{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T13:06:11Z","timestamp":1768568771743,"version":"3.49.0"},"posted":{"date-parts":[[2025,11,4]]},"group-title":"Infectious Diseases (except HIV\/AIDS)","reference-count":50,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T00:00:00Z","timestamp":1762214400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,11,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Long COVID is clinically heterogeneous, with evolving symptom trajectories that complicate classification. Prior clustering studies often rely on Electronic Health Records data, risking underreporting. We used longitudinal, community-based data to characterize symptom clusters and risk factors.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>We analyzed CHASING COVID Cohort participants with confirmed SARS-CoV-2 infection between December 2020 and December 2022, \u226512 months of follow-up, and long COVID (\u22651 new symptom and concurrent activity limitation 3\u201312 months post-infection, both absent pre-infection). The infection in this window was the index infection; those with pre-index long COVID were excluded. Symptoms were self-reported pre-infection and at \u223c3, 6, 9, and 12 months. Missing data were handled via multiple imputation by chained equations (30 datasets). Longitudinal K-means clustering was performed within each imputed dataset, with hierarchical aggregation to derive final assignments. Multivariable logistic regression (adjusting for age, sex) assessed associations of demographic, clinical, and social factors with cluster membership. Within the highest-burden cluster, hierarchical clustering identified symptom phenotypes.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Of 1,787 infected participants, 511 met criteria (22% \u226550 years; 55% female; 60% White non-Hispanic; 54% mental health disorder; 7.2% immunodeficiency; 21% \u22652 comorbidities; 30% prior infection; 24% never vaccinated pre-index). Three clusters emerged: highest, moderate, and lowest burden. The highest-burden cluster (median 6 symptoms at 6 and 9 months) was characterized by fatigue (57%\u201374%), concentration difficulty, post-exertional malaise, myalgia, sleep disturbance, gastrointestinal symptoms, headache, irritability, and mobility limitations (each \u223c46%\u201353%). The moderate cluster peaked at 3 symptoms (fatigue 42%); the lowest remained \u223c1 symptom (fatigue 24% at 12 months). Older age (aOR 2.68, 95% CI 1.59\u20134.53), female sex (2.36, 1.48\u20133.76), mental health disorder (2.65, 1.65\u20134.25), and immunodeficiency (4.23, 1.71\u201310.51) were associated with highest vs lowest burden. Within the highest-burden cluster, three phenotypes emerged: neurological\/multisystemic, psychiatric\/neurological, and physical\/respiratory.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>Distinct long-COVID clusters and phenotypes underscore heterogeneity and support tailored management and risk stratification.<\/jats:p>\n                <\/jats:sec>","DOI":"10.1101\/2025.11.01.25339283","type":"posted-content","created":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T21:15:19Z","timestamp":1762290919000},"source":"Crossref","is-referenced-by-count":0,"title":["Long COVID Longitudinal Symptoms Burden Clusters Within A National Community-Based Cohort"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9703-4412","authenticated-orcid":false,"given":"Yanhan","family":"Shen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zach","family":"Shahn","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8426-6572","authenticated-orcid":false,"given":"McKaylee M.","family":"Robertson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kelly","family":"Gebo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Denis","family":"Nash","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"the CHASING COVID Cohort Study Team","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2025110615151264000_2025.11.01.25339283v1.1","doi-asserted-by":"publisher","DOI":"10.1038\/s41579-022-00846-2"},{"key":"2025110615151264000_2025.11.01.25339283v1.2","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-021-01283-z"},{"key":"2025110615151264000_2025.11.01.25339283v1.3","doi-asserted-by":"publisher","DOI":"10.3389\/fmicb.2021.698169"},{"key":"2025110615151264000_2025.11.01.25339283v1.4","doi-asserted-by":"publisher","DOI":"10.1016\/j.tem.2023.03.002"},{"key":"2025110615151264000_2025.11.01.25339283v1.5","doi-asserted-by":"crossref","first-page":"842306","DOI":"10.3389\/frai.2022.842306","article-title":"Phenotype clustering in health care: A narrative review for clinicians","volume":"5","year":"2022","journal-title":"Front Artif Intell"},{"issue":"103722","key":"2025110615151264000_2025.11.01.25339283v1.6","first-page":"103722","article-title":"Characterizing long COVID: Deep phenotype of a complex condition","volume":"74","year":"2021","journal-title":"EBioMedicine"},{"key":"2025110615151264000_2025.11.01.25339283v1.7","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2023.8823"},{"key":"2025110615151264000_2025.11.01.25339283v1.8","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0286588"},{"key":"2025110615151264000_2025.11.01.25339283v1.9","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2023.1227883"},{"key":"2025110615151264000_2025.11.01.25339283v1.10","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0275274"},{"issue":"104208","key":"2025110615151264000_2025.11.01.25339283v1.11","first-page":"104208","article-title":"Phenotypes of disease severity in a cohort of hospitalized COVID-19 patients: Results from the IMPACC study","volume":"83","year":"2022","journal-title":"EBioMedicine"},{"issue":"11","key":"2025110615151264000_2025.11.01.25339283v1.12","doi-asserted-by":"crossref","first-page":"3617","DOI":"10.3390\/jcm12113617","article-title":"Clustering analysis identified three long COVID phenotypes and their association with general health status and working ability","volume":"12","year":"2023","journal-title":"J Clin Med"},{"key":"2025110615151264000_2025.11.01.25339283v1.13","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2022.18931"},{"key":"2025110615151264000_2025.11.01.25339283v1.14","doi-asserted-by":"crossref","unstructured":"Everitt BS , Landau S , Leese M , Stahl D. 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