postprandial triglycerides. CONCLUSIONS This study provides a comprehensive overview of the interconnections between plasma protein N-glycosylation and postprandial responses, demonstrating the incremental predictive benefit of N-glycans. We also suggest a considerable proportion of the effect of prediabetes on postprandial triglycerides is mediated by some plasma N-glycans.
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Affiliation(s)
- Panayiotis Louca
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Hospital Campus, Westminster Bridge Road, London, SE1 7EH, UK
| | | | | | - Ana Nogal
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Hospital Campus, Westminster Bridge Road, London, SE1 7EH, UK
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Hospital Campus, Westminster Bridge Road, London, SE1 7EH, UK
- NIHR Biomedical Research Centre at Guy's and St Thomas' Foundation Trust, London, SE1 9RT, UK
| | - Sarah E Berry
- Department of Nutritional Sciences, King's College London, Franklin Wilkins Building, London, SE1 9NH, UK
| | - Helena Deriš
- Genos Glycoscience Research Laboratory, Zagreb, Croatia
| | | | | | | | - Paul W Franks
- Lund University Diabetes Center, Lund University, Malmö, Sweden
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Ana M Valdes
- Academic Rheumatology Clinical Sciences Building, Nottingham City Hospital, University of Nottingham, Nottingham, UK
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Hospital Campus, Westminster Bridge Road, London, SE1 7EH, UK
| | - Gordan Lauc
- Genos Glycoscience Research Laboratory, Zagreb, Croatia
- University of Zagreb Faculty of Pharmacy and Biochemistry, Zagreb, Croatia
| | - Cristina Menni
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Hospital Campus, Westminster Bridge Road, London, SE1 7EH, UK.
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26
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Tsepilov YA, Elgaeva EE, Nostaeva AV, Compte R, Kuznetsov IA, Karssen LC, Freidin MB, Suri P, Williams FMK, Aulchenko YS. Development and Replication of a Genome-Wide Polygenic Risk Score for Chronic Back Pain. J Pers Med 2023; 13:977. [PMID: 37373966 DOI: 10.3390/jpm13060977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 05/25/2023] [Accepted: 06/05/2023] [Indexed: 06/29/2023] Open
Abstract
Chronic back pain (CBP) is a complex heritable trait and a major cause of disability worldwide. We developed and validated a genome-wide polygenic risk score (PRS) for CBP using a large-scale GWAS based on UK Biobank participants of European ancestry (N = 265,000). The PRS showed poor overall predictive ability (AUC = 0.56 and OR = 1.24 per SD, 95% CI: 1.22-1.26), but individuals from the 99th percentile of PRS distribution had a nearly two-fold increased risk of CBP (OR = 1.82, 95% CI: 1.60-2.06). We validated the PRS on an independent TwinsUK sample, obtaining a similar magnitude of effect. The PRS was significantly associated with various ICD-10 and OPCS-4 diagnostic codes, including chronic ischemic heart disease (OR = 1.1, p-value = 4.8 × 10-15), obesity, metabolism-related traits, spine disorders, disc degeneration, and arthritis-related disorders. PRS and environment interaction analysis with twelve known CBP risk factors revealed no significant results, suggesting that the magnitude of G × E interactions with studied factors is small. The limited predictive ability of the PRS that we developed is likely explained by the complexity, heterogeneity, and polygenicity of CBP, for which sample sizes of a few hundred thousand are insufficient to estimate small genetic effects robustly.
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Affiliation(s)
- Yakov A Tsepilov
- Laboratory of Recombination and Segregation Analysis, Institute of Cytology and Genetics, Novosibirsk 630090, Russia
- Kurchatov Genomics Center, Institute of Cytology and Genetics, Novosibirsk 630090, Russia
| | - Elizaveta E Elgaeva
- Laboratory of Recombination and Segregation Analysis, Institute of Cytology and Genetics, Novosibirsk 630090, Russia
- Department of Natural Sciences, Novosibirsk State University, Novosibirsk 630090, Russia
| | - Arina V Nostaeva
- Department of Natural Sciences, Novosibirsk State University, Novosibirsk 630090, Russia
| | - Roger Compte
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London SE1 7EH, UK
| | - Ivan A Kuznetsov
- Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow 121205, Russia
| | | | - Maxim B Freidin
- Department of Biology, School of Biological and Behavioural Sciences, Queen Mary University of London, London E1 4NS, UK
| | - Pradeep Suri
- Division of Rehabilitation Care Services, VA Puget Sound Health Care System, Seattle, WA 98208, USA
- Seattle Epidemiologic Research and Information Center, VA Puget Sound Health Care System, Seattle, WA 98208, USA
- Department of Rehabilitation Medicine, University of Washington, Seattle, WA 98208, USA
- Clinical Learning, Evidence, and Research (CLEAR) Center, University of Washington, Seattle, WA 98208, USA
| | - Frances M K Williams
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London SE1 7EH, UK
| | - Yurii S Aulchenko
- Laboratory of Recombination and Segregation Analysis, Institute of Cytology and Genetics, Novosibirsk 630090, Russia
- PolyOmica, 5237 PA s-Hertogenbosch, The Netherlands
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27
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McAllan L, Baranasic D, Villicaña S, Brown S, Zhang W, Lehne B, Adamo M, Jenkinson A, Elkalaawy M, Mohammadi B, Hashemi M, Fernandes N, Lambie N, Williams R, Christiansen C, Yang Y, Zudina L, Lagou V, Tan S, Castillo-Fernandez J, King JWD, Soong R, Elliott P, Scott J, Prokopenko I, Cebola I, Loh M, Lenhard B, Batterham RL, Bell JT, Chambers JC, Kooner JS, Scott WR. Integrative genomic analyses in adipocytes implicate DNA methylation in human obesity and diabetes. Nat Commun 2023; 14:2784. [PMID: 37188674 PMCID: PMC10185556 DOI: 10.1038/s41467-023-38439-z] [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: 10/13/2021] [Accepted: 05/03/2023] [Indexed: 05/17/2023] Open
Abstract
DNA methylation variations are prevalent in human obesity but evidence of a causative role in disease pathogenesis is limited. Here, we combine epigenome-wide association and integrative genomics to investigate the impact of adipocyte DNA methylation variations in human obesity. We discover extensive DNA methylation changes that are robustly associated with obesity (N = 190 samples, 691 loci in subcutaneous and 173 loci in visceral adipocytes, P < 1 × 10-7). We connect obesity-associated methylation variations to transcriptomic changes at >500 target genes, and identify putative methylation-transcription factor interactions. Through Mendelian Randomisation, we infer causal effects of methylation on obesity and obesity-induced metabolic disturbances at 59 independent loci. Targeted methylation sequencing, CRISPR-activation and gene silencing in adipocytes, further identifies regional methylation variations, underlying regulatory elements and novel cellular metabolic effects. Our results indicate DNA methylation is an important determinant of human obesity and its metabolic complications, and reveal mechanisms through which altered methylation may impact adipocyte functions.
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Affiliation(s)
- Liam McAllan
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, W12 0NN, UK
- MRC London Institute of Medical Sciences, London, W12 0NN, UK
| | - Damir Baranasic
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, W12 0NN, UK
- MRC London Institute of Medical Sciences, London, W12 0NN, UK
| | - Sergio Villicaña
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Scarlett Brown
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, W12 0NN, UK
- MRC London Institute of Medical Sciences, London, W12 0NN, UK
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, W2 1PG, UK
- Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, Middlesex, UB1 3HW, UK
| | - Benjamin Lehne
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, W2 1PG, UK
| | - Marco Adamo
- UCLH Bariatric Centre for Weight Loss, Weight Management and Metabolic and Endocrine Surgery, University College London Hospitals, Ground Floor West Wing, 250 Euston Road, London, NW1 2PG, UK
| | - Andrew Jenkinson
- UCLH Bariatric Centre for Weight Loss, Weight Management and Metabolic and Endocrine Surgery, University College London Hospitals, Ground Floor West Wing, 250 Euston Road, London, NW1 2PG, UK
| | - Mohamed Elkalaawy
- UCLH Bariatric Centre for Weight Loss, Weight Management and Metabolic and Endocrine Surgery, University College London Hospitals, Ground Floor West Wing, 250 Euston Road, London, NW1 2PG, UK
| | - Borzoueh Mohammadi
- UCLH Bariatric Centre for Weight Loss, Weight Management and Metabolic and Endocrine Surgery, University College London Hospitals, Ground Floor West Wing, 250 Euston Road, London, NW1 2PG, UK
| | - Majid Hashemi
- UCLH Bariatric Centre for Weight Loss, Weight Management and Metabolic and Endocrine Surgery, University College London Hospitals, Ground Floor West Wing, 250 Euston Road, London, NW1 2PG, UK
| | - Nadia Fernandes
- Imperial BRC Genomics Facility, Faculty of Medicine, Imperial College London, London, W12 0NN, UK
| | - Nathalie Lambie
- Imperial BRC Genomics Facility, Faculty of Medicine, Imperial College London, London, W12 0NN, UK
| | - Richard Williams
- Imperial BRC Genomics Facility, Faculty of Medicine, Imperial College London, London, W12 0NN, UK
| | - Colette Christiansen
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- School of Mathematics and Statistics, Faculty of Science, Technology, Engineering and Mathematics, The Open University, Milton Keynes, UK
| | - Youwen Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, W2 1PG, UK
- School of Cardiovascular and Metabolic Medicine and Sciences, James Black Centre, King's College London British Heart Foundation Centre of Excellence, 125 Coldharbour Lane, London, SE5 9NU, UK
| | - Liudmila Zudina
- Department of Clinical & Experimental Medicine, University of Surrey, Guildford, UK
| | - Vasiliki Lagou
- Department of Microbiology and Immunology, Laboratory of Adaptive Immunity, KU Leuven, Leuven, Belgium
- VIB-KU Leuven Center for Brain and Disease Research, Leuven, Belgium
| | - Sili Tan
- Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore
| | | | - James W D King
- MRC London Institute of Medical Sciences, London, W12 0NN, UK
| | - Richie Soong
- Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore
- Department of Pathology, National University Hospital, Singapore, Singapore
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, W2 1PG, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- National Institute for Health Research Biomedical Research Centre, Imperial College London, London, UK
| | - James Scott
- National Heart and Lung Institute, Imperial College London, London, W12 0NN, UK
- Imperial College Healthcare NHS Trust, London, W12 0HS, UK
| | - Inga Prokopenko
- Department of Clinical & Experimental Medicine, University of Surrey, Guildford, UK
- People-Centred Artificial Intelligence Institute, University of Surrey, Guildford, UK
- Institute of Biochemistry and Genetics, Ufa Federal Research Centre Russian Academy of Sciences, Ufa, Russian Federation
| | - Inês Cebola
- Section of Genetics and Genomics, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Marie Loh
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, W2 1PG, UK
- Translational Laboratory in Genetic Medicine (TLGM), Agency for Science, Technology and Research (A*STAR), 8A Biomedical Grove, Immunos, Level 5, Singapore, 138648, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Boris Lenhard
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, W12 0NN, UK
- MRC London Institute of Medical Sciences, London, W12 0NN, UK
| | - Rachel L Batterham
- UCLH Bariatric Centre for Weight Loss, Weight Management and Metabolic and Endocrine Surgery, University College London Hospitals, Ground Floor West Wing, 250 Euston Road, London, NW1 2PG, UK
- Centre for Obesity Research, Rayne Institute, Department of Medicine, University College, London, WC1E 6JJ, UK
- National Institute of Health Research University College London Hospitals Biomedical Research Centre, London, W1T 7DN, UK
| | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - John C Chambers
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, W2 1PG, UK
- Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, Middlesex, UB1 3HW, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, London, W12 0HS, UK
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Jaspal S Kooner
- Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, Middlesex, UB1 3HW, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, W12 0NN, UK
- Imperial College Healthcare NHS Trust, London, W12 0HS, UK
| | - William R Scott
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, W12 0NN, UK.
- MRC London Institute of Medical Sciences, London, W12 0NN, UK.
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, W2 1PG, UK.
- Imperial College Healthcare NHS Trust, London, W12 0HS, UK.
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28
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Freidin MB, Cheetham N, Duncan EL, Steves CJ, Doores KJ, Malim MH, Rossi N, Lord JM, Franks PW, Borsini A, Granville Smith I, Falchi M, Pariante C, Williams FMK. Long-COVID fatigue is not predicted by pre-pandemic plasma IL-6 levels in mild COVID-19. Inflamm Res 2023; 72:947-953. [PMID: 36995412 PMCID: PMC10062244 DOI: 10.1007/s00011-023-01722-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 03/13/2023] [Accepted: 03/20/2023] [Indexed: 03/31/2023] Open
Abstract
OBJECTIVE AND DESIGN Fatigue is a prominent symptom in the general population and may follow viral infection, including SARS-CoV2 infection which causes COVID-19. Chronic fatigue lasting more than three months is the major symptom of the post-COVID syndrome (known colloquially as long-COVID). The mechanisms underlying long-COVID fatigue are unknown. We hypothesized that the development of long-COVID chronic fatigue is driven by the pro-inflammatory immune status of an individual prior to COVID-19. SUBJECTS AND METHODS We analyzed pre-pandemic plasma levels of IL-6, which plays a key role in persistent fatigue, in N = 1274 community dwelling adults from TwinsUK. Subsequent COVID-19-positive and -negative participants were categorized based on SARS-CoV-2 antigen and antibody testing. Chronic fatigue was assessed using the Chalder Fatigue Scale. RESULTS COVID-19-positive participants exhibited mild disease. Chronic fatigue was a prevalent symptom among this population and significantly higher in positive vs. negative participants (17% vs 11%, respectively; p = 0.001). The qualitative nature of chronic fatigue as determined by individual questionnaire responses was similar in positive and negative participants. Pre-pandemic plasma IL-6 levels were positively associated with chronic fatigue in negative, but not positive individuals. Raised BMI was associated with chronic fatigue in positive participants. CONCLUSIONS Pre-existing increased IL-6 levels may contribute to chronic fatigue symptoms, but there was no increased risk in individuals with mild COVID-19 compared with uninfected individuals. Elevated BMI also increased the risk of chronic fatigue in mild COVID-19, consistent with previous reports.
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Affiliation(s)
- Maxim B Freidin
- Department of Twin Research and Genetic Epidemiology, School of Life Course and Population Sciences, King's College London, London, UK.
- Department of Biology, School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK.
| | - Nathan Cheetham
- Department of Twin Research and Genetic Epidemiology, School of Life Course and Population Sciences, King's College London, London, UK
| | - Emma L Duncan
- Department of Twin Research and Genetic Epidemiology, School of Life Course and Population Sciences, King's College London, London, UK
| | - Claire J Steves
- Department of Twin Research and Genetic Epidemiology, School of Life Course and Population Sciences, King's College London, London, UK
| | - Katherine J Doores
- Department of Infectious Diseases, School of Immunology & Microbial Sciences, King's College London, London, UK
| | - Michael H Malim
- Department of Infectious Diseases, School of Immunology & Microbial Sciences, King's College London, London, UK
| | - Niccolo Rossi
- Department of Twin Research and Genetic Epidemiology, School of Life Course and Population Sciences, King's College London, London, UK
| | - Janet M Lord
- MRC-Versus Arthritis Centre for Musculoskeletal Ageing Research, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospital Birmingham and University of Birmingham, Birmingham, UK
| | - Paul W Franks
- Lund University Diabetes Center, Lund University, Malmö, Sweden
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Alessandra Borsini
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Isabelle Granville Smith
- Department of Twin Research and Genetic Epidemiology, School of Life Course and Population Sciences, King's College London, London, UK
| | - Mario Falchi
- Department of Twin Research and Genetic Epidemiology, School of Life Course and Population Sciences, King's College London, London, UK
| | - Carmine Pariante
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Frances M K Williams
- Department of Twin Research and Genetic Epidemiology, School of Life Course and Population Sciences, King's College London, London, UK
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29
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Louca P, Meijnikman AS, Nogal A, Asnicar F, Attaye I, Vijay A, Kouraki A, Visconti A, Wong K, Berry SE, Leeming ER, Mompeo O, Tettamanzi F, Baleanu AF, Falchi M, Hadjigeorgiou G, Wolf J, Acherman YIZ, Van de Laar AW, Gerdes VEA, Michelotti GA, Franks PW, Segata N, Mangino M, Spector TD, Bulsiewicz WJ, Nieuwdorp M, Valdes AM, Menni C. The secondary bile acid isoursodeoxycholate correlates with post-prandial lipemia, inflammation, and appetite and changes post-bariatric surgery. Cell Rep Med 2023; 4:100993. [PMID: 37023745 PMCID: PMC10140478 DOI: 10.1016/j.xcrm.2023.100993] [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: 08/11/2022] [Revised: 10/12/2022] [Accepted: 03/14/2023] [Indexed: 04/08/2023]
Abstract
Primary and secondary bile acids (BAs) influence metabolism and inflammation, and the gut microbiome modulates levels of BAs. We systematically explore the host genetic, gut microbial, and habitual dietary contribution to a panel of 19 serum and 15 stool BAs in two population-based cohorts (TwinsUK, n = 2,382; ZOE PREDICT-1, n = 327) and assess changes post-bariatric surgery and after nutritional interventions. We report that BAs have a moderately heritable genetic component, and the gut microbiome accurately predicts their levels in serum and stool. The secondary BA isoursodeoxycholate (isoUDCA) can be explained mostly by gut microbes (area under the receiver operating characteristic curve [AUC] = ∼80%) and associates with post-prandial lipemia and inflammation (GlycA). Furthermore, circulating isoUDCA decreases significantly 1 year after bariatric surgery (β = -0.72, p = 1 × 10-5) and in response to fiber supplementation (β = -0.37, p < 0.03) but not omega-3 supplementation. In healthy individuals, isoUDCA fasting levels correlate with pre-meal appetite (p < 1 × 10-4). Our findings indicate an important role for isoUDCA in lipid metabolism, appetite, and, potentially, cardiometabolic risk.
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Affiliation(s)
- Panayiotis Louca
- Department of Twin Research & Genetic Epidemiology, King's College London, SE1 7EH London, UK
| | - Abraham S Meijnikman
- Department of (Experimental) Vascular Medicine, Amsterdam University Medical Centre (UMC), Amsterdam, the Netherlands
| | - Ana Nogal
- Department of Twin Research & Genetic Epidemiology, King's College London, SE1 7EH London, UK
| | | | - Ilias Attaye
- Department of (Experimental) Vascular Medicine, Amsterdam University Medical Centre (UMC), Amsterdam, the Netherlands
| | - Amrita Vijay
- Nottingham NIHR Biomedical Research Centre at the School of Medicine, University of Nottingham, NG5 1PB Nottingham, UK; Inflammation, Recovery and Injury Sciences, School of Medicine, University of Nottingham, NG5 1PB Nottingham, UK
| | - Afroditi Kouraki
- Nottingham NIHR Biomedical Research Centre at the School of Medicine, University of Nottingham, NG5 1PB Nottingham, UK; Inflammation, Recovery and Injury Sciences, School of Medicine, University of Nottingham, NG5 1PB Nottingham, UK
| | - Alessia Visconti
- Department of Twin Research & Genetic Epidemiology, King's College London, SE1 7EH London, UK
| | - Kari Wong
- Metabolon, Research Triangle Park, Morrisville, NC, USA
| | - Sarah E Berry
- Department of Nutritional Sciences, King's College London, London, UK
| | - Emily R Leeming
- Department of Twin Research & Genetic Epidemiology, King's College London, SE1 7EH London, UK
| | - Olatz Mompeo
- Department of Twin Research & Genetic Epidemiology, King's College London, SE1 7EH London, UK
| | - Francesca Tettamanzi
- Department of Twin Research & Genetic Epidemiology, King's College London, SE1 7EH London, UK
| | - Andrei-Florin Baleanu
- Department of Twin Research & Genetic Epidemiology, King's College London, SE1 7EH London, UK
| | - Mario Falchi
- Department of Twin Research & Genetic Epidemiology, King's College London, SE1 7EH London, UK
| | | | | | | | | | - Victor E A Gerdes
- Department of (Experimental) Vascular Medicine, Amsterdam University Medical Centre (UMC), Amsterdam, the Netherlands
| | | | - Paul W Franks
- Lund University Diabetes Center, Lund University, Malmö, Sweden; Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Nicola Segata
- Department CIBIO, University of Trento, Trento, Italy
| | - Massimo Mangino
- Department of Twin Research & Genetic Epidemiology, King's College London, SE1 7EH London, UK; NIHR Biomedical Research Centre at Guy's and St Thomas' Foundation Trust, SE1 9RT London, UK
| | - Tim D Spector
- Department of Twin Research & Genetic Epidemiology, King's College London, SE1 7EH London, UK
| | | | - Max Nieuwdorp
- Department of (Experimental) Vascular Medicine, Amsterdam University Medical Centre (UMC), Amsterdam, the Netherlands
| | - Ana M Valdes
- Nottingham NIHR Biomedical Research Centre at the School of Medicine, University of Nottingham, NG5 1PB Nottingham, UK; Inflammation, Recovery and Injury Sciences, School of Medicine, University of Nottingham, NG5 1PB Nottingham, UK.
| | - Cristina Menni
- Department of Twin Research & Genetic Epidemiology, King's College London, SE1 7EH London, UK.
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30
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Li Y, Xu Y, Le Roy C, Hu J, Steves CJ, Bell JT, Spector TD, Gibson R, Menni C, Rodriguez-Mateos A. Interplay between the (Poly)phenol Metabolome, Gut Microbiome, and Cardiovascular Health in Women: A Cross-Sectional Study from the TwinsUK Cohort. Nutrients 2023; 15:1900. [PMID: 37111123 PMCID: PMC10141398 DOI: 10.3390/nu15081900] [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: 03/24/2023] [Revised: 04/11/2023] [Accepted: 04/13/2023] [Indexed: 04/29/2023] Open
Abstract
BACKGROUND Dietary (poly)phenol consumption is inversely associated with cardiovascular disease (CVD) risk in epidemiological studies, but little is known about the role of the gut microbiome in this relationship. METHODS In 200 healthy females, aged 62.0 ± 10.0 years, from the TwinsUK cohort, 114 individual (poly)phenol metabolites were measured from spot urine using ultra-high-performance liquid chromatography-mass spectrometry. The associations between metabolites, the gut microbiome (alpha diversity and genera), and cardiovascular scores were investigated using linear mixed models adjusting age, BMI, fibre, energy intake, family relatedness, and multiple testing (FDR < 0.1). RESULTS Significant associations were found between phenolic acid metabolites, CVD risk, and the gut microbiome. A total of 35 phenolic acid metabolites were associated with the Firmicutes phylum, while 5 metabolites were associated with alpha diversity (FDR-adjusted p < 0.05). Negative associations were observed between the atherosclerotic CVD (ASCVD) risk score and five phenolic acid metabolites, two tyrosol metabolites, and daidzein with stdBeta (95% (CI)) ranging from -0.05 (-0.09, -0.01) for 3-(2,4-dihydroxyphenyl)propanoic acid to -0.04 (-0.08, -0.003) for 2-hydroxycinnamic acid (FDR-adjusted p < 0.1). The genus 5-7N15 in the Bacteroidetes phylum was positively associated with the same metabolites, including 3-(3,5-dihydroxyphenyl)propanoic acid, 3-(2,4-dihydroxyphenyl)propanoic acid, 3-(3,4-dihydroxyphenyl)propanoic acid), 3-hydroxyphenylethanol-4-sulfate, and 4-hydroxyphenylethanol-3-sulfate)(stdBeta (95% CI): 0.23 (0.09, 0.36) to 0.28 (0.15, 0.42), FDR-adjusted p < 0.05), and negatively associated with the ASCVD score (stdBeta (95% CI): -0.05 (-0.09, -0.01), FDR-adjusted p = 0.02). Mediation analysis showed that genus 5-7N15 mediated 23.8% of the total effect of 3-(3,4-dihydroxyphenyl)propanoic acid on the ASCVD score. CONCLUSIONS Coffee, tea, red wine, and several vegetables and fruits, especially berries, are the most abundant food sources of phenolic acids that have the strongest associations with CVD risk. We found that the gut microbiome, particularly the genus 5-7N15, partially mediates the negative association between urinary (poly)phenols and cardiovascular risk, supporting a key role of the gut microbiome in the health benefits of dietary (poly)phenols.
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Affiliation(s)
- Yong Li
- Department of Nutritional Sciences, School of Life Course and Population Sciences, Faculty of Life Sciences and Medicine, King’s College London, London WC2R 2LS, UK
| | - Yifan Xu
- Department of Nutritional Sciences, School of Life Course and Population Sciences, Faculty of Life Sciences and Medicine, King’s College London, London WC2R 2LS, UK
| | - Caroline Le Roy
- Department of Twin Research and Genetic Epidemiology, School of Life Course and Population Sciences, Faculty of Life Sciences and Medicine, King’s College London, London WC2R 2LS, UK
| | - Jiaying Hu
- Department of Nutritional Sciences, School of Life Course and Population Sciences, Faculty of Life Sciences and Medicine, King’s College London, London WC2R 2LS, UK
| | - Claire J. Steves
- Department of Twin Research and Genetic Epidemiology, School of Life Course and Population Sciences, Faculty of Life Sciences and Medicine, King’s College London, London WC2R 2LS, UK
| | - Jordana T. Bell
- Department of Twin Research and Genetic Epidemiology, School of Life Course and Population Sciences, Faculty of Life Sciences and Medicine, King’s College London, London WC2R 2LS, UK
| | - Tim D. Spector
- Department of Twin Research and Genetic Epidemiology, School of Life Course and Population Sciences, Faculty of Life Sciences and Medicine, King’s College London, London WC2R 2LS, UK
| | - Rachel Gibson
- Department of Nutritional Sciences, School of Life Course and Population Sciences, Faculty of Life Sciences and Medicine, King’s College London, London WC2R 2LS, UK
| | - Cristina Menni
- Department of Twin Research and Genetic Epidemiology, School of Life Course and Population Sciences, Faculty of Life Sciences and Medicine, King’s College London, London WC2R 2LS, UK
| | - Ana Rodriguez-Mateos
- Department of Nutritional Sciences, School of Life Course and Population Sciences, Faculty of Life Sciences and Medicine, King’s College London, London WC2R 2LS, UK
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31
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Rockweiler NB, Ramu A, Nagirnaja L, Wong WH, Noordam MJ, Drubin CW, Huang N, Miller B, Todres EZ, Vigh-Conrad KA, Zito A, Small KS, Ardlie KG, Cohen BA, Conrad DF. The origins and functional effects of postzygotic mutations throughout the human life span. Science 2023; 380:eabn7113. [PMID: 37053313 PMCID: PMC11246725 DOI: 10.1126/science.abn7113] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 03/17/2023] [Indexed: 04/15/2023]
Abstract
Postzygotic mutations (PZMs) begin to accrue in the human genome immediately after fertilization, but how and when PZMs affect development and lifetime health remain unclear. To study the origins and functional consequences of PZMs, we generated a multitissue atlas of PZMs spanning 54 tissue and cell types from 948 donors. Nearly half the variation in mutation burden among tissue samples can be explained by measured technical and biological effects, and 9% can be attributed to donor-specific effects. Through phylogenetic reconstruction of PZMs, we found that their type and predicted functional impact vary during prenatal development, across tissues, and through the germ cell life cycle. Thus, methods for interpreting effects across the body and the life span are needed to fully understand the consequences of genetic variants.
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Affiliation(s)
- Nicole B. Rockweiler
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Present address: Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA; Department of Genetics, Harvard Medical School, Boston, MA, 02115, USA
| | - Avinash Ramu
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Liina Nagirnaja
- Division of Genetics, Oregon National Primate Research Center, Oregon Health & Science University, Beaverton, OR, 97006, USA
| | - Wing H. Wong
- Department of Pediatrics, Division of Hematology and Oncology, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Present Address: Departments of Genetics and Medicine, Stanford University, CA 94305, USA
| | - Michiel J. Noordam
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Casey W. Drubin
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Ni Huang
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Present Address: T-Therapeutics Ltd., Cambridge CB21 6AD, UK
| | - Brian Miller
- Division of Genetics, Oregon National Primate Research Center, Oregon Health & Science University, Beaverton, OR, 97006, USA
| | - Ellen Z. Todres
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Katinka A. Vigh-Conrad
- Division of Genetics, Oregon National Primate Research Center, Oregon Health & Science University, Beaverton, OR, 97006, USA
| | - Antonino Zito
- Department of Twin Research and Genetic Epidemiology, King’s College London, London SE1 7EH, UK
- Present Address: Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, 02114, USA; Department of Genetics, The Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
| | - Kerrin S. Small
- Department of Twin Research and Genetic Epidemiology, King’s College London, London SE1 7EH, UK
| | | | - Barak A. Cohen
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Donald F. Conrad
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Division of Genetics, Oregon National Primate Research Center, Oregon Health & Science University, Beaverton, OR, 97006, USA
- Center for Embryonic Cell & Gene Therapy, Oregon Health & Science University, Portland, OR, 97239, USA
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Keehn L, Mangino M, Spector T, Chowienczyk P, Cecelja M. Relation of Pulse Wave Velocity to Contemporaneous and Historical Blood Pressure in Female Twins. Hypertension 2023; 80:361-369. [PMID: 36408690 PMCID: PMC9847690 DOI: 10.1161/hypertensionaha.122.19311] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
BACKGROUND An association between blood pressure and aortic stiffness is well known, but ambiguity remains as to whether one precedes the other. This study aimed to investigate the association of aortic stiffness with contemporaneous versus historic blood pressure and direction of causality between aortic stiffening and hypertension in female twins. METHODS Aortic stiffness, measured by carotid-femoral pulse wave velocity (PWV), and mean arterial pressure (MAP) was recorded in 2037 female TwinsUK participants (mean age: 62.4±9.7 years) at a single time point. A subset of 947 participants had repeat PWV and MAP measures (mean interval 5.5±1.7 years) with additional historic MAP (mean interval 6.6±3.3 years before baseline). RESULTS Cross-sectional multivariable linear regression analysis confirmed PWV significantly associated with age and MAP. In longitudinal analysis, annual progression of PWV was not associated with historic MAP (standardized beta coefficient [β]=-0.02, P=0.698), weakly associated with baseline MAP (β=0.09, P=0.049) but strongly associated with progression (from baseline to most recent measurement) of MAP (β= 0.26, P<0.001). Progression of MAP associated with both baseline and progression of PWV (β=0.13, P=0.003 and β=0.24, P<0.001, respectively). CONCLUSIONS Progression of aortic stiffness associates more strongly with contemporaneous MAP compared with historic MAP. In contrast, progression of MAP is associated with prior arterial stiffness. These findings suggest a bidirectional relationship between arterial stiffness and blood pressure, and that lowering blood pressure may prevent a cycle of arterial stiffening and hypertension.
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Affiliation(s)
- Louise Keehn
- Department of Clinical Pharmacology, King’s College London British Heart Foundation Centre, St Thomas’ Hospital (L.K., P.C., M.C.)
| | - Massimo Mangino
- NIHR Biomedical Research Centre at Guy’s and St Thomas’ Foundation Trust, London (M.M.)
- Department of Twin Research and Genetic Epidemiology, King’s College London, St Thomas Hospital (M.M., T.S.)
| | - Tim Spector
- Department of Twin Research and Genetic Epidemiology, King’s College London, St Thomas Hospital (M.M., T.S.)
| | - Phil Chowienczyk
- Department of Clinical Pharmacology, King’s College London British Heart Foundation Centre, St Thomas’ Hospital (L.K., P.C., M.C.)
| | - Marina Cecelja
- Department of Clinical Pharmacology, King’s College London British Heart Foundation Centre, St Thomas’ Hospital (L.K., P.C., M.C.)
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Zhang X, Adebayo AS, Wang D, Raza Y, Tomlinson M, Dooley H, Bowyer RC, Small KS, Steves CJ, Spector TD, Duncan EL, Visconti A, Falchi M. PPI-Induced Changes in Plasma Metabolite Levels Influence Total Hip Bone Mineral Density in a UK Cohort. J Bone Miner Res 2023; 38:326-334. [PMID: 36458982 PMCID: PMC10108201 DOI: 10.1002/jbmr.4754] [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: 07/22/2022] [Revised: 11/08/2022] [Accepted: 11/26/2022] [Indexed: 12/05/2022]
Abstract
Proton pump inhibitors (PPIs) are among the most used drugs in the UK. PPI use has been associated with decreased bone mineral density (BMD) and increased fracture risk, although these results have been inconsistent. We hypothesized that PPI could modulate BMD by altering gut and/or host systemic metabolic environments. Using data from more than 5000 British male and female individuals, we confirmed that PPI use is associated with decreased lumbar spine and total hip BMD. This effect was not mediated through the gut microbiome. We suggest here that PPI use may influence total hip BMD, both directly and indirectly, via plasma metabolites involved in the sex hormone pathway. © 2022 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).
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Affiliation(s)
- Xinyuan Zhang
- Department of Twins Research & Genetics EpidemiologyKing's College LondonLondonUK
| | - Adewale S. Adebayo
- Department of Twins Research & Genetics EpidemiologyKing's College LondonLondonUK
- Present address:
NIHR Leicester Biomedical Research Centre, Department of Cardiovascular SciencesUniversity of LeicesterLeicesterUK
| | - Dongmeng Wang
- Department of Twins Research & Genetics EpidemiologyKing's College LondonLondonUK
| | - Yasrab Raza
- Department of Twins Research & Genetics EpidemiologyKing's College LondonLondonUK
| | - Max Tomlinson
- Department of Twins Research & Genetics EpidemiologyKing's College LondonLondonUK
| | - Hannah Dooley
- Department of Twins Research & Genetics EpidemiologyKing's College LondonLondonUK
| | - Ruth C.E. Bowyer
- Department of Twins Research & Genetics EpidemiologyKing's College LondonLondonUK
| | - Kerrin S. Small
- Department of Twins Research & Genetics EpidemiologyKing's College LondonLondonUK
| | - Claire J. Steves
- Department of Twins Research & Genetics EpidemiologyKing's College LondonLondonUK
| | - Tim D. Spector
- Department of Twins Research & Genetics EpidemiologyKing's College LondonLondonUK
| | - Emma L. Duncan
- Department of Twins Research & Genetics EpidemiologyKing's College LondonLondonUK
| | - Alessia Visconti
- Department of Twins Research & Genetics EpidemiologyKing's College LondonLondonUK
| | - Mario Falchi
- Department of Twins Research & Genetics EpidemiologyKing's College LondonLondonUK
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Bowyer RCE, Huggins C, Toms R, Shaw RJ, Hou B, Thompson EJ, Kwong ASF, Williams DM, Kibble M, Ploubidis GB, Timpson NJ, Sterne JAC, Chaturvedi N, Steves CJ, Tilling K, Silverwood RJ. Characterising patterns of COVID-19 and long COVID symptoms: evidence from nine UK longitudinal studies. Eur J Epidemiol 2023; 38:199-210. [PMID: 36680646 PMCID: PMC9860244 DOI: 10.1007/s10654-022-00962-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 12/26/2022] [Indexed: 01/22/2023]
Abstract
Multiple studies across global populations have established the primary symptoms characterising Coronavirus Disease 2019 (COVID-19) and long COVID. However, as symptoms may also occur in the absence of COVID-19, a lack of appropriate controls has often meant that specificity of symptoms to acute COVID-19 or long COVID, and the extent and length of time for which they are elevated after COVID-19, could not be examined. We analysed individual symptom prevalences and characterised patterns of COVID-19 and long COVID symptoms across nine UK longitudinal studies, totalling over 42,000 participants. Conducting latent class analyses separately in three groups ('no COVID-19', 'COVID-19 in last 12 weeks', 'COVID-19 > 12 weeks ago'), the data did not support the presence of more than two distinct symptom patterns, representing high and low symptom burden, in each group. Comparing the high symptom burden classes between the 'COVID-19 in last 12 weeks' and 'no COVID-19' groups we identified symptoms characteristic of acute COVID-19, including loss of taste and smell, fatigue, cough, shortness of breath and muscle pains or aches. Comparing the high symptom burden classes between the 'COVID-19 > 12 weeks ago' and 'no COVID-19' groups we identified symptoms characteristic of long COVID, including fatigue, shortness of breath, muscle pain or aches, difficulty concentrating and chest tightness. The identified symptom patterns among individuals with COVID-19 > 12 weeks ago were strongly associated with self-reported length of time unable to function as normal due to COVID-19 symptoms, suggesting that the symptom pattern identified corresponds to long COVID. Building the evidence base regarding typical long COVID symptoms will improve diagnosis of this condition and the ability to elicit underlying biological mechanisms, leading to better patient access to treatment and services.
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Affiliation(s)
- Ruth C E Bowyer
- Department of Twin Research and Genetic Epidemiology, School of Life Course and Population Sciences, King's College London, London, UK
- The Alan Turing Institute, London, UK
| | - Charlotte Huggins
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Renin Toms
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Population Wellbeing, School of Health Sciences, Cardiff Metropolitan University, Cardiff, UK
| | - Richard J Shaw
- MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow, UK
| | - Bo Hou
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Ellen J Thompson
- Department of Twin Research and Genetic Epidemiology, School of Life Course and Population Sciences, King's College London, London, UK
| | - Alex S F Kwong
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Dylan M Williams
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Milla Kibble
- Department of Twin Research and Genetic Epidemiology, School of Life Course and Population Sciences, King's College London, London, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - George B Ploubidis
- Centre for Longitudinal Studies, UCL Social Research Institute, University College London, London, UK
| | - Nicholas J Timpson
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
| | - Jonathan A C Sterne
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
- Health Data Research UK South West, Bristol, UK
| | - Nishi Chaturvedi
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
| | - Claire J Steves
- Department of Twin Research and Genetic Epidemiology, School of Life Course and Population Sciences, King's College London, London, UK
- Department of Ageing and Health, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Kate Tilling
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
| | - Richard J Silverwood
- Centre for Longitudinal Studies, UCL Social Research Institute, University College London, London, UK.
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Kurushima Y, Wells P, Bowyer R, Zoheir N, Doran S, Richardson J, Sprockett D, Relman D, Steves C, Nibali L. Host Genotype Links to Salivary and Gut Microbiota by Periodontal Status. J Dent Res 2023; 102:146-156. [PMID: 36214094 PMCID: PMC9986680 DOI: 10.1177/00220345221125402] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
Limited evidence describing how host genetic variants affect the composition of the microbiota is currently available. The aim of this study was to assess the associations between a set of candidate host genetic variants and microbial composition in both saliva and gut in the TwinsUK registry. A total of 1,746 participants were included in this study and provided stool samples. A subset of 1,018 participants also provided self-reported periodontal data, and 396 of those participants provided a saliva sample. Host DNA was extracted from whole-blood samples and processed for Infinium Global screening array, focusing on 37 selected single-nucleotide polymorphisms (SNPs) previously associated with periodontitis. The gut and salivary microbiota of participants were profiled using 16S ribosomal RNA amplicon sequencing. Associations between genotype on the selected SNPs and microbial outcomes, including α diversity, β diversity, and amplicon sequence variants (ASVs), were investigated in a multivariate mixed model. Self-reported periodontal status was also compared with microbial outcomes. Downstream analyses in gut microbiota and salivary microbiota were carried out separately. IL10 rs6667202 and VDR 2228570 SNPs were associated with salivary α diversity, and SNPs in IL10, HSA21, UHRF2, and Fc-γR genes were associated with dissimilarity matrix generated from salivary β diversity. The SNP that was associated with the greatest number of salivary ASVs was VDR 2228570 followed by IL10 rs6667202, and that of gut ASVs was NPY rs2521364. There were 77 salivary ASVs and 39 gut ASVs differentially abundant in self-reported periodontal disease versus periodontal health. The dissimilarity between saliva and gut microbiota within individuals appeared significantly greater in self-reported periodontal cases compared to periodontal health. IL10 and VDR gene variants may affect salivary microbiota composition. Periodontal status may drive variations in the salivary microbiota and possibly, to a lesser extent, in the gut microbiota.
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Affiliation(s)
- Y. Kurushima
- Periodontology Unit, Centre for Host Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, UK
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King’s College London, London, UK
| | - P.M. Wells
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King’s College London, London, UK
| | - R.C.E. Bowyer
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King’s College London, London, UK
| | - N. Zoheir
- Periodontology Unit, Centre for Host Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, UK
| | - S. Doran
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, UK
| | - J.P. Richardson
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, UK
| | - D.D. Sprockett
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA
| | - D.A. Relman
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - C.J. Steves
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King’s College London, London, UK
| | - L. Nibali
- Periodontology Unit, Centre for Host Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, UK
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Cheetham NJ, Kibble M, Wong A, Silverwood RJ, Knuppel A, Williams DM, Hamilton OKL, Lee PH, Bridger Staatz C, Di Gessa G, Zhu J, Katikireddi SV, Ploubidis GB, Thompson EJ, Bowyer RCE, Zhang X, Abbasian G, Garcia MP, Hart D, Seow J, Graham C, Kouphou N, Acors S, Malim MH, Mitchell RE, Northstone K, Major-Smith D, Matthews S, Breeze T, Crawford M, Molloy L, Kwong ASF, Doores K, Chaturvedi N, Duncan EL, Timpson NJ, Steves CJ. Antibody levels following vaccination against SARS-CoV-2: associations with post-vaccination infection and risk factors in two UK longitudinal studies. eLife 2023; 12:e80428. [PMID: 36692910 PMCID: PMC9940912 DOI: 10.7554/elife.80428] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 12/22/2022] [Indexed: 01/25/2023] Open
Abstract
Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody levels can be used to assess humoral immune responses following SARS-CoV-2 infection or vaccination, and may predict risk of future infection. Higher levels of SARS-CoV-2 anti-Spike antibodies are known to be associated with increased protection against future SARS-CoV-2 infection. However, variation in antibody levels and risk factors for lower antibody levels following each round of SARS-CoV-2 vaccination have not been explored across a wide range of socio-demographic, SARS-CoV-2 infection and vaccination, and health factors within population-based cohorts. Methods Samples were collected from 9361 individuals from TwinsUK and ALSPAC UK population-based longitudinal studies and tested for SARS-CoV-2 antibodies. Cross-sectional sampling was undertaken jointly in April-May 2021 (TwinsUK, N=4256; ALSPAC, N=4622), and in TwinsUK only in November 2021-January 2022 (N=3575). Variation in antibody levels after first, second, and third SARS-CoV-2 vaccination with health, socio-demographic, SARS-CoV-2 infection, and SARS-CoV-2 vaccination variables were analysed. Using multivariable logistic regression models, we tested associations between antibody levels following vaccination and: (1) SARS-CoV-2 infection following vaccination(s); (2) health, socio-demographic, SARS-CoV-2 infection, and SARS-CoV-2 vaccination variables. Results Within TwinsUK, single-vaccinated individuals with the lowest 20% of anti-Spike antibody levels at initial testing had threefold greater odds of SARS-CoV-2 infection over the next 6-9 months (OR = 2.9, 95% CI: 1.4, 6.0), compared to the top 20%. In TwinsUK and ALSPAC, individuals identified as at increased risk of COVID-19 complication through the UK 'Shielded Patient List' had consistently greater odds (two- to fourfold) of having antibody levels in the lowest 10%. Third vaccination increased absolute antibody levels for almost all individuals, and reduced relative disparities compared with earlier vaccinations. Conclusions These findings quantify the association between antibody level and risk of subsequent infection, and support a policy of triple vaccination for the generation of protective antibodies. Funding Antibody testing was funded by UK Health Security Agency. The National Core Studies program is funded by COVID-19 Longitudinal Health and Wellbeing - National Core Study (LHW-NCS) HMT/UKRI/MRC ([MC_PC_20030] and [MC_PC_20059]). Related funding was also provided by the NIHR 606 (CONVALESCENCE grant [COV-LT-0009]). TwinsUK is funded by the Wellcome Trust, Medical Research Council, Versus Arthritis, European Union Horizon 2020, Chronic Disease Research Foundation (CDRF), Zoe Ltd and the National Institute for Health Research (NIHR) Clinical Research Network (CRN) and Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust in partnership with King's College London. The UK Medical Research Council and Wellcome (Grant ref: [217065/Z/19/Z]) and the University of Bristol provide core support for ALSPAC.
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Affiliation(s)
- Nathan J Cheetham
- Department of Twin Research and Genetic Epidemiology, King’s College LondonLondonUnited Kingdom
| | - Milla Kibble
- Department of Twin Research and Genetic Epidemiology, King’s College LondonLondonUnited Kingdom
- Population Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
- Department of Applied Mathematics and Theoretical Physics, University of CambridgeCambridgeUnited Kingdom
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing, University College LondonLondonUnited Kingdom
| | | | - Anika Knuppel
- MRC Unit for Lifelong Health and Ageing, University College LondonLondonUnited Kingdom
| | - Dylan M Williams
- MRC Unit for Lifelong Health and Ageing, University College LondonLondonUnited Kingdom
- Department of Medical Epidemiology and Biostatistics, Karolinska InstitutetStockholmSweden
| | - Olivia KL Hamilton
- MRC/CSO Social and Public Health Sciences Unit, University of GlasgowGlasgowUnited Kingdom
| | - Paul H Lee
- Department of Health Sciences, University of LeicesterLeicesterUnited Kingdom
| | | | - Giorgio Di Gessa
- Department of Epidemiology and Public Health, University College LondonLondonUnited Kingdom
| | - Jingmin Zhu
- Department of Epidemiology and Public Health, University College LondonLondonUnited Kingdom
| | | | - George B Ploubidis
- Centre for Longitudinal Studies, University College LondonLondonUnited Kingdom
| | - Ellen J Thompson
- Department of Twin Research and Genetic Epidemiology, King’s College LondonLondonUnited Kingdom
- MRC Unit for Lifelong Health and Ageing, University College LondonLondonUnited Kingdom
| | - Ruth CE Bowyer
- Department of Twin Research and Genetic Epidemiology, King’s College LondonLondonUnited Kingdom
- Population Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
- AI for Science and Government, The Alan Turing InstituteLondonUnited Kingdom
| | - Xinyuan Zhang
- Department of Twin Research and Genetic Epidemiology, King’s College LondonLondonUnited Kingdom
| | - Golboo Abbasian
- Department of Twin Research and Genetic Epidemiology, King’s College LondonLondonUnited Kingdom
| | - Maria Paz Garcia
- Department of Twin Research and Genetic Epidemiology, King’s College LondonLondonUnited Kingdom
| | - Deborah Hart
- Department of Twin Research and Genetic Epidemiology, King’s College LondonLondonUnited Kingdom
| | - Jeffrey Seow
- Department of Infectious Diseases, King's College LondonLondonUnited Kingdom
| | - Carl Graham
- Department of Infectious Diseases, King's College LondonLondonUnited Kingdom
| | - Neophytos Kouphou
- Department of Infectious Diseases, King's College LondonLondonUnited Kingdom
| | - Sam Acors
- Department of Infectious Diseases, King's College LondonLondonUnited Kingdom
| | - Michael H Malim
- Department of Infectious Diseases, King's College LondonLondonUnited Kingdom
| | - Ruth E Mitchell
- Population Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
| | - Kate Northstone
- Population Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
| | - Daniel Major-Smith
- Population Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
| | - Sarah Matthews
- Population Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
| | - Thomas Breeze
- Population Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
| | - Michael Crawford
- Population Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
| | - Lynn Molloy
- Population Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
| | - Alex SF Kwong
- Population Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
- Division of Psychiatry, University of EdinburghEdinburghUnited Kingdom
| | - Katie Doores
- Department of Infectious Diseases, King's College LondonLondonUnited Kingdom
| | - Nishi Chaturvedi
- MRC Unit for Lifelong Health and Ageing, University College LondonLondonUnited Kingdom
| | - Emma L Duncan
- Department of Twin Research and Genetic Epidemiology, King’s College LondonLondonUnited Kingdom
- Guy’s & St Thomas’s NHS Foundation TrustLondonUnited Kingdom
| | - Nicholas J Timpson
- Population Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
| | - Claire J Steves
- Department of Twin Research and Genetic Epidemiology, King’s College LondonLondonUnited Kingdom
- Guy’s & St Thomas’s NHS Foundation TrustLondonUnited Kingdom
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Brooks-Pollock E, Northstone K, Pellis L, Scarabel F, Thomas A, Nixon E, Matthews DA, Bowyer V, Garcia MP, Steves CJ, Timpson NJ, Danon L. Voluntary risk mitigation behaviour can reduce impact of SARS-CoV-2: a real-time modelling study of the January 2022 Omicron wave in England. BMC Med 2023; 21:25. [PMID: 36658548 PMCID: PMC9851586 DOI: 10.1186/s12916-022-02714-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 12/15/2022] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Predicting the likely size of future SARS-CoV-2 waves is necessary for public health planning. In England, voluntary "plan B" mitigation measures were introduced in December 2021 including increased home working and face coverings in shops but stopped short of restrictions on social contacts. The impact of voluntary risk mitigation behaviours on future SARS-CoV-2 burden is unknown. METHODS We developed a rapid online survey of risk mitigation behaviours ahead of the winter 2021 festive period and deployed in two longitudinal cohort studies in the UK (Avon Longitudinal Study of Parents and Children (ALSPAC) and TwinsUK/COVID Symptom Study (CSS) Biobank) in December 2021. Using an individual-based, probabilistic model of COVID-19 transmission between social contacts with SARS-CoV-2 Omicron variant parameters and realistic vaccine coverage in England, we predicted the potential impact of the SARS-CoV-2 Omicron wave in England in terms of the effective reproduction number and cumulative infections, hospital admissions and deaths. Using survey results, we estimated in real-time the impact of voluntary risk mitigation behaviours on the Omicron wave in England, if implemented for the entire epidemic wave. RESULTS Over 95% of survey respondents (NALSPAC = 2686 and NTwins = 6155) reported some risk mitigation behaviours, with vaccination and using home testing kits reported most frequently. Less than half of those respondents reported that their behaviour was due to "plan B". We estimate that without risk mitigation behaviours, the Omicron variant is consistent with an effective reproduction number between 2.5 and 3.5. Due to the reduced vaccine effectiveness against infection with the Omicron variant, our modelled estimates suggest that between 55% and 60% of the English population could be infected during the current wave, translating into between 12,000 and 46,000 cumulative deaths, depending on assumptions about severity and vaccine effectiveness. The actual number of deaths was 15,208 (26 November 2021-1 March 2022). We estimate that voluntary risk reduction measures could reduce the effective reproduction number to between 1.8 and 2.2 and reduce the cumulative number of deaths by up to 24%. CONCLUSIONS Predicting future infection burden is affected by uncertainty in disease severity and vaccine effectiveness estimates. In addition to biological uncertainty, we show that voluntary measures substantially reduce the projected impact of the SARS-CoV-2 Omicron variant but that voluntary measures alone would be unlikely to completely control transmission.
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Affiliation(s)
- Ellen Brooks-Pollock
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Kate Northstone
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Lorenzo Pellis
- Department of Mathematics, University of Manchester, Manchester, UK
- School of Biological Sciences, University of Bristol, Bristol, UK
| | | | - Amy Thomas
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Emily Nixon
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- The Alan Turing Institute, London, UK
| | - David A Matthews
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, UK
| | - Vicky Bowyer
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Maria Paz Garcia
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Claire J Steves
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Nicholas J Timpson
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit at University of Bristol, Bristol, UK
| | - Leon Danon
- Department of Engineering Mathematics, University of Bristol, Bristol, UK
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38
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Frankenfeld CL. Fecal Metabolome: New Addition to the Toolbox for Dietary Assessment? J Nutr 2023; 152:2643-2644. [PMID: 36288243 DOI: 10.1093/jn/nxac233] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
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Shinn LM, Mansharamani A, Baer DJ, Novotny JA, Charron CS, Khan NA, Zhu R, Holscher HD. Fecal Metabolites as Biomarkers for Predicting Food Intake by Healthy Adults. J Nutr 2023; 152:2956-2965. [PMID: 36040343 PMCID: PMC9840004 DOI: 10.1093/jn/nxac195] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 08/01/2022] [Accepted: 08/25/2022] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND The fecal metabolome is affected by diet and includes metabolites generated by human and microbial metabolism. Advances in -omics technologies and analytic approaches have allowed researchers to identify metabolites and better utilize large data sets to generate usable information. One promising aspect of these advancements is the ability to determine objective biomarkers of food intake. OBJECTIVES We aimed to utilize a multivariate, machine learning approach to identify metabolite biomarkers that accurately predict food intake. METHODS Data were aggregated from 5 controlled feeding studies in adults that tested the impact of specific foods (almonds, avocados, broccoli, walnuts, barley, and oats) on the gastrointestinal microbiota. Fecal samples underwent GC-MS metabolomic analysis; 344 metabolites were detected in preintervention samples, whereas 307 metabolites were detected postintervention. After removing metabolites that were only detected in either pre- or postintervention and those undetectable in ≥80% of samples in all study groups, changes in 96 metabolites relative concentrations (treatment postintervention minus preintervention) were utilized in random forest models to 1) examine the relation between food consumption and fecal metabolome changes and 2) rank the fecal metabolites by their predictive power (i.e., feature importance score). RESULTS Using the change in relative concentration of 96 fecal metabolites, 6 single-food random forest models for almond, avocado, broccoli, walnuts, whole-grain barley, and whole-grain oats revealed prediction accuracies between 47% and 89%. When comparing foods with one another, almond intake was differentiated from walnut intake with 91% classification accuracy. CONCLUSIONS Our findings reveal promise in utilizing fecal metabolites as objective complements to certain self-reported food intake estimates. Future research on other foods at different doses and dietary patterns is needed to identify biomarkers that can be applied in feeding study compliance and clinical settings.
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Affiliation(s)
- Leila M Shinn
- Division of Nutritional Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Aditya Mansharamani
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - David J Baer
- Beltsville Human Nutrition Research Center, Agricultural Research Service, USDA, Beltsville, MD, USA
| | - Janet A Novotny
- Beltsville Human Nutrition Research Center, Agricultural Research Service, USDA, Beltsville, MD, USA
| | - Craig S Charron
- Beltsville Human Nutrition Research Center, Agricultural Research Service, USDA, Beltsville, MD, USA
| | - Naiman A Khan
- Division of Nutritional Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Kinesiology & Community Health, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Ruoqing Zhu
- Department of Statistics, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Hannah D Holscher
- Division of Nutritional Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Kinesiology & Community Health, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Food Science and Human Nutrition, University of Illinois at Urbana-Champaign, Urbana, IL, USA
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Mijakovac A, Frkatović A, Hanić M, Ivok J, Martinić Kavur M, Pučić-Baković M, Spector T, Zoldoš V, Mangino M, Lauc G. Heritability of the glycan clock of biological age. Front Cell Dev Biol 2022; 10:982609. [PMID: 36619858 PMCID: PMC9815111 DOI: 10.3389/fcell.2022.982609] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 11/24/2022] [Indexed: 12/24/2022] Open
Abstract
Immunoglobulin G is posttranslationally modified by the addition of complex N-glycans affecting its function and mediating inflammation at multiple levels. IgG glycome composition changes with age and health in a predictive pattern, presumably due to inflammaging. As a result, a novel biological aging biomarker, glycan clock of age, was developed. Glycan clock of age is the first of biological aging clocks for which multiple studies showed a possibility of clock reversal even with simple lifestyle interventions. However, none of the previous studies determined to which extent the glycan clock can be turned, and how much is fixed by genetic predisposition. To determine the contribution of genetic and environmental factors to phenotypic variation of the glycan clock, we performed heritability analysis on two TwinsUK female cohorts. IgG glycans from monozygotic and dizygotic twin pairs were analyzed by UHPLC and glycan age was calculated using the glycan clock. In order to determine additive genetic, shared, and unique environmental contributions, a classical twin design was applied. Heritability of the glycan clock was calculated for participants of one cross-sectional and one longitudinal cohort with three time points to assess the reliability of measurements. Heritability estimate for the glycan clock was 39% on average, suggesting a moderate contribution of additive genetic factors (A) to glycan clock variation. Remarkably, heritability estimates remained approximately the same in all time points of the longitudinal study, even though IgG glycome composition changed substantially. Most environmental contributions came from shared environmental factors (C), with unique environmental factors (E) having a minor role. Interestingly, heritability estimates nearly doubled, to an average of 71%, when we included age as a covariant. This intervention also inflated the estimates of unique environmental factors contributing to glycan clock variation. A complex interplay between genetic and environmental factors defines alternative IgG glycosylation during aging and, consequently, dictates the glycan clock's ticking. Apparently, environmental factors (including lifestyle choices) have a strong impact on the biological age measured with the glycan clock, which additionally clarifies why this aging clock is one of the most potent biomarkers of biological aging.
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Affiliation(s)
- Anika Mijakovac
- Division of Molecular Biology, Department of Biology, Faculty of Science, University of Zagreb, Zagreb, Croatia
| | | | - Maja Hanić
- Genos Glycoscience Research Laboratory, Zagreb, Croatia
| | - Jelena Ivok
- Genos Glycoscience Research Laboratory, Zagreb, Croatia
| | | | | | - Tim Spector
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
| | - Vlatka Zoldoš
- Division of Molecular Biology, Department of Biology, Faculty of Science, University of Zagreb, Zagreb, Croatia
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom,NIHR Biomedical Research Centre at Guy’s and St Thoma’s Foundation Trust, London, United Kingdom
| | - Gordan Lauc
- Genos Glycoscience Research Laboratory, Zagreb, Croatia,Department of Biochemistry and Molecular Biology, Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, Croatia,*Correspondence: Gordan Lauc,
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Roberts AL, Morea A, Amar A, Zito A, El-Sayed Moustafa JS, Tomlinson M, Bowyer RCE, Zhang X, Christiansen C, Costeira R, Steves CJ, Mangino M, Bell JT, Wong CCY, Vyse TJ, Small KS. Age acquired skewed X chromosome inactivation is associated with adverse health outcomes in humans. eLife 2022; 11:e78263. [PMID: 36412098 PMCID: PMC9681199 DOI: 10.7554/elife.78263] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 10/11/2022] [Indexed: 11/23/2022] Open
Abstract
Background Ageing is a heterogenous process characterised by cellular and molecular hallmarks, including changes to haematopoietic stem cells and is a primary risk factor for chronic diseases. X chromosome inactivation (XCI) randomly transcriptionally silences either the maternal or paternal X in each cell of 46, XX females to balance the gene expression with 46, XY males. Age acquired XCI-skew describes the preferential selection of cells across a tissue resulting in an imbalance of XCI, which is particularly prevalent in blood tissues of ageing females, and yet its clinical consequences are unknown. Methods We assayed XCI in 1575 females from the TwinsUK population cohort using DNA extracted from whole blood. We employed prospective, cross-sectional, and intra-twin study designs to characterise the relationship of XCI-skew with molecular and cellular measures of ageing, cardiovascular disease risk, and cancer diagnosis. Results We demonstrate that XCI-skew is independent of traditional markers of biological ageing and is associated with a haematopoietic bias towards the myeloid lineage. Using an atherosclerotic cardiovascular disease risk score, which captures traditional risk factors, XCI-skew is associated with an increased cardiovascular disease risk both cross-sectionally and within XCI-skew discordant twin pairs. In a prospective 10 year follow-up study, XCI-skew is predictive of future cancer incidence. Conclusions Our study demonstrates that age acquired XCI-skew captures changes to the haematopoietic stem cell population and has clinical potential as a unique biomarker of chronic disease risk. Funding KSS acknowledges funding from the Medical Research Council [MR/M004422/1 and MR/R023131/1]. JTB acknowledges funding from the ESRC [ES/N000404/1]. MM acknowledges funding from the National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust in partnership with King's College London. TwinsUK is funded by the Wellcome Trust, Medical Research Council, European Union, Chronic Disease Research Foundation (CDRF), Zoe Global Ltd and the National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust in partnership with King's College London.
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Affiliation(s)
- Amy L Roberts
- Department of Twin Research & Genetic Epidemiology, King’s College LondonLondonUnited Kingdom
| | - Alessandro Morea
- Department of Twin Research & Genetic Epidemiology, King’s College LondonLondonUnited Kingdom
- Department of Medical and Molecular Genetics, King’s College LondonLondonUnited Kingdom
| | - Ariella Amar
- Department of Medical and Molecular Genetics, King’s College LondonLondonUnited Kingdom
| | - Antonino Zito
- Department of Twin Research & Genetic Epidemiology, King’s College LondonLondonUnited Kingdom
| | | | - Max Tomlinson
- Department of Twin Research & Genetic Epidemiology, King’s College LondonLondonUnited Kingdom
- Department of Medical and Molecular Genetics, King’s College LondonLondonUnited Kingdom
| | - Ruth CE Bowyer
- Department of Twin Research & Genetic Epidemiology, King’s College LondonLondonUnited Kingdom
| | - Xinyuan Zhang
- Department of Twin Research & Genetic Epidemiology, King’s College LondonLondonUnited Kingdom
| | - Colette Christiansen
- Department of Twin Research & Genetic Epidemiology, King’s College LondonLondonUnited Kingdom
| | - Ricardo Costeira
- Department of Twin Research & Genetic Epidemiology, King’s College LondonLondonUnited Kingdom
| | - Claire J Steves
- Department of Twin Research & Genetic Epidemiology, King’s College LondonLondonUnited Kingdom
| | - Massimo Mangino
- Department of Twin Research & Genetic Epidemiology, King’s College LondonLondonUnited Kingdom
- NIHR Biomedical Research Centre, Guy's and St Thomas' Foundation TrustLondonUnited Kingdom
| | - Jordana T Bell
- Department of Twin Research & Genetic Epidemiology, King’s College LondonLondonUnited Kingdom
| | - Chloe CY Wong
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College LondonLondonUnited Kingdom
| | - Timothy J Vyse
- Department of Medical and Molecular Genetics, King’s College LondonLondonUnited Kingdom
| | - Kerrin S Small
- Department of Twin Research & Genetic Epidemiology, King’s College LondonLondonUnited Kingdom
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Österdahl MF, Christakou E, Hart D, Harris F, Shahrabi Y, Pollock E, Wadud M, Spector TD, Brown MA, Seow J, Malim MH, Steves CJ, Doores KJ, Duncan EL, Tree T. Concordance of B- and T-cell responses to SARS-CoV-2 infection, irrespective of symptoms suggestive of COVID-19. J Med Virol 2022; 94:5217-5224. [PMID: 35864567 PMCID: PMC9349709 DOI: 10.1002/jmv.28016] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 06/29/2022] [Accepted: 06/30/2022] [Indexed: 12/15/2022]
Abstract
This study assessed T-cell responses in individuals with and without a positive antibody response to SARS-CoV-2, in symptomatic and asymptomatic individuals during the COVID-19 pandemic. Participants were drawn from the TwinsUK cohort, grouped by (a) presence or absence of COVID-associated symptoms (S+, S-), logged prospectively through the COVID Symptom Study app, and (b) anti-IgG Spike and anti-IgG Nucleocapsid antibodies measured by ELISA (Ab+, Ab-), during the first wave of the UK pandemic. T-cell helper and regulatory responses after stimulation with SARS-CoV-2 peptides were assessed. Thirty-two participants were included in the final analysis. Fourteen of 15 with IgG Spike antibodies had a T-cell response to SARS-CoV-2-specific peptides; none of 17 participants without IgG Spike antibodies had a T-cell response (χ2 : 28.2, p < 0.001). Quantitative T-cell responses correlated strongly with fold-change in IgG Spike antibody titer (ρ = 0.79, p < 0.0001) but not to symptom score (ρ = 0.17, p = 0.35). Humoral and cellular immune responses to SARS-CoV-2 are highly correlated. We found no evidence of cellular immunity suggestive of SARS-CoV2 infection in individuals with a COVID-19-like illness but negative antibodies.
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Affiliation(s)
- Marc F. Österdahl
- Department of Twin Research & Genetic Epidemiology, School of Life Course Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
- Department of Ageing and HealthGuy's and St Thomas' NHS Foundation TrustLondonUK
| | - Eleni Christakou
- Department of Immunobiology, School of Immunology & Microbial SciencesKing's College LondonLondonUK
| | - Deborah Hart
- Department of Twin Research & Genetic Epidemiology, School of Life Course Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
| | - Ffion Harris
- Department of Immunobiology, School of Immunology & Microbial SciencesKing's College LondonLondonUK
| | - Yasaman Shahrabi
- Department of Immunobiology, School of Immunology & Microbial SciencesKing's College LondonLondonUK
| | - Emily Pollock
- Department of Immunobiology, School of Immunology & Microbial SciencesKing's College LondonLondonUK
| | - Muntaha Wadud
- Department of Immunobiology, School of Immunology & Microbial SciencesKing's College LondonLondonUK
| | - Tim D. Spector
- Department of Twin Research & Genetic Epidemiology, School of Life Course Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
| | - Matthew A. Brown
- Guy's and St Thomas' NHS Foundation Trust and King's College London NIHR Biomedical Research CentreKing's College LondonLondonUK
| | - Jeffrey Seow
- Department of Infectious Diseases, School of Immunology & Microbial SciencesKing's College LondonLondonUK
| | - Michael H. Malim
- Department of Infectious Diseases, School of Immunology & Microbial SciencesKing's College LondonLondonUK
| | - Claire J. Steves
- Department of Twin Research & Genetic Epidemiology, School of Life Course Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
- Department of Ageing and HealthGuy's and St Thomas' NHS Foundation TrustLondonUK
| | - Katie J. Doores
- Department of Infectious Diseases, School of Immunology & Microbial SciencesKing's College LondonLondonUK
| | - Emma L. Duncan
- Department of Twin Research & Genetic Epidemiology, School of Life Course Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
- Department of EndocrinologyGuy's and St Thomas' NHS Foundation TrustLondonUK
| | - Timothy Tree
- Department of Immunobiology, School of Immunology & Microbial SciencesKing's College LondonLondonUK
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43
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Santamaria P, Bowyer RC, Nibali L. Associations between host genetic variants and Herpes Simplex Labialis in the TwinsUK cohort. Arch Oral Biol 2022; 145:105587. [DOI: 10.1016/j.archoralbio.2022.105587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 11/14/2022] [Accepted: 11/15/2022] [Indexed: 11/18/2022]
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Louca P, Berry SE, Bermingham K, Franks PW, Wolf J, Spector TD, Valdes AM, Chowienczyk P, Menni C. Postprandial Responses to a Standardised Meal in Hypertension: The Mediatory Role of Visceral Fat Mass. Nutrients 2022; 14:4499. [PMID: 36364763 PMCID: PMC9655022 DOI: 10.3390/nu14214499] [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: 09/18/2022] [Revised: 10/17/2022] [Accepted: 10/18/2022] [Indexed: 11/16/2022] Open
Abstract
Postprandial insulinaemia, triglyceridaemia and measures of inflammation are thought to be more closely associated with cardiovascular risk than fasting measures. Although hypertension is associated with altered fasting metabolism, it is unknown as to what extent postprandial lipaemic and inflammatory metabolic responses differ between hypertensive and normotensive individuals. Linear models adjusting for age, sex, body mass index (BMI), visceral fat mass (VFM) and multiple testing (false discovery rate), were used to investigate whether hypertensive cases and normotensive controls had different fasting and postprandial (in response to two standardised test meal challenges) lipaemic, glycaemic, insulinaemic, and inflammatory (glycoprotein acetylation (GlycA)) responses in 989 participants from the ZOE PREDICT-1 nutritional intervention study. Compared to normotensive controls, hypertensive individuals had significantly higher fasting and postprandial insulin, triglycerides, and markers of inflammation after adjusting for age, sex, and BMI (effect size: Beta (Standard Error) ranging from 0.17 (0.08), p = 0.04 for peak insulin to 0.29 (0.08), p = 4.4 × 10-4 for peak GlycA). No difference was seen for postprandial glucose. When further adjusting for VFM effects were attenuated. Causal mediation analysis suggests that 36% of the variance in postprandial insulin response and 33.8% of variance in postprandial triglyceride response were mediated by VFM. Hypertensive individuals have different postprandial insulinaemic and lipaemic responses compared to normotensive controls and this is partially mediated by visceral fat mass. Consequently, reducing VFM should be a key focus of health interventions in hypertension. Trial registration: The ClinicalTrials.gov registration identifier is NCT03479866.
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Affiliation(s)
- Panayiotis Louca
- Department of Twin Research, King’s College London, St Thomas’ Hospital Campus, London SE1 7EH, UK
| | - Sarah E. Berry
- Department of Nutritional Sciences, King’s College London, Franklin Wilkins Building, London SE1 9NH, UK
| | - Kate Bermingham
- Department of Twin Research, King’s College London, St Thomas’ Hospital Campus, London SE1 7EH, UK
- Department of Nutritional Sciences, King’s College London, Franklin Wilkins Building, London SE1 9NH, UK
| | - Paul W. Franks
- Genetic & Molecular Epidemiology Unit, Department of Clinical Sciences, Lund University, SE-20502 Malmo, Sweden
| | | | - Tim D. Spector
- Department of Twin Research, King’s College London, St Thomas’ Hospital Campus, London SE1 7EH, UK
| | - Ana M. Valdes
- Nottingham NIHR Biomedical Research Centre, School of Medicine, University of Nottingham, Nottingham NG5 1PB, UK
| | - Phil Chowienczyk
- Vascular Risk & Surgery, King’s College London, St Thomas’ Hospital Campus, London SE1 7EH, UK
| | - Cristina Menni
- Department of Nutritional Sciences, King’s College London, Franklin Wilkins Building, London SE1 9NH, UK
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Maddock J, Parsons S, Di Gessa G, Green MJ, Thompson EJ, Stevenson AJ, Kwong AS, McElroy E, Santorelli G, Silverwood RJ, Captur G, Chaturvedi N, Steves CJ, Steptoe A, Patalay P, Ploubidis GB, Katikireddi SV. Inequalities in healthcare disruptions during the COVID-19 pandemic: evidence from 12 UK population-based longitudinal studies. BMJ Open 2022; 12:e064981. [PMID: 36229151 PMCID: PMC9561494 DOI: 10.1136/bmjopen-2022-064981] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
OBJECTIVES We investigated associations between multiple sociodemographic characteristics (sex, age, occupational social class, education and ethnicity) and self-reported healthcare disruptions during the early stages of the COVID-19 pandemic. DESIGN Coordinated analysis of prospective population surveys. SETTING Community-dwelling participants in the UK between April 2020 and January 2021. PARTICIPANTS Over 68 000 participants from 12 longitudinal studies. OUTCOMES Self-reported healthcare disruption to medication access, procedures and appointments. RESULTS Prevalence of healthcare disruption varied substantially across studies: between 6% and 32% reported any disruption, with 1%-10% experiencing disruptions in medication, 1%-17% experiencing disruption in procedures and 4%-28% experiencing disruption in clinical appointments. Females (OR 1.27; 95% CI 1.15 to 1.40; I2=54%), older persons (eg, OR 1.39; 95% CI 1.13 to 1.72; I2=77% for 65-75 years vs 45-54 years) and ethnic minorities (excluding white minorities) (OR 1.19; 95% CI 1.05 to 1.35; I2=0% vs white) were more likely to report healthcare disruptions. Those in a more disadvantaged social class were also more likely to report healthcare disruptions (eg, OR 1.17; 95% CI 1.08 to 1.27; I2=0% for manual/routine vs managerial/professional), but no clear differences were observed by education. We did not find evidence that these associations differed by shielding status. CONCLUSIONS Healthcare disruptions during the COVID-19 pandemic could contribute to the maintenance or widening of existing health inequalities.
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Affiliation(s)
- Jane Maddock
- MRC Unit for Lifelong Health and Ageing, UCL, London, UK
| | - Sam Parsons
- Centre for Longitudinal Studies, Social Research Institute, UCL, London, UK
| | | | - Michael J Green
- MRC/CSO Social & Public Health Sciences Unit, University of Glasgow, Glasgow, UK
| | - Ellen J Thompson
- Department of Twin Research and Genetic Epidemiology, School of Life Course & Population Sciences, King's College London, London, UK
| | - Anna J Stevenson
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Alex Sf Kwong
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Eoin McElroy
- Department of Neuroscience, Psychology and Behaviour, University of Leicester, Leicester, UK
| | | | | | | | | | - Claire J Steves
- Department of Twin Research and Genetic Epidemiology, School of Life Course & Population Sciences, King's College London, London, UK
| | - Andrew Steptoe
- Department of Epidemiology and Public Health, UCL, London, UK
| | - Praveetha Patalay
- MRC Unit for Lifelong Health and Ageing, UCL, London, UK
- Centre for Longitudinal Studies, Social Research Institute, UCL, London, UK
| | - George B Ploubidis
- Centre for Longitudinal Studies, Social Research Institute, UCL, London, UK
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Louca P, Tran TQB, Toit CD, Christofidou P, Spector TD, Mangino M, Suhre K, Padmanabhan S, Menni C. Machine learning integration of multimodal data identifies key features of blood pressure regulation. EBioMedicine 2022; 84:104243. [PMID: 36084617 PMCID: PMC9463529 DOI: 10.1016/j.ebiom.2022.104243] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 08/02/2022] [Accepted: 08/11/2022] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Association studies have identified several biomarkers for blood pressure and hypertension, but a thorough understanding of their mutual dependencies is lacking. By integrating two different high-throughput datasets, biochemical and dietary data, we aim to understand the multifactorial contributors of blood pressure (BP). METHODS We included 4,863 participants from TwinsUK with concurrent BP, metabolomics, genomics, biochemical measures, and dietary data. We used 5-fold cross-validation with the machine learning XGBoost algorithm to identify features of importance in context of one another in TwinsUK (80% training, 20% test). The features tested in TwinsUK were then probed using the same algorithm in an independent dataset of 2,807 individuals from the Qatari Biobank (QBB). FINDINGS Our model explained 39·2% [4·5%, MAE:11·32 mmHg (95%CI, +/- 0·65)] of the variance in systolic BP (SBP) in TwinsUK. Of the top 50 features, the most influential non-demographic variables were dihomo-linolenate, cis-4-decenoyl carnitine, lactate, chloride, urate, and creatinine along with dietary intakes of total, trans and saturated fat. We also highlight the incremental value of each included dimension. Furthermore, we replicated our model in the QBB [SBP variance explained = 45·2% (13·39%)] cohort and 30 of the top 50 features overlapped between cohorts. INTERPRETATION We show that an integrated analysis of omics, biochemical and dietary data improves our understanding of their in-between relationships and expands the range of potential biomarkers for blood pressure. Our results point to potentially key biological pathways to be prioritised for mechanistic studies. FUNDING Chronic Disease Research Foundation, Medical Research Council, Wellcome Trust, Qatar Foundation.
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Affiliation(s)
- Panayiotis Louca
- Department of Twin Research and Genetic Epidemiology, King's College London, London, England, SE1 7EH, United Kingdom
| | - Tran Quoc Bao Tran
- Institute of Cardiovascular & Medical Sciences, University of Glasgow, Glasgow G12 8QQ, United Kingdom
| | - Clea du Toit
- Institute of Cardiovascular & Medical Sciences, University of Glasgow, Glasgow G12 8QQ, United Kingdom
| | - Paraskevi Christofidou
- Department of Twin Research and Genetic Epidemiology, King's College London, London, England, SE1 7EH, United Kingdom
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, England, SE1 7EH, United Kingdom
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, King's College London, London, England, SE1 7EH, United Kingdom; NIHR Biomedical Research Centre at Guy's and St Thomas' Foundation Trust, London, SE1 9RT, United Kingdom
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Doha, Qatar; Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Sandosh Padmanabhan
- Institute of Cardiovascular & Medical Sciences, University of Glasgow, Glasgow G12 8QQ, United Kingdom.
| | - Cristina Menni
- Department of Twin Research and Genetic Epidemiology, King's College London, London, England, SE1 7EH, United Kingdom.
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Klimentidis YC, Newell M, van der Zee MD, Bland VL, May-Wilson S, Arani G, Menni C, Mangino M, Arora A, Raichlen DA, Alexander GE, Wilson JF, Boomsma DI, Hottenga JJ, de Geus EJ, Pirastu N. Genome-wide Association Study of Liking for Several Types of Physical Activity in the UK Biobank and Two Replication Cohorts. Med Sci Sports Exerc 2022; 54:1252-1260. [PMID: 35320144 PMCID: PMC9288543 DOI: 10.1249/mss.0000000000002907] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
INTRODUCTION A lack of physical activity (PA) is one of the most pressing health issues today. Our individual propensity for PA is influenced by genetic factors. Stated liking of different PA types may help capture additional and informative dimensions of PA behavior genetics. METHODS In over 157,000 individuals from the UK Biobank, we performed genome-wide association studies of five items assessing the liking of different PA types, plus an additional derived trait of overall PA-liking. We attempted to replicate significant associations in the Netherlands Twin Register (NTR) and TwinsUK. Additionally, polygenic scores (PGS) were trained in the UK Biobank for each PA-liking item and for self-reported PA behavior, and tested for association with PA in the NTR. RESULTS We identified a total of 19 unique significant loci across all five PA-liking items and the overall PA-liking trait, and these showed strong directional consistency in the replication cohorts. Four of these loci were previously identified for PA behavior, including CADM2 , which was associated with three PA-liking items. The PA-liking items were genetically correlated with self-reported ( rg = 0.38-0.80) and accelerometer ( rg = 0.26-0.49) PA measures, and with a wide range of health-related traits. Each PA-liking PGS significantly predicted the same PA-liking item in NTR. The PGS of liking for going to the gym predicted PA behavior in the NTR ( r2 = 0.40%) nearly as well as a PGS based on self-reported PA behavior ( r2 = 0.42%). Combining the two PGS into a single model increased the r2 to 0.59%, suggesting that PA-liking captures distinct and relevant dimensions of PA behavior. CONCLUSIONS We have identified the first loci associated with PA-liking and extended our understanding of the genetic basis of PA behavior.
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Affiliation(s)
- Yann C. Klimentidis
- Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ
| | - Michelle Newell
- Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ
| | - Matthijs D. van der Zee
- Department of Biological Psychology, Vrije Universiteit Amsterdam, THE NETHERLANDS
- Amsterdam Public Health Institute, Amsterdam UMC, THE NETHERLANDS
| | - Victoria L. Bland
- Division of Geriatric Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Sebastian May-Wilson
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UNITED KINGDOM
| | - Gayatri Arani
- Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ
| | - Cristina Menni
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, UNITED KINGDOM
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, UNITED KINGDOM
- NIHR Biomedical Research Centre at Guy’s and St Thomas’ Foundation Trust, London, UNITED KINGDOM
| | - Amit Arora
- Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ
| | - David A. Raichlen
- Human and Evolutionary Biology Section, Department of Biological Sciences, University of Southern California, CA
| | - Gene E. Alexander
- Department of Psychology and Psychiatry, University of Arizona, Tucson, AZ
- Neuroscience and Physiological Sciences Graduate Inter-Disciplinary Programs, University of Arizona, Tucson, AZ
- Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ
- Arizona Alzheimer’s Consortium, AZ
| | - James F. Wilson
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UNITED KINGDOM
- MRC Human Genetics Unit, Institute of Genetic and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UNITED KINGDOM
| | - Dorret I. Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, THE NETHERLANDS
- Amsterdam Public Health Institute, Amsterdam UMC, THE NETHERLANDS
| | - Jouke-Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit Amsterdam, THE NETHERLANDS
- Amsterdam Public Health Institute, Amsterdam UMC, THE NETHERLANDS
| | - Eco J.C. de Geus
- Department of Biological Psychology, Vrije Universiteit Amsterdam, THE NETHERLANDS
- Amsterdam Public Health Institute, Amsterdam UMC, THE NETHERLANDS
| | - Nicola Pirastu
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UNITED KINGDOM
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48
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Christiansen C, Tomlinson M, Eliot M, Nilsson E, Costeira R, Xia Y, Villicaña S, Mompeo O, Wells P, Castillo-Fernandez J, Potier L, Vohl MC, Tchernof A, Moustafa JES, Menni C, Steves CJ, Kelsey K, Ling C, Grundberg E, Small KS, Bell JT. Adipose methylome integrative-omic analyses reveal genetic and dietary metabolic health drivers and insulin resistance classifiers. Genome Med 2022; 14:75. [PMID: 35843982 PMCID: PMC9290282 DOI: 10.1186/s13073-022-01077-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 06/21/2022] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND There is considerable evidence for the importance of the DNA methylome in metabolic health, for example, a robust methylation signature has been associated with body mass index (BMI). However, visceral fat (VF) mass accumulation is a greater risk factor for metabolic disease than BMI alone. In this study, we dissect the subcutaneous adipose tissue (SAT) methylome signature relevant to metabolic health by focusing on VF as the major risk factor of metabolic disease. We integrate results with genetic, blood methylation, SAT gene expression, blood metabolomic, dietary intake and metabolic phenotype data to assess and quantify genetic and environmental drivers of the identified signals, as well as their potential functional roles. METHODS Epigenome-wide association analyses were carried out to determine visceral fat mass-associated differentially methylated positions (VF-DMPs) in SAT samples from 538 TwinsUK participants. Validation and replication were performed in 333 individuals from 3 independent cohorts. To assess functional impacts of the VF-DMPs, the association between VF and gene expression was determined at the genes annotated to the VF-DMPs and an association analysis was carried out to determine whether methylation at the VF-DMPs is associated with gene expression. Further epigenetic analyses were carried out to compare methylation levels at the VF-DMPs as the response variables and a range of different metabolic health phenotypes including android:gynoid fat ratio (AGR), lipids, blood metabolomic profiles, insulin resistance, T2D and dietary intake variables. The results from all analyses were integrated to identify signals that exhibit altered SAT function and have strong relevance to metabolic health. RESULTS We identified 1181 CpG positions in 788 genes to be differentially methylated with VF (VF-DMPs) with significant enrichment in the insulin signalling pathway. Follow-up cross-omic analysis of VF-DMPs integrating genetics, gene expression, metabolomics, diet, and metabolic traits highlighted VF-DMPs located in 9 genes with strong relevance to metabolic disease mechanisms, with replication of signals in FASN, SREBF1, TAGLN2, PC and CFAP410. PC methylation showed evidence for mediating effects of diet on VF. FASN DNA methylation exhibited putative causal effects on VF that were also strongly associated with insulin resistance and methylation levels in FASN better classified insulin resistance (AUC=0.91) than BMI or VF alone. CONCLUSIONS Our findings help characterise the adiposity-associated methylation signature of SAT, with insights for metabolic disease risk.
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Affiliation(s)
- Colette Christiansen
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.
| | - Max Tomlinson
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- Department of Medical & Molecular Genetics, King's College London, London, UK
| | - Melissa Eliot
- Department of Epidemiology, Brown University School of Public Health, Providence, R.I., USA
| | - Emma Nilsson
- Epigenetics and Diabetes Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Scania University Hospital, Malmö, Sweden
| | - Ricardo Costeira
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Yujing Xia
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Sergio Villicaña
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Olatz Mompeo
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Philippa Wells
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | | | - Louis Potier
- Diabetology Department, Bichat Hospital, AP-HP, Université de Paris, Paris, France
| | - Marie-Claude Vohl
- Institute of Nutrition and Functional Foods (INAF), Université Laval, Québec, QC, Canada
| | - Andre Tchernof
- Québec Heart and Lung Institute, Université Laval, Québec, QC, Canada
| | | | - Cristina Menni
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Claire J Steves
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Karl Kelsey
- Department of Epidemiology, Brown University School of Public Health, Providence, R.I., USA
| | - Charlotte Ling
- Epigenetics and Diabetes Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Scania University Hospital, Malmö, Sweden
| | - Elin Grundberg
- Genomic Medicine Center, Children's Mercy Research Institute, Children's Mercy Kansas City, Kansas City, MO, 64108, USA
| | - Kerrin S Small
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.
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49
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Thompson EJ, Williams DM, Walker AJ, Mitchell RE, Niedzwiedz CL, Yang TC, Huggins CF, Kwong ASF, Silverwood RJ, Di Gessa G, Bowyer RCE, Northstone K, Hou B, Green MJ, Dodgeon B, Doores KJ, Duncan EL, Williams FMK, Steptoe A, Porteous DJ, McEachan RRC, Tomlinson L, Goldacre B, Patalay P, Ploubidis GB, Katikireddi SV, Tilling K, Rentsch CT, Timpson NJ, Chaturvedi N, Steves CJ. Long COVID burden and risk factors in 10 UK longitudinal studies and electronic health records. Nat Commun 2022; 13:3528. [PMID: 35764621 PMCID: PMC9240035 DOI: 10.1038/s41467-022-30836-0] [Citation(s) in RCA: 217] [Impact Index Per Article: 108.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 05/19/2022] [Indexed: 12/14/2022] Open
Abstract
The frequency of, and risk factors for, long COVID are unclear among community-based individuals with a history of COVID-19. To elucidate the burden and possible causes of long COVID in the community, we coordinated analyses of survey data from 6907 individuals with self-reported COVID-19 from 10 UK longitudinal study (LS) samples and 1.1 million individuals with COVID-19 diagnostic codes in electronic healthcare records (EHR) collected by spring 2021. Proportions of presumed COVID-19 cases in LS reporting any symptoms for 12+ weeks ranged from 7.8% and 17% (with 1.2 to 4.8% reporting debilitating symptoms). Increasing age, female sex, white ethnicity, poor pre-pandemic general and mental health, overweight/obesity, and asthma were associated with prolonged symptoms in both LS and EHR data, but findings for other factors, such as cardio-metabolic parameters, were inconclusive.
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Affiliation(s)
- Ellen J Thompson
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK.
| | - Dylan M Williams
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK.
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
| | - Alex J Walker
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxfort, UK
| | - Ruth E Mitchell
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | | | - Tiffany C Yang
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, BD9 6RJ, UK
| | - Charlotte F Huggins
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Alex S F Kwong
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Richard J Silverwood
- Centre for Longitudinal Studies, UCL Social Research Institute, University College London, London, UK
| | - Giorgio Di Gessa
- Department of Epidemiology and Public Health, University College London, London, UK
| | - Ruth C E Bowyer
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK
| | - Kate Northstone
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Bo Hou
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, BD9 6RJ, UK
| | - Michael J Green
- MRC/CSO Social & Public Health Sciences Unit, University of Glasgow, Glasgow, UK
| | - Brian Dodgeon
- Centre for Longitudinal Studies, UCL Social Research Institute, University College London, London, UK
| | - Katie J Doores
- School of Immunology & Microbial Sciences, King's College London, London, UK
| | - Emma L Duncan
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK
| | - Frances M K Williams
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK
| | - Andrew Steptoe
- Department of Epidemiology and Public Health, University College London, London, UK
| | - David J Porteous
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Rosemary R C McEachan
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, BD9 6RJ, UK
| | - Laurie Tomlinson
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Ben Goldacre
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxfort, UK
| | - Praveetha Patalay
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
- Centre for Longitudinal Studies, UCL Social Research Institute, University College London, London, UK
| | - George B Ploubidis
- Centre for Longitudinal Studies, UCL Social Research Institute, University College London, London, UK
| | | | - Kate Tilling
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
| | - Christopher T Rentsch
- Electronic Health Records Research Group, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
- VA Connecticut Healthcare System, West Haven, CT, USA
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Nishi Chaturvedi
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
| | - Claire J Steves
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK.
- Department of Ageing and Health, Guys and St Thomas's NHS Foundation Trust, London, UK.
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50
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Louca P, Nogal A, Moskal A, Goulding NJ, Shipley MJ, Alkis T, Lindbohm JV, Hu J, Kifer D, Wang N, Chawes B, Rexrode KM, Ben-Shlomo Y, Kivimaki M, Murphy RA, Yu B, Gunter MJ, Suhre K, Lawlor DA, Mangino M, Menni C. Cross-Sectional Blood Metabolite Markers of Hypertension: A Multicohort Analysis of 44,306 Individuals from the COnsortium of METabolomics Studies. Metabolites 2022; 12:601. [PMID: 35888725 PMCID: PMC9324896 DOI: 10.3390/metabo12070601] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 05/27/2022] [Accepted: 06/09/2022] [Indexed: 12/30/2022] Open
Abstract
Hypertension is the main modifiable risk factor for cardiovascular morbidity and mortality but discovering molecular mechanisms for targeted treatment has been challenging. Here we investigate associations of blood metabolite markers with hypertension by integrating data from nine intercontinental cohorts from the COnsortium of METabolomics Studies. We included 44,306 individuals with circulating metabolites (up to 813). Metabolites were aligned and inverse normalised to allow intra-platform comparison. Logistic models adjusting for covariates were performed in each cohort and results were combined using random-effect inverse-variance meta-analyses adjusting for multiple testing. We further conducted canonical pathway analysis to investigate the pathways underlying the hypertension-associated metabolites. In 12,479 hypertensive cases and 31,827 controls without renal impairment, we identified 38 metabolites, associated with hypertension after adjusting for age, sex, body mass index, ethnicity, and multiple testing. Of these, 32 metabolite associations, predominantly lipid (steroids and fatty acyls) and organic acids (amino-, hydroxy-, and keto-acids) remained after further adjusting for comorbidities and dietary intake. Among the identified metabolites, 5 were novel, including 2 bile acids, 2 glycerophospholipids, and ketoleucine. Pathway analysis further implicates the role of the amino-acids, serine/glycine, and bile acids in hypertension regulation. In the largest cross-sectional hypertension-metabolomics study to date, we identify 32 circulating metabolites (of which 5 novel and 27 confirmed) that are potentially actionable targets for intervention. Further in-vivo studies are needed to identify their specific role in the aetiology or progression of hypertension.
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Affiliation(s)
- Panayiotis Louca
- Department of Twin Research, King’s College London, London SE1 7EH, UK; (P.L.); (A.N.); (M.M.)
| | - Ana Nogal
- Department of Twin Research, King’s College London, London SE1 7EH, UK; (P.L.); (A.N.); (M.M.)
| | - Aurélie Moskal
- Nutrition and Metabolism Section, International Agency for Research on Cancer, 69372 Lyon, France; (A.M.); (M.J.G.)
| | - Neil J. Goulding
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK; (N.J.G.); (Y.B.-S.); (D.A.L.)
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK
| | - Martin J. Shipley
- Department Epidemiology and Public Health, University College London, London WC1E 7HB, UK; (M.J.S.); (J.V.L.); (M.K.)
| | - Taryn Alkis
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center, Houston, TX 77030, USA; (T.A.); (B.Y.)
| | - Joni V. Lindbohm
- Department Epidemiology and Public Health, University College London, London WC1E 7HB, UK; (M.J.S.); (J.V.L.); (M.K.)
- Clinicum, Department of Public Health, University of Helsinki, P.O. Box 20 Helsinki, Finland
| | - Jie Hu
- Division of Women’s Health, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA; (J.H.); (K.M.R.)
| | - Domagoj Kifer
- Faculty of Pharmacy and Biochemistry, University of Zagreb, 10000 Zagreb, Croatia;
| | - Ni Wang
- Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, 2820 Gentofte, Denmark; (N.W.); (B.C.)
- Department of Biotechnology and Biomedicine, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
| | - Bo Chawes
- Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, 2820 Gentofte, Denmark; (N.W.); (B.C.)
| | - Kathryn M. Rexrode
- Division of Women’s Health, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA; (J.H.); (K.M.R.)
| | - Yoav Ben-Shlomo
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK; (N.J.G.); (Y.B.-S.); (D.A.L.)
- NIHR Applied Research Collaboration West, University Hospitals Bristol and Weston National Health Service Foundation Trust, Bristol BS1 2NT, UK
| | - Mika Kivimaki
- Department Epidemiology and Public Health, University College London, London WC1E 7HB, UK; (M.J.S.); (J.V.L.); (M.K.)
| | - Rachel A. Murphy
- School of Population and Public Health, University of British Columbia, Vancouver, BC V6T 1Z3, Canada;
- Cancer Control Research, BC Cancer, Vancouver, BC V5Z 1G1, Canada
| | - Bing Yu
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center, Houston, TX 77030, USA; (T.A.); (B.Y.)
| | - Marc J. Gunter
- Nutrition and Metabolism Section, International Agency for Research on Cancer, 69372 Lyon, France; (A.M.); (M.J.G.)
| | - Karsten Suhre
- Department of Biophysics and Physiology, Weill Cornell Medicine-Qatar, Doha 24144, Qatar;
| | - Deborah A. Lawlor
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK; (N.J.G.); (Y.B.-S.); (D.A.L.)
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK
- Bristol NIHR Biomedical Research Centre, University of Bristol, Bristol BS1 2NT, UK
| | - Massimo Mangino
- Department of Twin Research, King’s College London, London SE1 7EH, UK; (P.L.); (A.N.); (M.M.)
- NIHR Biomedical Research Centre at Guy’s and St Thomas’ Foundation Trust, London SE1 9RT, UK
| | - Cristina Menni
- Department of Twin Research, King’s College London, London SE1 7EH, UK; (P.L.); (A.N.); (M.M.)
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