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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: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 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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Tsereteli N, Vallat R, Fernandez-Tajes J, Delahanty LM, Ordovas JM, Drew DA, Valdes AM, Segata N, Chan AT, Wolf J, Berry SE, Walker MP, Spector TD, Franks PW. Impact of insufficient sleep on dysregulated blood glucose control under standardised meal conditions. Diabetologia 2022; 65:356-365. [PMID: 34845532 PMCID: PMC8741723 DOI: 10.1007/s00125-021-05608-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 08/03/2021] [Indexed: 12/01/2022]
Abstract
AIMS/HYPOTHESIS Sleep, diet and exercise are fundamental to metabolic homeostasis. In this secondary analysis of a repeated measures, nutritional intervention study, we tested whether an individual's sleep quality, duration and timing impact glycaemic response to a breakfast meal the following morning. METHODS Healthy adults' data (N = 953 [41% twins]) were analysed from the PREDICT dietary intervention trial. Participants consumed isoenergetic standardised meals over 2 weeks in the clinic and at home. Actigraphy was used to assess sleep variables (duration, efficiency, timing) and continuous glucose monitors were used to measure glycaemic variation (>8000 meals). RESULTS Sleep variables were significantly associated with postprandial glycaemic control (2 h incremental AUC), at both between- and within-person levels. Sleep period time interacted with meal type, with a smaller effect of poor sleep on postprandial blood glucose levels when high-carbohydrate (low fat/protein) (pinteraction = 0.02) and high-fat (pinteraction = 0.03) breakfasts were consumed compared with a reference 75 g OGTT. Within-person sleep period time had a similar interaction (high carbohydrate: pinteraction = 0.001, high fat: pinteraction = 0.02). Within- and between-person sleep efficiency were significantly associated with lower postprandial blood glucose levels irrespective of meal type (both p < 0.03). Later sleep midpoint (time deviation from midnight) was found to be significantly associated with higher postprandial glucose, in both between-person and within-person comparisons (p = 0.035 and p = 0.051, respectively). CONCLUSIONS/INTERPRETATION Poor sleep efficiency and later bedtime routines are associated with more pronounced postprandial glycaemic responses to breakfast the following morning. A person's deviation from their usual sleep pattern was also associated with poorer postprandial glycaemic control. These findings underscore sleep as a modifiable, non-pharmacological therapeutic target for the optimal regulation of human metabolic health. Trial registration ClinicalTrials.gov NCT03479866.
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Affiliation(s)
- Neli Tsereteli
- Genetic & Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Raphael Vallat
- Center for Human Sleep Science, Department of Psychology, University of California, Berkeley, CA, USA
| | - Juan Fernandez-Tajes
- Genetic & Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Linda M Delahanty
- Diabetes Center, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Jose M Ordovas
- JM-USDA-Human Nutrition Research Diabetes Center on Aging at Tufts University, Boston, MA, USA
- IMDEA-Food, Madrid, Spain
| | - David A Drew
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Ana M Valdes
- NIHR Nottingham BRC at the Nottingham University Hospitals NHS Trust and University of Nottingham, Nottingham, UK
| | - Nicola Segata
- Department of Cellular, Computational and Integrative Biology, University of Trento, Trento, Italy
- European Institute of Oncology Scientific Institute for Research, Hospitalization and Healthcare, Milan, Italy
| | - Andrew T Chan
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Sarah E Berry
- Department of Nutritional Research, Kings College London, London, UK
| | - Matthew P Walker
- Center for Human Sleep Science, Department of Psychology, University of California, Berkeley, CA, USA
| | | | - Paul W Franks
- Genetic & Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Malmö, Sweden.
- Harvard Chan School of Public Health, Boston, MA, USA.
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Pan XF, Yang JJ, Shu XO, Moore SC, Palmer ND, Guasch-Ferré M, Herrington DM, Harada S, Eliassen H, Wang TJ, Gerszten RE, Albanes D, Tzoulaki I, Karaman I, Elliott P, Zhu H, Wagenknecht LE, Zheng W, Cai H, Cai Q, Matthews CE, Menni C, Meyer KA, Lipworth LP, Ose J, Fornage M, Ulrich CM, Yu D. Associations of circulating choline and its related metabolites with cardiometabolic biomarkers: an international pooled analysis. Am J Clin Nutr 2021; 114:893-906. [PMID: 34020444 PMCID: PMC8408854 DOI: 10.1093/ajcn/nqab152] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 04/09/2021] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Choline is an essential nutrient; however, the associations of choline and its related metabolites with cardiometabolic risk remain unclear. OBJECTIVE We examined the associations of circulating choline, betaine, carnitine, and dimethylglycine (DMG) with cardiometabolic biomarkers and their potential dietary and nondietary determinants. METHODS The cross-sectional analyses included 32,853 participants from 17 studies, who were free of cancer, cardiovascular diseases, chronic kidney diseases, and inflammatory bowel disease. In each study, metabolites and biomarkers were log-transformed and standardized by means and SDs, and linear regression coefficients (β) and 95% CIs were estimated with adjustments for potential confounders. Study-specific results were combined by random-effects meta-analyses. A false discovery rate <0.05 was considered significant. RESULTS We observed moderate positive associations of circulating choline, carnitine, and DMG with creatinine [β (95% CI): 0.136 (0.084, 0.188), 0.106 (0.045, 0.168), and 0.128 (0.087, 0.169), respectively, for each SD increase in biomarkers on the log scale], carnitine with triglycerides (β = 0.076; 95% CI: 0.042, 0.109), homocysteine (β = 0.064; 95% CI: 0.033, 0.095), and LDL cholesterol (β = 0.055; 95% CI: 0.013, 0.096), DMG with homocysteine (β = 0.068; 95% CI: 0.023, 0.114), insulin (β = 0.068; 95% CI: 0.043, 0.093), and IL-6 (β = 0.060; 95% CI: 0.027, 0.094), but moderate inverse associations of betaine with triglycerides (β = -0.146; 95% CI: -0.188, -0.104), insulin (β = -0.106; 95% CI: -0.130, -0.082), homocysteine (β = -0.097; 95% CI: -0.149, -0.045), and total cholesterol (β = -0.074; 95% CI: -0.102, -0.047). In the whole pooled population, no dietary factor was associated with circulating choline; red meat intake was associated with circulating carnitine [β = 0.092 (0.042, 0.142) for a 1 serving/d increase], whereas plant protein was associated with circulating betaine [β = 0.249 (0.110, 0.388) for a 5% energy increase]. Demographics, lifestyle, and metabolic disease history showed differential associations with these metabolites. CONCLUSIONS Circulating choline, carnitine, and DMG were associated with unfavorable cardiometabolic risk profiles, whereas circulating betaine was associated with a favorable cardiometabolic risk profile. Future prospective studies are needed to examine the associations of these metabolites with incident cardiovascular events.
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Affiliation(s)
- Xiong-Fei Pan
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jae Jeong Yang
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Steven C Moore
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Nicholette D Palmer
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - David M Herrington
- Section on Cardiology, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Sei Harada
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
| | - Heather Eliassen
- Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Thomas J Wang
- Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Internal Medicine, UT Southwestern Medical Center, Dallas, TX, USA
| | - Robert E Gerszten
- Broad Institute of Harvard and Massachusetts Institute of Technology and Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
- Dementia Research Institute, Imperial College London, London, United Kingdom
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Ibrahim Karaman
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
- Dementia Research Institute, Imperial College London, London, United Kingdom
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
- Dementia Research Institute, Imperial College London, London, United Kingdom
| | - Huilian Zhu
- Department of Nutrition, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Lynne E Wagenknecht
- Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Hui Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Charles E Matthews
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Cristina Menni
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
| | - Katie A Meyer
- Department of Nutrition and Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, USA
| | - Loren P Lipworth
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jennifer Ose
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
- Huntsman Cancer Institute, Salt Lake City, UT, USA
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center, Houston, TX, USA
| | - Cornelia M Ulrich
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
- Huntsman Cancer Institute, Salt Lake City, UT, USA
| | - Danxia Yu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
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Freidin MB, Stalteri MA, Wells PM, Lachance G, Baleanu AF, Bowyer RCE, Kurilshikov A, Zhernakova A, Steves CJ, Williams FMK. An association between chronic widespread pain and the gut microbiome. Rheumatology (Oxford) 2021; 60:3727-3737. [PMID: 33331911 PMCID: PMC8328510 DOI: 10.1093/rheumatology/keaa847] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 11/12/2020] [Indexed: 01/04/2023] Open
Abstract
OBJECTIVES Chronic widespread musculoskeletal pain (CWP) is a characteristic symptom of fibromyalgia, which has been shown to be associated with an altered gut microbiome. Microbiome studies to date have not examined the milder CWP phenotype specifically nor have they explored the role of raised BMI. The aim of this study was to investigate whether the microbiome is abnormal in CWP. METHODS CWP was assessed using a standardized screening questionnaire in female volunteers from the TwinsUK cohort including 113 CWP cases and 1623 controls. The stool microbiome was characterized using 16S rRNA amplicon sequencing and amplicon sequence variants, and associations with CWP examined using linear mixed-effects models adjusting for BMI, age, diet, family relatedness and technical factors. RESULTS Alpha diversity was significantly lower in CWP cases than controls (Mann-Whitney test, P-values 2.3e-04 and 1.2e-02, for Shannon and Simpson indices respectively). The species Coprococcus comes was significantly depleted in CWP cases (Padj = 3.04e-03). A genome-wide association study (GWAS) performed for C. comes in TwinsUK followed by meta-analysis with three Dutch cohorts (total n = 3521) resulted in nine suggestive regions, with the most convincing on chromosome 4 near the TRAM1L1 gene (rs76957229, P = 7.4e-8). A Mendelian randomization study based on the results of the GWAS did not support a causal role for C. comes on the development of CWP. CONCLUSIONS We have demonstrated reduced diversity in the microbiome in CWP, indicating an involvement of the gut microbiota in CWP; prospectively the microbiome may offer therapeutic opportunities for this condition.
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Affiliation(s)
- Maxim B Freidin
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK
| | - Maria A Stalteri
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK
| | - Philippa M Wells
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK
| | - Genevieve Lachance
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK
| | - Andrei-Florin Baleanu
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's 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
| | - Alexander Kurilshikov
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Alexandra Zhernakova
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Claire J Steves
- 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
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