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      Distinguishing features of long COVID identified through immune profiling.

      Author(s): 1 , 2 , 1 , 1 , 3 , 1 , 1 , 4 , 1 , 1 , 2 , 5 , 1 , 1 , 6 , 6 , 6 , 1 , 7 , 1 , 8 , 1 , 1 , 1 , 1 , 9 , 1 , 2 , 2 , 2 , 1 , 1 , 1 , 1 , 1 , 10 , 10 , 11 , 5 , 9 , 12 , 13 , 6 , 1 , 9 , 11 , 7 , 7 , 7 , 5 , 9 , 13 , 14 , 9 , 15 , 16 , 17 , 9 , 18 , 7 , 9 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28
      Publication date (Electronic):
      Journal: Nature
      Publisher: Springer Science and Business Media LLC

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          Abstract

          Post-acute infection syndromes may develop after acute viral disease1. 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 dysfunctions2-4. 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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          Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support.

          Paul A. Harris, Robert Taylor, Robert Thielke … (2009)
          Research electronic data capture (REDCap) is a novel workflow methodology and software solution designed for rapid development and deployment of electronic data capture tools to support clinical and translational research. We present: (1) a brief description of the REDCap metadata-driven software toolset; (2) detail concerning the capture and use of study-related metadata from scientific research teams; (3) measures of impact for REDCap; (4) details concerning a consortium network of domestic and international institutions collaborating on the project; and (5) strengths and limitations of the REDCap system. REDCap is currently supporting 286 translational research projects in a growing collaborative network including 27 active partner institutions.
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            edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

            Mark Robinson, Davis J. McCarthy, Gordon K. Smyth (2009)
            Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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              A brief measure for assessing generalized anxiety disorder: the GAD-7.

              Robert L Spitzer, Kurt Kroenke, Janet B W Williams … (2006)
              Generalized anxiety disorder (GAD) is one of the most common mental disorders; however, there is no brief clinical measure for assessing GAD. The objective of this study was to develop a brief self-report scale to identify probable cases of GAD and evaluate its reliability and validity. A criterion-standard study was performed in 15 primary care clinics in the United States from November 2004 through June 2005. Of a total of 2740 adult patients completing a study questionnaire, 965 patients had a telephone interview with a mental health professional within 1 week. For criterion and construct validity, GAD self-report scale diagnoses were compared with independent diagnoses made by mental health professionals; functional status measures; disability days; and health care use. A 7-item anxiety scale (GAD-7) had good reliability, as well as criterion, construct, factorial, and procedural validity. A cut point was identified that optimized sensitivity (89%) and specificity (82%). Increasing scores on the scale were strongly associated with multiple domains of functional impairment (all 6 Medical Outcomes Study Short-Form General Health Survey scales and disability days). Although GAD and depression symptoms frequently co-occurred, factor analysis confirmed them as distinct dimensions. Moreover, GAD and depression symptoms had differing but independent effects on functional impairment and disability. There was good agreement between self-report and interviewer-administered versions of the scale. The GAD-7 is a valid and efficient tool for screening for GAD and assessing its severity in clinical practice and research.
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                Author and article information

                Journal
                Journal ID (iso-abbrev): Nature
                Title: Nature
                Publisher: Springer Science and Business Media LLC
                ISSN (Electronic): 1476-4687
                ISSN (Print): 0028-0836
                Publication date (Electronic): Nov 2023
                Volume: 623
                Issue: 7985
                Affiliations
                [1 ] Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA.
                [2 ] Abilities Research Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
                [3 ] Department of Ophthalmology, USC Keck School of Medicine, Los Angeles, CA, USA.
                [4 ] Department of Dermatology, Yale School of Medicine, New Haven, CT, USA.
                [5 ] Yale Institute for Global Health, Yale School of Public Health, New Haven, CT, USA.
                [6 ] SerImmune, Goleta, CA, USA.
                [7 ] Department of Internal Medicine (Pulmonary, Critical Care and Sleep Medicine), Yale School of Medicine, New Haven, CT, USA.
                [8 ] Department of Microbiology, Yale School of Medicine, New Haven, CT, USA.
                [9 ] Center for Infection and Immunity, Yale School of Medicine, New Haven, CT, USA.
                [10 ] Department of Neurology and Neurological Sciences, Stanford University, Palo Alto, CA, USA.
                [11 ] Howard Hughes Medical Institute, Chevy Chase, MD, USA.
                [12 ] Department of Pediatrics (Infectious Diseases), Yale New Haven Hospital, New Haven, CT, USA.
                [13 ] Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA.
                [14 ] Department of Internal Medicine (Infectious Diseases), Yale School of Medicine, New Haven, CT, USA.
                [15 ] Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT, USA.
                [16 ] Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
                [17 ] Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA.
                [18 ] Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
                [19 ] Center for Infection and Immunity, Yale School of Medicine, New Haven, CT, USA. david.vandijk@yale.edu.
                [20 ] Department of Computer Science, Yale University, New Haven, CT, USA. david.vandijk@yale.edu.
                [21 ] Department of Internal Medicine (Cardiology), Yale School of Medicine, New Haven, CT, USA. david.vandijk@yale.edu.
                [22 ] Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA. aaron.ring@yale.edu.
                [23 ] Center for Infection and Immunity, Yale School of Medicine, New Haven, CT, USA. aaron.ring@yale.edu.
                [24 ] Abilities Research Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA. david.putrino@mountsinai.org.
                [25 ] Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, NY, USA. david.putrino@mountsinai.org.
                [26 ] Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA. akiko.iwasaki@yale.edu.
                [27 ] Center for Infection and Immunity, Yale School of Medicine, New Haven, CT, USA. akiko.iwasaki@yale.edu.
                [28 ] Howard Hughes Medical Institute, Chevy Chase, MD, USA. akiko.iwasaki@yale.edu.
                Article
                Publisher Item ID: 10.1038/s41586-023-06651-y
                DOI: 10.1038/s41586-023-06651-y
                PMC ID: 10620090
                PubMed ID: 37748514
                SO-VID: 3e5d1b6b-ed10-4225-a1a2-d62abf439d1d
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