1
|
Mutie PM, Pomares-Milan H, Atabaki-Pasdar N, Coral D, Fitipaldi H, Tsereteli N, Tajes JF, Franks PW, Giordano GN. Investigating the causal relationships between excess adiposity and cardiometabolic health in men and women. Diabetologia 2023; 66:321-335. [PMID: 36221008 PMCID: PMC9807546 DOI: 10.1007/s00125-022-05811-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 08/23/2022] [Indexed: 01/07/2023]
Abstract
AIMS/HYPOTHESIS Excess adiposity is differentially associated with increased risk of cardiometabolic disease in men and women, according to observational studies. Causal inference studies largely assume a linear relationship between BMI and cardiometabolic outcomes, which may not be the case. In this study, we investigated the shapes of the causal relationships between BMI and cardiometabolic diseases and risk factors. We further investigated sex differences within the causal framework. METHODS To assess causal relationships between BMI and the outcomes, we used two-stage least-squares Mendelian randomisation (MR), with a polygenic risk score for BMI as the instrumental variable. To elucidate the shapes of the causal relationships, we used a non-linear MR fractional polynomial method, and used piecewise MR to investigate threshold relationships and confirm the shapes. RESULTS BMI was associated with type 2 diabetes (OR 3.10; 95% CI 2.73, 3.53), hypertension (OR 1.53; 95% CI 1.44, 1.62) and coronary artery disease (OR 1.20; 95% CI 1.08, 1.33), but not chronic kidney disease (OR 1.08; 95% CI 0.67, 1.72) or stroke (OR 1.08; 95% CI 0.92, 1.28). The data suggest that these relationships are non-linear. For cardiometabolic risk factors, BMI was positively associated with glucose, HbA1c, triacylglycerol levels and both systolic and diastolic BP. BMI had an inverse causal relationship with total cholesterol, LDL-cholesterol and HDL-cholesterol. The data suggest a non-linear causal relationship between BMI and BP and other biomarkers (p<0.001) except lipoprotein A. The piecewise MR results were consistent with the fractional polynomial results. The causal effect of BMI on coronary artery disease, total cholesterol and LDL-cholesterol was different in men and women, but this sex difference was only significant for LDL-cholesterol after controlling for multiple testing (p<0.001). Further, the causal effect of BMI on coronary artery disease varied by menopause status in women. CONCLUSIONS/INTERPRETATION We describe the shapes of causal effects of BMI on cardiometabolic diseases and risk factors, and report sex differences in the causal effects of BMI on LDL-cholesterol. We found evidence of non-linearity in the causal effect of BMI on diseases and risk factor biomarkers. Reducing excess adiposity is highly beneficial for health, but there is greater need to consider biological sex in the management of adiposity.
Collapse
Affiliation(s)
- Pascal M Mutie
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Clinical Research Centre, Lund University, Lund, Sweden
| | - Hugo Pomares-Milan
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Clinical Research Centre, Lund University, Lund, Sweden
| | - Naeimeh Atabaki-Pasdar
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Clinical Research Centre, Lund University, Lund, Sweden
| | - Daniel Coral
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Clinical Research Centre, Lund University, Lund, Sweden
| | - Hugo Fitipaldi
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Clinical Research Centre, Lund University, Lund, Sweden
| | - Neli Tsereteli
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Clinical Research Centre, Lund University, Lund, Sweden
| | - Juan Fernandez Tajes
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Clinical Research Centre, Lund University, Lund, Sweden
| | - Paul W Franks
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Clinical Research Centre, Lund University, Lund, Sweden.
- Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Giuseppe N Giordano
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Clinical Research Centre, Lund University, Lund, Sweden
| |
Collapse
|
2
|
Coral DE, Fernandez-Tajes J, Tsereteli N, Pomares-Millan H, Fitipaldi H, Mutie PM, Atabaki-Pasdar N, Kalamajski S, Poveda A, Miller-Fleming TW, Zhong X, Giordano GN, Pearson ER, Cox NJ, Franks PW. A phenome-wide comparative analysis of genetic discordance between obesity and type 2 diabetes. Nat Metab 2023; 5:237-247. [PMID: 36703017 PMCID: PMC9970876 DOI: 10.1038/s42255-022-00731-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 12/20/2022] [Indexed: 01/27/2023]
Abstract
Obesity and type 2 diabetes are causally related, yet there is considerable heterogeneity in the consequences of both conditions and the mechanisms of action are poorly defined. Here we show a genetic-driven approach defining two obesity profiles that convey highly concordant and discordant diabetogenic effects. We annotate and then compare association signals for these profiles across clinical and molecular phenotypic layers. Key differences are identified in a wide range of traits, including cardiovascular mortality, fat distribution, liver metabolism, blood pressure, specific lipid fractions and blood levels of proteins involved in extracellular matrix remodelling. We find marginal differences in abundance of Bacteroidetes and Firmicutes bacteria in the gut. Instrumental analyses reveal prominent causal roles for waist-to-hip ratio, blood pressure and cholesterol content of high-density lipoprotein particles in the development of diabetes in obesity. We prioritize 17 genes from the discordant signature that convey protection against type 2 diabetes in obesity, which may represent logical targets for precision medicine approaches.
Collapse
Affiliation(s)
- Daniel E Coral
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden.
| | - Juan Fernandez-Tajes
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Neli Tsereteli
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Hugo Pomares-Millan
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Hugo Fitipaldi
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Pascal M Mutie
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Naeimeh Atabaki-Pasdar
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Sebastian Kalamajski
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Alaitz Poveda
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Tyne W Miller-Fleming
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xue Zhong
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Giuseppe N Giordano
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Ewan R Pearson
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
- Population Health and Genomics, University of Dundee, Dundee, UK
| | - Nancy J Cox
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Paul W Franks
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden.
- Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| |
Collapse
|
3
|
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.
Collapse
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.
| |
Collapse
|
4
|
Sudre CH, Murray B, Varsavsky T, Graham MS, Penfold RS, Bowyer RC, Pujol JC, Klaser K, Antonelli M, Canas LS, Molteni E, Modat M, Jorge Cardoso M, May A, Ganesh S, Davies R, Nguyen LH, Drew DA, Astley CM, Joshi AD, Merino J, Tsereteli N, Fall T, Gomez MF, Duncan EL, Menni C, Williams FMK, Franks PW, Chan AT, Wolf J, Ourselin S, Spector T, Steves CJ. Author Correction: Attributes and predictors of long COVID. Nat Med 2021; 27:1116. [PMID: 34045738 DOI: 10.1038/s41591-021-01361-2] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Carole H Sudre
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.,MRC Unit for Lifelong Health and Ageing, Department of Population Health Sciences, University College London, London, UK.,Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Benjamin Murray
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Thomas Varsavsky
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Mark S Graham
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Rose S Penfold
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | | | | | - Kerstin Klaser
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Michela Antonelli
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Liane S Canas
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Erika Molteni
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - M Jorge Cardoso
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | | | | | | | - Long H Nguyen
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - David A Drew
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Christina M Astley
- Division of Endocrinology & Computational Epidemiology, Boston Children's Hospital, Boston, MA, USA
| | - Amit D Joshi
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Jordi Merino
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.,Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA.,Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Neli Tsereteli
- Lund University Diabetes Centre, Department of Clinical Sciences, Malmö, Sweden
| | - Tove Fall
- Molecular Epidemiology, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Maria F Gomez
- Lund University Diabetes Centre, Department of Clinical Sciences, Malmö, Sweden
| | - Emma L Duncan
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Cristina Menni
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Frances M K Williams
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Paul W Franks
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.,Lund University Diabetes Centre, Department of Clinical Sciences, Malmö, Sweden
| | - Andrew T Chan
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | | | - Sebastien Ourselin
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.,AI Institute '3IA Côte d'Azur', Université Côte d'Azur, Nice, France
| | - Tim Spector
- 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.
| |
Collapse
|
5
|
Molteni E, Astley CM, Ma W, Sudre CH, Magee LA, Murray B, Fall T, Gomez MF, Tsereteli N, Franks PW, Brownstein JS, Davies R, Wolf J, Spector TD, Ourselin S, Steves CJ, Chan AT, Modat M. Symptoms and syndromes associated with SARS-CoV-2 infection and severity in pregnant women from two community cohorts. Sci Rep 2021; 11:6928. [PMID: 33767292 PMCID: PMC7994587 DOI: 10.1038/s41598-021-86452-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 03/12/2021] [Indexed: 01/10/2023] Open
Abstract
We tested whether pregnant and non-pregnant women differ in COVID-19 symptom profile and severity, and we extended previous investigations on hospitalized pregnant women to those who did not require hospitalization. Two female community-based cohorts (18-44 years) provided longitudinal (smartphone application, N = 1,170,315, n = 79 pregnant tested positive) and cross-sectional (web-based survey, N = 1,344,966, n = 134 pregnant tested positive) data, prospectively collected through self-participatory citizen surveillance in UK, Sweden and USA. Pregnant and non-pregnant were compared for frequencies of events, including SARS-CoV-2 testing, symptoms and hospitalization rates. Multivariable regression was used to investigate symptoms severity and comorbidity effects. Pregnant and non-pregnant women positive for SARS-CoV-2 infection were not different in syndromic severity, except for gastrointestinal symptoms. Pregnant were more likely to have received testing, despite reporting fewer symptoms. Pre-existing lung disease was most closely associated with syndromic severity in pregnant hospitalized. Heart and kidney diseases and diabetes increased risk. The most frequent symptoms among non-hospitalized women were anosmia [63% pregnant, 92% non-pregnant] and headache [72%, 62%]. Cardiopulmonary symptoms, including persistent cough [80%] and chest pain [73%], were more frequent among pregnant who were hospitalized. Consistent with observations in non-pregnant populations, lung disease and diabetes were associated with increased risk of more severe SARS-CoV-2 infection during pregnancy.
Collapse
Affiliation(s)
- Erika Molteni
- School of Biomedical Engineering and Imaging Sciences, King's College London, 9th floor, Becket House, 1 Lambeth Palace Road, London, SE1 7EU, UK.
| | | | - Wenjie Ma
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Carole H Sudre
- School of Biomedical Engineering and Imaging Sciences, King's College London, 9th floor, Becket House, 1 Lambeth Palace Road, London, SE1 7EU, UK
| | - Laura A Magee
- Department of Women and Children's Health, School of Life Course Sciences and the Institute of Women and Children's Health, King's College London, London, UK
| | - Benjamin Murray
- School of Biomedical Engineering and Imaging Sciences, King's College London, 9th floor, Becket House, 1 Lambeth Palace Road, London, SE1 7EU, UK
| | - Tove Fall
- Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Maria F Gomez
- Department of Clinical Sciences, Lund University Diabetes Centre, Jan Waldenströms gata 35, 21428, Malmö, Sweden
| | - Neli Tsereteli
- Department of Clinical Sciences, Lund University Diabetes Centre, Jan Waldenströms gata 35, 21428, Malmö, Sweden
| | - Paul W Franks
- Department of Clinical Sciences, Lund University Diabetes Centre, Jan Waldenströms gata 35, 21428, Malmö, Sweden
| | - John S Brownstein
- Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | | | | | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, 9th floor, Becket House, 1 Lambeth Palace Road, London, SE1 7EU, UK
| | - Claire J Steves
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Andrew T Chan
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Marc Modat
- School of Biomedical Engineering and Imaging Sciences, King's College London, 9th floor, Becket House, 1 Lambeth Palace Road, London, SE1 7EU, UK
| |
Collapse
|
6
|
Sudre CH, Murray B, Varsavsky T, Graham MS, Penfold RS, Bowyer RC, Pujol JC, Klaser K, Antonelli M, Canas LS, Molteni E, Modat M, Jorge Cardoso M, May A, Ganesh S, Davies R, Nguyen LH, Drew DA, Astley CM, Joshi AD, Merino J, Tsereteli N, Fall T, Gomez MF, Duncan EL, Menni C, Williams FMK, Franks PW, Chan AT, Wolf J, Ourselin S, Spector T, Steves CJ. Attributes and predictors of long COVID. Nat Med 2021; 27:626-631. [PMID: 33692530 DOI: 10.1038/s41591-021-01292-y] [Citation(s) in RCA: 1235] [Impact Index Per Article: 411.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 02/19/2021] [Indexed: 02/07/2023]
Abstract
Reports of long-lasting coronavirus disease 2019 (COVID-19) symptoms, the so-called 'long COVID', are rising but little is known about prevalence, risk factors or whether it is possible to predict a protracted course early in the disease. We analyzed data from 4,182 incident cases of COVID-19 in which individuals self-reported their symptoms prospectively in the COVID Symptom Study app1. A total of 558 (13.3%) participants reported symptoms lasting ≥28 days, 189 (4.5%) for ≥8 weeks and 95 (2.3%) for ≥12 weeks. Long COVID was characterized by symptoms of fatigue, headache, dyspnea and anosmia and was more likely with increasing age and body mass index and female sex. Experiencing more than five symptoms during the first week of illness was associated with long COVID (odds ratio = 3.53 (2.76-4.50)). A simple model to distinguish between short COVID and long COVID at 7 days (total sample size, n = 2,149) showed an area under the curve of the receiver operating characteristic curve of 76%, with replication in an independent sample of 2,472 individuals who were positive for severe acute respiratory syndrome coronavirus 2. This model could be used to identify individuals at risk of long COVID for trials of prevention or treatment and to plan education and rehabilitation services.
Collapse
Affiliation(s)
- Carole H Sudre
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.,MRC Unit for Lifelong Health and Ageing, Department of Population Health Sciences, University College London, London, UK.,Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Benjamin Murray
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Thomas Varsavsky
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Mark S Graham
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Rose S Penfold
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | | | | | - Kerstin Klaser
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Michela Antonelli
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Liane S Canas
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Erika Molteni
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - M Jorge Cardoso
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | | | | | | | - Long H Nguyen
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - David A Drew
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Christina M Astley
- Division of Endocrinology & Computational Epidemiology, Boston Children's Hospital, Boston, MA, USA
| | - Amit D Joshi
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Jordi Merino
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.,Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA.,Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Neli Tsereteli
- Lund University Diabetes Centre, Department of Clinical Sciences, Malmö, Sweden
| | - Tove Fall
- Molecular Epidemiology, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Maria F Gomez
- Lund University Diabetes Centre, Department of Clinical Sciences, Malmö, Sweden
| | - Emma L Duncan
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Cristina Menni
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Frances M K Williams
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Paul W Franks
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.,Lund University Diabetes Centre, Department of Clinical Sciences, Malmö, Sweden
| | - Andrew T Chan
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | | | - Sebastien Ourselin
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.,AI Institute '3IA Côte d'Azur', Université Côte d'Azur, Nice, France
| | - Tim Spector
- 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.
| |
Collapse
|
7
|
Molteni E, Astley CM, Ma W, Sudre CH, Magee LA, Murray B, Fall T, Gomez MF, Tsereteli N, Franks PW, Brownstein JS, Davies R, Wolf J, Spector TD, Ourselin S, Steves CJ, Chan AT, Modat M. SARS-CoV-2 (COVID-19) infection in pregnant women: characterization of symptoms and syndromes predictive of disease and severity through real-time, remote participatory epidemiology. medRxiv 2020. [PMID: 32839787 PMCID: PMC7444306 DOI: 10.1101/2020.08.17.20161760] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Objective: To test whether pregnant and non-pregnant women differ in COVID-19 symptom profile and severity. To extend previous investigations on hospitalized pregnant women to those who did not require hospitalization. Design: Observational study prospectively collecting longitudinal (smartphone application interface) and cross-sectional (web-based survey) data. Setting: Community-based self-participatory citizen surveillance in the United Kingdom, Sweden and the United States of America. Population: Two female community-based cohorts aged 18–44 years. The discovery cohort was drawn from 1,170,315 UK, Sweden and USA women (79 pregnant tested positive) who self-reported status and symptoms longitudinally via smartphone. The replication cohort included 1,344,966 USA women (134 pregnant tested positive) who provided cross-sectional self-reports. Methods: Pregnant and non-pregnant were compared for frequencies of symptoms and events, including SARS-CoV-2 testing and hospitalization rates. Multivariable regression was used to investigate symptoms severity and comorbidity effects. Results: Pregnant and non-pregnant women positive for SARS-CoV-2 infection were not different in syndromic severity. Pregnant were more likely to have received testing than non-pregnant, despite reporting fewer symptoms. Pre-existing lung disease was most closely associated with the syndromic severity in pregnant hospitalized women. Heart and kidney diseases and diabetes increased risk. The most frequent symptoms among all non-hospitalized women were anosmia [63% pregnant, 92% non-pregnant] and headache [72%, 62%]. Cardiopulmonary symptoms, including persistent cough [80%] and chest pain [73%], were more frequent among pregnant women who were hospitalized. Conclusions: Symptom characteristics and severity were comparable among pregnant and non-pregnant women, except for gastrointestinal symptoms. Consistent with observations in non-pregnant populations, lung disease and diabetes were associated with increased risk of more severe SARS-CoV-2 infection during pregnancy. Pregnancy with SARS-CoV-2 has no higher risk of severe symptoms. Underlying lung disease or cardiac condition can increase risk.
Collapse
Affiliation(s)
- Erika Molteni
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | | | - Wenjie Ma
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Carole H Sudre
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Laura A Magee
- Department of Women and Children's Health, School of Life Course Sciences and the Institute of Women and Children's Health, King's College London, London, United Kingdom
| | - Benjamin Murray
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Tove Fall
- Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Sweden
| | - Maria F Gomez
- Department of Clinical Sciences, Lund University Diabetes Centre, Jan Waldenströms gata 35, SE-21428, Malmo, Sweden
| | - Neli Tsereteli
- Department of Clinical Sciences, Lund University Diabetes Centre, Jan Waldenströms gata 35, SE-21428, Malmo, Sweden
| | - Paul W Franks
- Department of Clinical Sciences, Lund University Diabetes Centre, Jan Waldenströms gata 35, SE-21428, Malmo, Sweden
| | - John S Brownstein
- Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | | | | | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
| | - Sebastien Ourselin
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Claire J Steves
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
| | - Andrew T Chan
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| |
Collapse
|
8
|
Bell SA, Delpech V, Casabona J, Tsereteli N, de Wit J. Delivery of HIV test results, post-test discussion and referral in health care settings: a review of guidance for European countries. HIV Med 2015; 16:620-7. [DOI: 10.1111/hiv.12278] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/14/2015] [Indexed: 10/23/2022]
Affiliation(s)
- SA Bell
- Centre for Social Research in Health; UNSW Australia; Sydney NSW Australia
| | | | - J Casabona
- Center for STI/HIV Epidemiological Studies of Catalonia (CEEISCAT/ASPCAT) and CIBERESP; Badalona Catalonia Spain
| | - N Tsereteli
- Centre for Information and Counseling on Reproductive Health - Tanadgoma; Tbilisi Georgia
| | - J de Wit
- Centre for Social Research in Health; UNSW Australia; Sydney NSW Australia
| |
Collapse
|
9
|
Tsereteli N, Chikovani I, Chkhaidze N, Goguadze K, Shengelia N, Rukhadze N. HIV testing uptake among female sex workers and men who have sex with men in Tbilisi, Georgia. HIV Med 2014; 14 Suppl 3:29-32. [PMID: 24033900 DOI: 10.1111/hiv.12065] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/12/2013] [Indexed: 11/27/2022]
Abstract
OBJECTIVES The aim of the study was to investigate HIV testing practice among female sex workers (FSWs) and men who have sex with men (MSM) in Tbilisi, Georgia and to identify determinants of never testing behaviour among MSM. METHODS Data obtained in two rounds of bio-behavioural surveys among FSWs (2006 and 2009) and MSM (2007 and 2010) were analysed. Determinants of never testing behaviour among MSM were investigated among 278 respondents recruited in 2010 through respondent-driven sampling. RESULTS Knowledge about the availability of HIV testing and never testing behaviour did not show changes among FSWs and MSM. Every third FSW and every second MSM had never been tested for HIV according to the latest surveys in 2010. In bivariate analysis among MSM, consistent condom use during anal intercourse with a male partner in the last year, awareness of HIV testing locations and preventive programme coverage were negatively associated with never testing behaviour, while those who considered themselves at no risk of HIV transmission were more likely to have never been tested. In multivariate analysis, lower odds of never testing for HIV remained for those who were aware of HIV testing locations [adjusted odds ratio (AOR) 0.12; 95% confidence interval (CI) 0.04-0.32] and who reported being covered by HIV prevention programmes (AOR 0.26; 95% CI 0.12-0.56). CONCLUSIONS In view of the concentrated HIV epidemic among MSM in Georgia and the low rate of HIV testing uptake, interventions in this key population should take into consideration the factors associated with testing behaviour. The barriers to HIV testing and counselling uptake should be further investigated.
Collapse
Affiliation(s)
- N Tsereteli
- Center for Information and Counseling on Reproductive Health - Tanadgoma, Tbilisi
| | | | | | | | | | | |
Collapse
|