The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Distinguishing features of long COVID identified through immune profiling
Open accessAbstract
Post-acute infection syndromes may develop after acute viral disease 1 . Infection with SARS-CoV-2 can result in the development of a post-acute infection syndrome known as long COVID. Individuals with long COVID frequently report unremitting fatigue, post-exertional malaise, and a variety of cognitive and autonomic dysfunctions 2–4 . However, the biological processes that are associated with the development and persistence of these symptoms are unclear. Here 275 individuals with or without long COVID were enrolled in a cross-sectional study that included multidimensional immune phenotyping and unbiased machine learning methods to identify biological features associated with long COVID. Marked differences were noted in circulating myeloid and lymphocyte populations relative to the matched controls, as well as evidence of exaggerated humoral responses directed against SARS-CoV-2 among participants with long COVID. Furthermore, higher antibody responses directed against non-SARS-CoV-2 viral pathogens were observed among individuals with long COVID, particularly Epstein–Barr virus. Levels of soluble immune mediators and hormones varied among groups, with cortisol levels being lower among participants with long COVID. Integration of immune phenotyping data into unbiased machine learning models identified the key features that are most strongly associated with long COVID status. Collectively, these findings may help to guide future studies into the pathobiology of long COVID and help with developing relevant biomarkers.
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1 retracted
RETRACTED: 6-month consequences of COVID-19 in patients discharged from hospital: a cohort study
Huang, C., Huang, L., Wang, Y., Li, X., Ren, L., Gu, X., Kang, L., Guo, L., Liu, M., Zhou, X., et al. (2021) · The Lancet · doi:10.1016/s0140-6736(20)32656-8 · Cited by 4,084
RetractedHarris, P. A., Taylor, R., Thielke, R., Payne, J., Gonzalez, N., & Conde, J. G. (2009) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2008.08.010 · Cited by 51,384
A Brief Measure for Assessing Generalized Anxiety Disorder: The GAD-7
Spitzer, R. L., Kroenke, K., Williams, J. B. W., & Löwe, B. (2006) · Archives of Internal Medicine · doi:10.1001/archinte.166.10.1092 · Cited by 31,765
The REDCap consortium: building an international community of software platform partners
Harris, P. A., Taylor, R., Minor, B. L., Elliott, V., Fernandez, M., O'Neal, L., McLeod, L., Delacqua, G., Delacqua, F., Kirby, J., et al. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103208 · Cited by 24,013
DeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L. (1988) · Biometrics · doi:10.2307/2531595 · Cited by 22,649
pROC: an open-source package for R and S+ to analyze and compare ROC curves
Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., & Müller, M. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-77 · Cited by 13,697
An interactive web-based dashboard to track COVID-19 in real time
Dong, E., Du, H., & Gardner, L. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30120-1 · Cited by 10,048
Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L)
Herdman, M., Gudex, C., Lloyd, A., Janssen, M., Kind, P., Parkin, D., Bonsel, G., & Badia, X. (2011) · Quality of Life Research · doi:10.1007/s11136-011-9903-x · Cited by 9,795
College Admissions and the Stability of Marriage
Gale, D., & Shapley, L. S. (1962) · The American Mathematical Monthly · doi:10.1080/00029890.1962.11989827 · Cited by 5,985
Nalbandian, A., Sehgal, K., Gupta, A., Madhavan, M. V., McGroder, C., Stevens, J. S., Cook, J. R., Nordvig, A. S., Shalev, D., Sehrawat, T. S., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01283-z · Cited by 4,862
Cited by
Long COVID: major findings, mechanisms and recommendations
Davis, H. E., McCorkell, L., Vogel, J. M., & Topol, E. J. (2023) · Nature Reviews Microbiology · doi:10.1038/s41579-022-00846-2 · Cited by 4,153
Long COVID science, research and policy
Al-Aly, Z., Davis, H., McCorkell, L., Soares, L., Wulf-Hanson, S., Iwasaki, A., & Topol, E. J. (2024) · Nature Medicine · doi:10.1038/s41591-024-03173-6 · Cited by 538
Self-citationThe neurobiology of long COVID
Monje, M., & Iwasaki, A. (2022) · Neuron · doi:10.1016/j.neuron.2022.10.006 · Cited by 415
Self-citationYin, K., Peluso, M. J., Luo, X., Thomas, R., Shin, M.-G., Neidleman, J., Andrew, A., Young, K. C., Ma, T., Hoh, R., et al. (2024) · Nature Immunology · doi:10.1038/s41590-023-01724-6 · Cited by 394
Altmann, D. M., Whettlock, E. M., Liu, S., Arachchillage, D. J., & Boyton, R. J. (2023) · Nature Reviews Immunology · doi:10.1038/s41577-023-00904-7 · Cited by 389
Metadata
- Authors
- Klein, J., Wood, J., Jaycox, J. R., Dhodapkar, R. M., Lu, P., Gehlhausen, J. R., Tabachnikova, A., Greene, K., Tabacof, L., Malik, A. A., Silva Monteiro, V., Silva, J., Kamath, K., Zhang, M., Dhal, A., Ott, I. M., Valle, G., Peña-Hernández, M., Mao, T., Bhattacharjee, B., Takahashi, T., Lucas, C., Song, E., McCarthy, D., Breyman, E., Tosto-Mancuso, J., Dai, Y., Perotti, E., Akduman, K., Tzeng, T. J., Xu, L., Geraghty, A. C., Monje, M., Yildirim, I., Shon, J., Medzhitov, R., Lutchmansingh, D., Possick, J. D., Kaminski, N., Omer, S. B., Krumholz, H. M., Guan, L., Dela Cruz, C. S., van Dijk, D., Ring, A. M., Putrino, D., & Iwasaki, A.
- Venue
- Nature
- Published
- 2023-09-25
- Volume
- 623
- Issue
- 7985
- Pages
- 139-148
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W4387012832
- Cited by
- 806
- References
- 28
- FWCI
- 114.40
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Klein, J., Wood, J., Jaycox, J. R., Dhodapkar, R. M., Lu, P., Gehlhausen, J. R., Tabachnikova, A., Greene, K., Tabacof, L., Malik, A. A., Silva Monteiro, V., Silva, J., Kamath, K., Zhang, M., Dhal, A., Ott, I. M., Valle, G., Peña-Hernández, M., Mao, T., . . . Iwasaki, A. (2023). Distinguishing features of long COVID identified through immune profiling. Nature. https://doi.org/10.1038/s41586-023-06651-y
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
