TY - JOUR
T1 - Profiling post-COVID-19 condition across different variants of SARS-CoV-2
T2 - a prospective longitudinal study in unvaccinated wild-type, unvaccinated alpha-variant, and vaccinated delta-variant populations
AU - Canas, Liane S.
AU - Molteni, Erika
AU - Deng, Jie
AU - Sudre, Carole H.
AU - Murray, Benjamin
AU - Kerfoot, Eric
AU - Antonelli, Michela
AU - Rjoob, Khaled
AU - Capdevila Pujol, Joan
AU - Polidori, Lorenzo
AU - May, Anna
AU - Österdahl, Marc F.
AU - Whiston, Ronan
AU - Cheetham, Nathan J.
AU - Bowyer, Vicky
AU - Spector, Tim D.
AU - Hammers, Alexander
AU - Duncan, Emma L.
AU - Ourselin, Sebastien
AU - Steves, Claire J.
AU - Modat, Marc
N1 - Funding Information:
We thank the study participants (including those who shared opinions about our work and how could we improve it to understand symptom progression and recovery) and the CSS Biobank volunteer advisory panel for their feedback on the significance of this work. This research was partly funded by the Wellcome Trust (215010/Z/18/Z), the Wellcome Engineering and Physical Sciences Research Council Centre for Medical Engineering at King's College London (WT 203148/Z/16/Z), Engineering and Physical Sciences Research Council (EP/T022205/1), and the UK Department of Health via the UK National Institute for Health and Care Research comprehensive Biomedical Research Centre award to Guy's & St Thomas's NHS Foundation Trust in partnership with King's College London and King's College Hospital NHS Foundation Trust. Further support was provided by the Chronic Disease Research Foundation (CDRF-22/2020) and the UK Department of Health via the National Core Studies, an initiative funded by UK Research and Innovation, the National Institute for Health and Care Research, and the Health and Safety Executive. The COVID-19 Longitudinal Health and Wellbeing National Core Study was funded by the UK Medical Research Council (MC_PC_20030, COV-LT-0009). The work was further supported by a grant from the UK Department of Health and Social Care to ZOE. We also acknowledge support from the UK Research and Innovation London Medical Imaging and Artificial Intelligence Centre for Value-Based Healthcare. Investigators also received support from the Wellcome Flagship Programme (WT213038/Z/18/Z and WT212904/Z/18/Z), the UK Medical Research Council, the British Heart Foundation, the UK Alzheimer's Society (AS-JF-17–011), the EU, the NIHR, and the NIHR-funded BioResource, Clinical Research Facility and Biomedical Research Centre at Guy's and St Thomas's NHS Foundation Trust (in partnership with King's College London). SO was supported by the French Government through the 3IA Côte d'Azur Investments in the Future project managed by the National Research Agency (reference number ANR-19-P3IA-0002).
Publisher Copyright:
© 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license
PY - 2023/7
Y1 - 2023/7
N2 - Background: Self-reported symptom studies rapidly increased understanding of SARS-CoV-2 during the COVID-19 pandemic and enabled monitoring of long-term effects of COVID-19 outside hospital settings. Post-COVID-19 condition presents as heterogeneous profiles, which need characterisation to enable personalised patient care. We aimed to describe post-COVID-19 condition profiles by viral variant and vaccination status. Methods: In this prospective longitudinal cohort study, we analysed data from UK-based adults (aged 18–100 years) who regularly provided health reports via the Covid Symptom Study smartphone app between March 24, 2020, and Dec 8, 2021. We included participants who reported feeling physically normal for at least 30 days before testing positive for SARS-CoV-2 who subsequently developed long COVID (ie, symptoms lasting longer than 28 days from the date of the initial positive test). We separately defined post-COVID-19 condition as symptoms that persisted for at least 84 days after the initial positive test. We did unsupervised clustering analysis of time-series data to identify distinct symptom profiles for vaccinated and unvaccinated people with post-COVID-19 condition after infection with the wild-type, alpha (B.1.1.7), or delta (B.1.617.2 and AY.x) variants of SARS-CoV-2. Clusters were then characterised on the basis of symptom prevalence, duration, demography, and previous comorbidities. We also used an additional testing sample with additional data from the Covid Symptom Study Biobank (collected between October, 2020, and April, 2021) to investigate the effects of the identified symptom clusters of post-COVID-19 condition on the lives of affected people. Findings: We included 9804 people from the COVID Symptom Study with long COVID, 1513 (15%) of whom developed post-COVID-19 condition. Sample sizes were sufficient only for analyses of the unvaccinated wild-type, unvaccinated alpha variant, and vaccinated delta variant groups. We identified distinct profiles of symptoms for post-COVID-19 condition within and across variants: four endotypes were identified for infections due to the wild-type variant (in unvaccinated people), seven for the alpha variant (in unvaccinated people), and five for the delta variant (in vaccinated people). Across all variants, we identified a cardiorespiratory cluster of symptoms, a central neurological cluster, and a multi-organ systemic inflammatory cluster. These three main clusers were confirmed in a testing sample. Gastrointestinal symptoms clustered in no more than two specific phenotypes per viral variant. Interpretation: Our unsupervised analysis identified different profiles of post-COVID-19 condition, characterised by differing symptom combinations, durations, and functional outcomes. Our classification could be useful for understanding the distinct mechanisms of post-COVID-19 condition, as well as for identification of subgroups of individuals who might be at risk of prolonged debilitation. Funding: UK Government Department of Health and Social Care, Chronic Disease Research Foundation, The Wellcome Trust, UK Engineering and Physical Sciences Research Council, UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value-Based Healthcare, UK National Institute for Health Research, UK Medical Research Council, British Heart Foundation, UK Alzheimer's Society, and ZOE.
AB - Background: Self-reported symptom studies rapidly increased understanding of SARS-CoV-2 during the COVID-19 pandemic and enabled monitoring of long-term effects of COVID-19 outside hospital settings. Post-COVID-19 condition presents as heterogeneous profiles, which need characterisation to enable personalised patient care. We aimed to describe post-COVID-19 condition profiles by viral variant and vaccination status. Methods: In this prospective longitudinal cohort study, we analysed data from UK-based adults (aged 18–100 years) who regularly provided health reports via the Covid Symptom Study smartphone app between March 24, 2020, and Dec 8, 2021. We included participants who reported feeling physically normal for at least 30 days before testing positive for SARS-CoV-2 who subsequently developed long COVID (ie, symptoms lasting longer than 28 days from the date of the initial positive test). We separately defined post-COVID-19 condition as symptoms that persisted for at least 84 days after the initial positive test. We did unsupervised clustering analysis of time-series data to identify distinct symptom profiles for vaccinated and unvaccinated people with post-COVID-19 condition after infection with the wild-type, alpha (B.1.1.7), or delta (B.1.617.2 and AY.x) variants of SARS-CoV-2. Clusters were then characterised on the basis of symptom prevalence, duration, demography, and previous comorbidities. We also used an additional testing sample with additional data from the Covid Symptom Study Biobank (collected between October, 2020, and April, 2021) to investigate the effects of the identified symptom clusters of post-COVID-19 condition on the lives of affected people. Findings: We included 9804 people from the COVID Symptom Study with long COVID, 1513 (15%) of whom developed post-COVID-19 condition. Sample sizes were sufficient only for analyses of the unvaccinated wild-type, unvaccinated alpha variant, and vaccinated delta variant groups. We identified distinct profiles of symptoms for post-COVID-19 condition within and across variants: four endotypes were identified for infections due to the wild-type variant (in unvaccinated people), seven for the alpha variant (in unvaccinated people), and five for the delta variant (in vaccinated people). Across all variants, we identified a cardiorespiratory cluster of symptoms, a central neurological cluster, and a multi-organ systemic inflammatory cluster. These three main clusers were confirmed in a testing sample. Gastrointestinal symptoms clustered in no more than two specific phenotypes per viral variant. Interpretation: Our unsupervised analysis identified different profiles of post-COVID-19 condition, characterised by differing symptom combinations, durations, and functional outcomes. Our classification could be useful for understanding the distinct mechanisms of post-COVID-19 condition, as well as for identification of subgroups of individuals who might be at risk of prolonged debilitation. Funding: UK Government Department of Health and Social Care, Chronic Disease Research Foundation, The Wellcome Trust, UK Engineering and Physical Sciences Research Council, UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value-Based Healthcare, UK National Institute for Health Research, UK Medical Research Council, British Heart Foundation, UK Alzheimer's Society, and ZOE.
UR - http://www.scopus.com/inward/record.url?scp=85163430405&partnerID=8YFLogxK
U2 - 10.1016/S2589-7500(23)00056-0
DO - 10.1016/S2589-7500(23)00056-0
M3 - Article
C2 - 37202336
AN - SCOPUS:85163430405
SN - 2589-7500
VL - 5
SP - e421-e434
JO - The Lancet Digital Health
JF - The Lancet Digital Health
IS - 7
ER -