Abstract
Objective and design
Fatigue is a prominent symptom in the general population and may follow viral infection, including SARS-CoV2 infection which causes COVID-19. Chronic fatigue lasting more than three months is the major symptom of the post-COVID syndrome (known colloquially as long-COVID). The mechanisms underlying long-COVID fatigue are unknown. We hypothesized that the development of long-COVID chronic fatigue is driven by the pro-inflammatory immune status of an individual prior to COVID-19.
Subjects and methods
We analyzed pre-pandemic plasma levels of IL-6, which plays a key role in persistent fatigue, in N = 1274 community dwelling adults from TwinsUK. Subsequent COVID-19-positive and -negative participants were categorized based on SARS-CoV-2 antigen and antibody testing. Chronic fatigue was assessed using the Chalder Fatigue Scale.
Results
COVID-19-positive participants exhibited mild disease. Chronic fatigue was a prevalent symptom among this population and significantly higher in positive vs. negative participants (17% vs 11%, respectively; p = 0.001). The qualitative nature of chronic fatigue as determined by individual questionnaire responses was similar in positive and negative participants. Pre-pandemic plasma IL-6 levels were positively associated with chronic fatigue in negative, but not positive individuals. Raised BMI was associated with chronic fatigue in positive participants.
Conclusions
Pre-existing increased IL-6 levels may contribute to chronic fatigue symptoms, but there was no increased risk in individuals with mild COVID-19 compared with uninfected individuals. Elevated BMI also increased the risk of chronic fatigue in mild COVID-19, consistent with previous reports.
Similar content being viewed by others
Avoid common mistakes on your manuscript.
Introduction
Fatigue is a common symptom of COVID-19 and is one of its most pronounced acute and post-acute clinical manifestations [1]. Long-lasting manifestations—so-called long-COVID—is a subject of intense interest. Affected individuals report fatigue along with shortness of breath, headache, and loss of sense of taste and smell [2]. Mechanisms underlying the persistence of long-COVID symptoms are yet to be determined. To date, exploration of blood-borne biomarkers following COVID-19 infection has not revealed an association with inflammatory markers or white cell count [3].
The cytokine interleukin (IL)-6 has a recognized role in fatigue development in many clinical settings, including autoimmune inflammatory arthritis (reviewed in [4]) and cancer [5]. It is an established driver of acute responses to COVID-19, and treatment with anti-IL-6 monoclonal antibodies reduces mortality in severe COVID-19 [6, 7]. IL-6 is also considered a key mediator of neuropsychiatric symptoms of long-COVID, including fatigue [8]. Finally, a Mendelian randomization study of depressive symptoms has suggested that IL-6 manifests a causal influence on fatigue, as well as sleep problems and suicidality [9].
We hypothesized that long-COVID fatigue is driven at least in part by the pre-existing immune status of an individual. We have shown previously that chronic fatigue induced by treatment with interferon-alpha (IFN-α) for chronic hepatitis B viral infection is predicted by higher baseline IL-6 and IL-10 levels, as well as an exaggerated elevation of IL-6 and IL-10, in response to treatment [10]. In addition, increasing age is a major risk factor for both illness severity and longevity after SARS-CoV2 infection [2]. Aging has been associated with elevated levels of pro-inflammatory cytokines, such as TNF-α and IL-6, and reduced levels of anti-inflammatory mediator IL-10 [11, 12]. An age-related, chronic, pro-inflammatory milieu may mediate the risk for susceptibility to COVID-19 and long-COVID fatigue. To test this hypothesis, we analyzed levels of pro-inflammatory cytokine IL-6 in plasma collected prior to the SARS-CoV-2 pandemic in a longitudinal cohort sample of UK adults, who were assessed during the pandemic for chronic fatigue symptoms and SARS-CoV-2 infection.
Materials and methods
Sample and phenotyping
Participants were selected from the UK Twin Registry (TwinsUK), an adult cohort which has been shown to be representative of the general population for lifestyle and health-related traits [13]. Ethics permission was obtained and participants have provided fully informed consent; the Declaration of Helsinki was adhered to. The registry comprises 14,500 same sex mono- and dizygotic twin volunteers from the general UK population recruited from previous twin registers and national media campaigns. The cohort is predominantly female (83%), and mainly of Northern European descent [14]. Samples and data for this project were collected as part of ongoing research initiatives into inflammaging as well as more recent research into COVID-19. Participant selection and grouping is depicted on Fig. 1. Participants (n = 5755) were invited to complete a COVID-19 Personal Experience (CoPE) Questionnaire which included questions about COVID-19 infection, related symptoms and fatigue over the previous three months [15]. CoPE was completed in multiple waves during the pandemic: April 2020, August 2020, November 2020, and April 2021. Participants also provided serum for COVID-19 antigen and antibody testing at multiple points during the pandemic, with primary collections of n = 506 in April-June 2020, n = 5165 in August 2020, n = 137 in November–December 2020, and n = 4291 in April–May 2021.
We followed guidance on interpretation of antibody test results from the Centres for Disease Control and Prevention (https://www.cdc.gov/coronavirus/2019-ncov/lab/resources/antibody-tests-guidelines.html) to assign natural COVID-19 infection status from the results of swab antigen tests; and enzyme-linked immunosorbent assays (ELISA) anti-Nucleocapsid (anti-N) and anti-Spike (anti-S) Roche antibody tests [16] performed at both King’s College London and 3rd-party laboratories as described previously [17]. Individuals with a positive antigen test at any point over the CoPE questionnaire administration period, who were positive for anti-S antibodies prior to self-reported COVID-19 vaccination date, or positive for anti-N antibodies at any point, were classified as COVID-19-positive cases. Individuals with negative antigen or anti-N antibody results, or negative anti-S antibody results before COVID-19 vaccination, were classified as COVID-19 negative, or controls. Those with negative anti-N antibody and negative antigen results but positive anti-S after COVID-19 vaccination were excluded from the analysis as anti-S antibodies are generated in response to vaccination. Individuals with no laboratory antigen or antibody test results were also excluded from the analysis.
Within the CoPE questionnaire, the Chalder Fatigue Scale (CFQ) [18] was used to classify participants into those experiencing chronic fatigue and those who did not. CFQ comprises 11 questions concerning physical and mental aspects of fatigue. Reliability of CFQ has been shown in clinical and non-clinical settings [18, 19]. CFQ responses were extracted from the CoPE questionnaires administered in August and November 2020. Responses were coded as 0 (“Less than usual”, “No more than usual”) or 1 (“More than usual”, “Much more than usual”) followed by summing the scores for different questions and assigning a diagnosis of fatigue to those with summary score of 4 or more [19]. Volunteers reporting fatigue at both time points were diagnosed as having chronic fatigue because it lasted 3 months or longer. Those who reported fatigue at a single time point were removed from the study.
Pre-pandemic IL-6 levels were ascertained using plasma specimens obtained in 1997–2018 (median 2009). Samples were assayed using Olink Target 96 Inflammation assay (https://www.olink.com/products-services/target/inflammation/). Where multiple plasma specimens were available, we selected the most recent, pre-pandemic specimen. IL-6 levels were expressed as normalized protein expression on the Olink arbitrary unit in log2 scale.
Statistical analysis
A generalized mixed-effects model with chronic fatigue as a dichotomous categorical response variable and IL-6 levels as a predictor was examined, adjusting for age, body mass index (BMI), and sex as fixed effects. Family structure and zygosity were considered as random factors. IL-6 levels were adjusted for age and BMI followed by transformation of the residuals to a normal distribution using qqnorm function in R statistical environment. The model was fitted for COVID-19-positive and COVID-19-negative participants separately. A set of sensitivity analyses was performed. The first investigated sample integrity over time and comprised only plasma samples collected two years before the pandemic; the second was analysis without adjustment of IL-6 levels for age and BMI levels. Finally, we repeated the analysis after excluding individuals with major inflammatory disease (rheumatoid arthritis, systemic lupus erythematosus, ulcerative colitis, and Crohn’s disease; n = 11).
Results
Prevalence of fatigue
After selecting participants reporting the same fatigue status at both time points, the sample for analysis comprised total n = 1274 participants, of whom n = 282 were classified COVID-19 positive, and n = 162 were classified as having chronic fatigue (Table 1). None of the COVID-19-positive participants had been hospitalized, so their COVID-19 was considered relatively mild. The prevalence of long-term fatigue was 17.4% among COVID-19-positive participants and 11.4% in COVID-19-negative participants (Fisher’s exact p = 0.011). COVID-19-positive participants were on average two years younger than COVID-19-negative participants, and there was no difference in BMI observed (Table 1). Two (1.1%) and nine (0.7%) cases of major inflammatory disease were reported by those with and without chronic fatigue, respectively (Fisher’s exact test p = 0.641).
Structure of fatigue
The qualitative nature of chronic fatigue was similar in COVID-19-positive and -negative participants, with no differences in prevalence of positive answers in the CFQ (Table 2).
IL-6 levels
In the total sample, chronic fatigue was associated with elevated pre-pandemic plasma IL-6 levels, which persisted after adjusting cytokine levels for age, sex, BMI, and COVID-19 status; chronic fatigue was as also associated with higher levels of BMI (Fig. 2).
Stratified analysis established that pre-pandemic plasma IL-6 levels were elevated in participants with chronic fatigue in the COVID-19-negative group. The same relationship was not seen in the COVID-19-positive group (Table 3). We also calculated prevalence of fatigue in COVID-19-positive and COVID-19-negative participants stratified by high and low levels of pre-pandemic IL-6. We defined high and low IL-6 levels as values equal to or above 75% percentile of IL-6 distribution and equal to or below 25% percentile, respectively. The prevalence of fatigue was found to be significantly higher in high IL-6 level group compared to low IL-6 level group in COVID-negative participants (17.5% vs 7.6%, p = 0.001); however, no such differences were found in COVID-positive group (19.2% vs 19.0%, p = 0.999). Sensitivity analysis of participants having plasma collected no earlier than 2017 showed similarity with the main analysis: β = 0.653 ± 0.375, p = 0.0814; and β = − 0.760 ± 0.625, p = 0.224 for COVID-19-negative and COVID-19-positive participants, respectively). Sensitivity analysis without adjusting IL-6 levels for age and BMI levels at the time of plasma collection, produced almost identical results to the main analysis: β = 0.321 ± 0.115, p = 0.005; and β = − 0.061 ± 0.180, p = 0.774 for COVID-19-negative and COVID-19-positive participants, respectively. Sensitivity analysis with excluding cases of major inflammatory disease, produced almost identical results, too: β = 0.307 ± 0.108, p = 0.004; and β = -0.078 ± 0.166, p = 0.639, for COVID-19-negative and COVID-19-positive participants, respectively.
BMI
Higher BMI was associated with chronic fatigue in COVID-19-positive participants and in the whole sample (Table 3). To explore the relationship between chronic fatigue and BMI in COVID-19-positive participants, we examined groups by BMI (BMI < 18.5; n = 6), healthy weight (BMI > 18.5 and < 25; n = 115), overweight (BMI > 25 and < 30; n = 102), and obese (BMI ≥ 30; n = 59). There was a significant difference in prevalence of these groups in COVID-19 positive cases with and without chronic fatigue (χ2 = 8.3, df = 3, p value = 0.040; Fig. 3). This was largely driven by a lower prevalence of healthy weight and higher prevalence of obesity in COVID-19-positive participants with chronic fatigue.
Discussion
This is among the first studies to examine pre-infective inflammatory cytokine levels and subsequent long-COVID chronic fatigue in participants who experienced mild COVID-19 illness. We found elevated pre-pandemic IL-6 levels increased the risk of developing fatigue in adult volunteers who had never had COVID-19; however, pre-pandemic IL-6 levels did not predict fatigue in our COVID-19-positive group. This finding is in keeping with other post-viral and post-infective fatigue research and studies in a general population [20, 21]. We previously demonstrated that greater pro-inflammatory cytokine elevations in response to IFN-α treatment was associated with developing persistent fatigue [10]. In that study, we also observed a role for pre-treatment higher cytokine levels of IL-6 and lower IL-10 and the development chronic fatigue — which led to the current hypothesis.
While our COVID-19-positive group were not hospitalized and therefore classified with ‘mild’ illness, it is likely that IL-6 and other pro-inflammatory cytokines reach high levels during COVID-19 infection [20], well in excess of levels associated with well-known risk factors such as elevated BMI [21]. Taken together, these and our findings suggest any elevation of circulating pro-inflammatory cytokines, be it a protracted, low-grade inflammatory dysregulation or an acute sickness response to infection will increase chronic fatigue risk, with higher cytokine levels associated with higher risk. Baseline immune status or cytokine levels of an apparently healthy individual may only predict chronic fatigue in the absence of pronounced cytokine elevation, such as an acute sickness response to viral or bacterial infection.
Our hypothesis that chronic fatigue in community dwelling adults after SARS-CoV-2 infection was predicated on pre-existing pro-inflammatory immune state) was not supported by our results.
We did however find a statistically significant association between higher BMI and long-COVID fatigue in COVID-19-positive participants (Table 3). This is consistent with a study demonstrating association between obesity and fatigue while controlling for other potential contributors including IL-6 levels [22]. Indeed, almost a third of circulating IL-6 is thought to be produced by adipocytes [23], in keeping with BMI being an important risk factor for chronic fatigue. Of interest, the relationship with BMI and chronic fatigue was not evident in COVID-19-negative participants, an association usually apparent in the population [24].
Allied to this, higher BMI and advanced age are well-established risk factors for severe COVID-19 [22]; and both are associated with greater systemic inflammation [23]. Our findings show that following mild COVID-19 chronic fatigue does not appear to be qualitatively different from other forms of chronic fatigue, at least according to the CFQ, which is a well-validated tool to discriminate between clinical and non-clinical conditions [19]. Whether this is true for more severe disease requiring hospitalization is unclear.
The most pronounced limitation to this study is the focus on a single cytokine and we recognize that other inflammatory mediators such as TNF-α are likely to contribute to chronic fatigue also.
In summary, our work sheds light on the role of IL-6 in general chronic fatigue, but it does not support a specific role for IL-6 levels in the development of chronic fatigue following mild COVID-19.
Data availability
The datasets analyzed in the current study are available on request from TwinsUK repository (https://twinsukapps.kcl.ac.uk/data_request).
References
Carfi A, Bernabei R, Landi F, Gemelli Against C-P-ACSG. Persistent symptoms in patients after acute COVID-19. JAMA. 2020;324(6):603–5.
Sudre CH, Murray B, Varsavsky T, Graham MS, Penfold RS, Bowyer RC, et al. Attributes and predictors of long COVID. Nat Med. 2021;27(4):626–31.
Townsend L, Dyer AH, Jones K, Dunne J, Mooney A, Gaffney F, et al. Persistent fatigue following SARS-CoV-2 infection is common and independent of severity of initial infection. PLoS One. 2020;15(11):e0240784.
Grygiel-Gorniak B, Puszczewicz M. Fatigue and interleukin-6 - a multi-faceted relationship. Reumatologia. 2015;53(4):207–12.
Kolak A, Kamińska M, Wysokińska E, Surdyka D, Kieszko D, Pakieła M, et al. The problem of fatigue in patients suffering from neoplastic disease. Contemp Oncol (Pozn). 2017;21(2):131–5.
Castelnovo L, Tamburello A, Lurati A, Zaccara E, Marrazza MG, Olivetti M, et al. Anti-IL6 treatment of serious COVID-19 disease: a monocentric retrospective experience. Medicine (Baltimore). 2021;100(1): e23582.
Abidi E, El Nekidy WS, Alefishat E, Rahman N, Petroianu GA, El-Lababidi R, et al. Tocilizumab and COVID-19: timing of administration and efficacy. Front Pharmacol. 2022;13:825749.
Kappelmann N, Dantzer R, Khandaker GM. Interleukin-6 as potential mediator of long-term neuropsychiatric symptoms of COVID-19. Psychoneuroendocrinology. 2021;131:105295.
Milaneschi Y, Kappelmann N, Ye Z, Lamers F, Moser S, Jones PB, et al. Association of inflammation with depression and anxiety: evidence for symptom-specificity and potential causality from UK Biobank and NESDA cohorts. Mol Psychiatry. 2021;26:7393–402.
Russell A, Hepgul N, Nikkheslat N, Borsini A, Zajkowska Z, Moll N, et al. Persistent fatigue induced by interferon-alpha: a novel, inflammation-based, proxy model of chronic fatigue syndrome. Psychoneuroendocrinology. 2019;100:276–85.
Franceschi C, Campisi J. Chronic inflammation (inflammaging) and its potential contribution to age-associated diseases. J Gerontol A Biol Sci Med Sci. 2014;69(Suppl 1):S4-9.
Bartlett DB, Firth CM, Phillips AC, Moss P, Baylis D, Syddall H, et al. The age-related increase in low-grade systemic inflammation (Inflammaging) is not driven by cytomegalovirus infection. Aging Cell. 2012;11(5):912–5.
Andrew T, Hart DJ, Snieder H, de Lange M, Spector TD, MacGregor AJ. Are twins and singletons comparable? A study of disease-related and lifestyle characteristics in adult women. Twin Res. 2001;4(6):464–77.
Verdi S, Abbasian G, Bowyer RCE, Lachance G, Yarand D, Christofidou P, et al. TwinsUK: the UK adult twin registry update. Twin Res Hum Genet. 2019;22(6):523–9.
Suthahar A, Sharma P, Hart D, García MP, Horsfall R, Bowyer RCE, et al. TwinsUK COVID-19 personal experience questionnaire (CoPE): wave 1 data capture April-May 2020. Wellcome Open Res. 2021;6:123.
Muench P, Jochum S, Wenderoth V, Ofenloch-Haehnle B, Hombach M, Strobl M, et al. Development and validation of the Elecsys Anti-SARS-CoV-2 immunoassay as a highly specific tool for determining past exposure to SARS-CoV-2. J Clin Microbiol. 2020;58(10):e01694-e1720.
Seow J, Graham C, Merrick B, Acors S, Pickering S, Steel KJA, et al. Longitudinal observation and decline of neutralizing antibody responses in the three months following SARS-CoV-2 infection in humans. Nat Microbiol. 2020;5(12):1598–607.
Jackson C. The Chalder fatigue scale (CFQ 11). Occup Med (Lond). 2015;65(1):86.
Cella M, Chalder T. Measuring fatigue in clinical and community settings. J Psychosom Res. 2010;69(1):17–22.
Cho HJ, Kivimaki M, Bower JE, Irwin MR. Association of C-reactive protein and interleukin-6 with new-onset fatigue in the Whitehall II prospective cohort study. Psychol Med. 2013;43(8):1773–83.
Hickie I, Davenport T, Wakefield D, Vollmer-Conna U, Cameron B, Vernon SD, et al. Post-infective and chronic fatigue syndromes precipitated by viral and non-viral pathogens: prospective cohort study. BMJ. 2006;333(7568):575.
Lim W, Hong S, Nelesen R, Dimsdale JE. The association of obesity, cytokine levels, and depressive symptoms with diverse measures of fatigue in healthy subjects. Arch Intern Med. 2005;165(8):910–5.
Fried SK, Bunkin DA, Greenberg AS. Omental and subcutaneous adipose tissues of obese subjects release interleukin-6: depot difference and regulation by glucocorticoid. J Clin Endocrinol Metab. 1998;83(3):847–50.
Collin SM, Nikolaus S, Heron J, Knoop H, White PD, Crawley E. Chronic fatigue syndrome (CFS) symptom-based phenotypes in two clinical cohorts of adult patients in the UK and The Netherlands. J Psychosom Res. 2016;81:14–23.
Acknowledgements
The study was supported by Kennedy Trust grant #KENN 19-20-10. JML is supported by the NIHR Birmingham Biomedical Research Centre, and CMP by the South London and Maudsley NHS Foundation Trust & King’s College London Biomedical Research Centre; the views expressed here are those of the authors and not necessarily those of the NHS, NIHR or Department for Health and Social Care. Mario Falchi is supported by MRC grant MR/T004142/1. TwinsUK is funded by the Wellcome Trust, Medical Research Council, Versus Arthritis, European Union Horizon 2020, Chronic Disease Research Foundation (CDRF), Zoe Ltd and the National Institute for Health Research (NIHR) Clinical Research Network (CRN) and Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust in partnership with King’s College London.
Author information
Authors and Affiliations
Contributions
Clinical data acquisition: NC, ED, CS, KJD, MHM. Cytokine data aquisition and pre-processing: NR, MF, MBF. Data analysis: NC, NR, MBF, AB. Manuscript preparation: MBF, FMKW, IGS, JML, PF, CP. All authors reviwed and approved the manuscript.
Corresponding author
Ethics declarations
Conflict of interest
Authors declare no conflicts of interest to disclose.
Additional information
Responsible Editor: John Di Battista.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Freidin, M.B., Cheetham, N., Duncan, E.L. et al. Long-COVID fatigue is not predicted by pre-pandemic plasma IL-6 levels in mild COVID-19. Inflamm. Res. 72, 947–953 (2023). https://doi.org/10.1007/s00011-023-01722-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00011-023-01722-2