Inflammatory profiles are associated with long COVID up to 6 months after COVID-19 onset: A prospective cohort study of individuals with mild to critical COVID-19

Elke Wynberg; Alvin X Han; Hugo D G van Willigen; Anouk Verveen; Lisa van Pul; Irma Maurer; Ester M van Leeuwen; Joost G van den Aardweg; Menno D de Jong; Pythia Nieuwkerk; Maria Prins; Neeltje A Kootstra; Godelieve J de Bree; on behalf of the RECoVERED Study Group

doi:10.1371/journal.pone.0304990

. 2024 Jul 15;19(7):e0304990. doi: 10.1371/journal.pone.0304990

Inflammatory profiles are associated with long COVID up to 6 months after COVID-19 onset: A prospective cohort study of individuals with mild to critical COVID-19

Elke Wynberg ^1,^2,^*,^#, Alvin X Han ^1,^#, Hugo D G van Willigen ^1,^#, Anouk Verveen ^3,^#, Lisa van Pul ^4,^#, Irma Maurer ^4,^#, Ester M van Leeuwen ^4,^#, Joost G van den Aardweg ⁵, Menno D de Jong ¹, Pythia Nieuwkerk ³, Maria Prins ^2,⁶, Neeltje A Kootstra ^3,^#, Godelieve J de Bree ^6,^#; on behalf of the RECoVERED Study Group^¶

Editor: Mickael Essouma⁷

¹Department of Medical Microbiology & Infection Prevention, Amsterdam UMC, University of Amsterdam, Amsterdam Institute for Infection and Immunity, Amsterdam, the Netherlands

²Department of Infectious Diseases, Public Health Service of Amsterdam, Amsterdam, the Netherlands

³Department of Medical Psychology, Amsterdam UMC, Amsterdam Public Health Research Institute, University of Amsterdam, Amsterdam, the Netherlands

⁴Department of Experimental Immunology, Amsterdam UMC, University of Amsterdam, Amsterdam Institute for Infection and Immunity, Amsterdam, the Netherlands

⁵Department of Pulmonology, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands

⁶Department of Infectious Diseases, Internal Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam Institute for Infection and Immunity, Amsterdam, the Netherlands

⁷Universal Scientific Education and Research Network, UNITED KINGDOM

Competing Interests: The authors have declared that no competing interests exist.

¶ Membership of the RECoVERED study group is listed in the Acknowledgments.

^✉

* E-mail: elke.wynberg@gmail.com

Contributed equally.

Roles

Elke Wynberg: Conceptualization, Data curation, Methodology, Project administration, Validation, Writing – original draft, Writing – review & editing

Alvin X Han: Conceptualization, Formal analysis, Methodology, Visualization, Writing – original draft, Writing – review & editing

Hugo D G van Willigen: Data curation, Project administration, Writing – review & editing

Anouk Verveen: Data curation, Project administration, Writing – review & editing

Lisa van Pul: Formal analysis, Investigation, Methodology, Writing – review & editing

Irma Maurer: Formal analysis, Investigation, Methodology, Writing – review & editing

Ester M van Leeuwen: Formal analysis, Investigation, Methodology, Writing – review & editing

Joost G van den Aardweg: Funding acquisition, Investigation, Methodology, Supervision, Validation, Writing – review & editing

Menno D de Jong: Funding acquisition, Project administration, Resources, Supervision, Writing – review & editing

Pythia Nieuwkerk: Funding acquisition, Project administration, Resources, Supervision, Writing – review & editing

Maria Prins: Funding acquisition, Methodology, Project administration, Resources, Supervision, Writing – review & editing

Neeltje A Kootstra: Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Validation, Writing – original draft, Writing – review & editing

Godelieve J de Bree: Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Validation, Writing – original draft, Writing – review & editing

Mickael Essouma: Editor

PMCID: PMC11249251 PMID: 39008486

Abstract

Background

After initial COVID-19, immune dysregulation may persist and drive post-acute sequelae of COVID-19 (PASC). We described longitudinal trajectories of cytokines in adults up to 6 months following SARS-CoV-2 infection and explored early predictors of PASC.

Methods

RECoVERED is a prospective cohort of individuals with laboratory-confirmed SARS-CoV-2 infection between May 2020 and June 2021 in Amsterdam, the Netherlands. Serum was collected at weeks 4, 12 and 24 of follow-up. Monthly symptom questionnaires were completed from month 2 after COVID-19 onset onwards; lung diffusion capacity (D_LCO) was tested at 6 months. Cytokine concentrations were analysed by human magnetic Luminex screening assay. We used a linear mixed-effects model to study log-concentrations of cytokines over time, assessing their association with socio-demographic and clinical characteristics that were included in the model as fixed effects.

Results

186/349 (53%) participants had ≥2 serum samples and were included in current analyses. Of these, 101/186 (54%: 45/101[45%] female, median age 55 years [IQR = 45–64]) reported PASC at 12 and 24 weeks after COVID-19 onset. We included 37 reference samples (17/37[46%] female, median age 49 years [IQR = 40–56]). In a multivariate model, PASC was associated with raised CRP and abnormal diffusion capacity with raised IL10, IL17, IL6, IP10 and TNFα at 24 weeks. Early (0–4 week) IL-1β and BMI at COVID-19 onset were predictive of PASC at 24 weeks.

Conclusions

Our findings indicate that immune dysregulation plays an important role in PASC pathogenesis, especially among individuals with reduced pulmonary function. Early IL-1β shows promise as a predictor of PASC.

Background

Almost half of individuals [1] infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are estimated to experience symptoms related to “long COVID” or post-acute sequelae of COVID-19 (PASC). PASC has been defined by the World Health Organization [2] as symptoms lasting longer than 3 months after initial infection, with these symptoms lasting for at least 2 months with no other explanation. The symptoms associated with PASC involve numerous different organ systems, including cough (respiratory), palpitations (cardiovascular), post-exertional malaise (musculoskeletal), and sleep disturbances, reduced concentration and cognitive fatigue (neuropsychiatric) [3]. Similar sequelae have been reported in children, although studies in paediatric populations are scarce [4].

There are many uncertainties regarding the pathophysiology of PASC, with hypotheses including viral persistence, hypercoagulability, autonomic dysfunction, chronic inflammation [3]–or a combination of these. Several studies have suggested that hyperinflammation observed during acute infection among individuals with severe COVID-19 [5–7] may persist among those with ongoing symptoms [8]. However, whether chronic inflammation also underpins the pathogenesis of PASC in individuals with initially mild or moderate COVID-19 is less clear. Studies investigating chronic inflammation in PASC to date have been largely inconclusive due to heterogeneity in research objectives, study design and population. Some studies aimed to outline immune disturbances in the first months after infection [9–11] in order to better understand COVID-19 immunopathology, whilst others specifically aimed to identify early inflammatory predictors for PASC that could be of clinical relevance [12–16]. As such, prospective data that aims to both outline longitudinal immune profiles among individuals with mild to critical COVID-19, and identify possible early markers of PASC, is needed to better understand the role of chronic inflammation in the development and persistence of PASC.

Using data from the prospective RECoVERED cohort study in Amsterdam, the Netherlands, we firstly aimed to investigate the evolution of serum levels of specific cytokines from COVID-19 onset onwards among individuals with PASC, compared to individuals without PASC. Reference samples were included from SARS-CoV-2-naïve individuals. Secondly, we aimed to explore the determinants of cytokine levels at 3 and 6 months following COVID-19 onset, including PASC status. Finally, aimed to identify possible early biomarkers for the development of PASC.

Methods

We present the methodology of our cohort study according to The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for reporting observational studies [17].

Study design and participant enrolment

RECoVERED is a prospective cohort study of adults aged 16–85 with SARS-CoV-2 infection between May 2020 and June 2021 in Amsterdam, the Netherlands. Full details of study procedures and additional inclusion/exclusion criteria have been published elsewhere [18]. Briefly, participants were enrolled within 7 days of diagnosis (at the Public Health Service of Amsterdam) or hospital admission (at the Amsterdam University Medical Centres [UMC]). All participants had laboratory-confirmed SARS-CoV-2 infection. RECoVERED was approved by the medical ethical review board of the Amsterdam University Medical Centres (METC NL73759.018.20). All participants provided written informed consent.

For the current analysis, we included a sub-set of participants with samples from at least two different time-points and with follow-up of at least 3 months following SARS-CoV-2 infection up to November 2022. We additionally included reference samples from SARS-CoV-2-uninfected, healthy individuals (i.e., no comorbidities) collected between March 2020 and November 2021, in order to better understand the impact of SARS-CoV-2 infection on absolute levels of cytokines. These samples were randomly selected from a prospective serologic surveillance cohort study among hospital healthcare workers in the Amsterdam UMC (S3 study; METC NL73478.029.20) [19].

Data collection

During the first month of follow-up, trained study staff interviewed participants on the presence of 21 different COVID-19 symptoms, took physical measurements, and recorded participants’ past medical and socio-demographic characteristics. Recorded symptoms included: fatigue, cough, fever, rhinorrhoea, sore throat, dyspnoea, loss of smell and/or taste, chest pain, headache, abdominal pain, confusion, arthralgia, myalgia, loss of appetite, wheeze, skin rash, nausea and/or vomiting, diarrhoea, earache, and spontaneous bleeding. Between months 3 to 12 of follow-up, monthly online questionnaires on the presence of the same 21 COVID-19 symptoms were completed by participants. Lung function tests including diffusion capacity (D_LCO) were performed at 1, 6 and 12 months after COVID-19 onset (detailed methods described elsewhere [20]).

Serum samples were collected at day 0 and 7 and subsequently at months 1, 3, and 6 of follow-up. Additional serum samples were collected per protocol within 24 hours of receiving a COVID-19 vaccination and 7 and 28 days following each COVID-19 vaccination. All serum samples were processed within 24 hours and stored at -80°C. In the present study, we defined three time-frames of sample collection for longitudinal analyses: 0–4, 9–12 and 21–24 weeks after COVID-19 onset. Post-vaccination samples were defined as those collected within 28 days after administration of a COVID-19 vaccine.

C-reactive protein (CRP), monocyte/macrophage surface receptors (CD14, CD163), tumor necrosis factor (TNF)-α, interferon-γ-inducible protein 10 (IP-10)/CXCL10, monocyte chemoattractant protein (MCP)1/CCL2, and interleukins (IL)1β, IL2, IL6, IL10, IL13, and IL17A concentrations were analyzed in serum by human magnetic luminex screening assay (LXSAHM-02 and LXSAHM-10; R&D Systems) using the Bio-Plex 200 System (Bio-Rad Laboratories). Assays were performed according to the manufacturer’s instructions. Titers of total antinuclear antibodies (ANA) as markers of auto-immunity were determined by immunofluorescence using the HEp-20-10 test kit (Euroimmun AG) for samples collected ≤4 weeks after COVID-19 onset.

Outcomes

The primary outcomes of our analysis were cytokine levels at 9–12 and 21–24 weeks after COVID-19 onset. Our secondary outcomes were: PASC status at 6 months, and CRP and IL6 levels at 21–24 weeks.

Definitions

COVID-19 onset was defined as the earliest day that COVID-19 symptoms were experienced for symptomatic patients, or the date of SARS-CoV-2 diagnosis for asymptomatic patients. PASC was defined as reporting at least one COVID-19 symptom that, from COVID-19 onset, occurred within one month and continued beyond 12 weeks [21]; symptoms arising after one month from COVID-19 onset were not attributed to PASC. COVID-19 clinical severity was categorised according to WHO criteria [22]: mild disease was defined as having a RR <20/minute and SpO2>94% on room air at both D0 and D7 study visits; moderate disease as having a RR20–30/minute and SpO2 90–94% on room air (or receiving oxygen therapy, if no off-oxygen measurement available) at either visit; severe disease as having a RR >30/minute and SpO2<90% on room air (or receiving oxygen therapy) at either visit; critical disease as requiring ICU admission as a result of COVID-19 at any point. BMI was coded in kg/m² as: <25, underweight or normal weight; 25–29, overweight; ≥30, obese. Diffusion capacity (D_LCO) at 6 months after COVID-19 onset was defined as impaired according to American Thoracic Society/European Respiratory Society guidelines [23], as described previously [20].

Statistical analysis

In the first descriptive analysis, socio-demographic, clinical and study characteristics were compared between included participants with and without PASC at 12 weeks after COVID-19 onset. Continuous variables were analyzed using the Kruskal-Wallis test and categorical and binary variables were compared using the Pearson χ² test (or Fisher exact test if n <5). To assess selection bias, we compared features of included and excluded participants. We used the Fisher’s exact test to evaluate the association between PASC and impaired diffusion capacity at 6 months after COVID-19 onset.

The primary outcome of the study was cytokine levels at 9–12 and 21–24 weeks after COVID-19 onset. A correlation matrix (Pearson’s correlation coefficient) of cytokines at 0–4, 9–12 and 21–24 weeks were used to help interpret the complexity of inter-marker associations. Next, we determined box and whisker plots of of median (IQR) log-concentrations of cytokines at 9–12 and 21–24 weeks after COVID-19 onset. During this analysis, data of study participants with and without PASC at 12 and 24 weeks after COVID-onset were compared with data from the uninfected healthy control population using the Mann-Whitney and Bonferroni correction tests. Comparisons were first performed using data from the whole cohort (mild to critical COVID-19) and subsequently restricted to participants with mild or moderate COVID-19. To additionally assess differences in cytokine levels using an objective measure of PASC, we compared median (IQR) log-concentrations of cytokines measured at 21–24 weeks between participants with and without an impaired diffusion capacity (D_LCO) at 6 months after COVID-19 onset. We subsequently used linear mixed-effects tests to assess the effect of pre-COVID and COVID-related factors (including PASC status) on cytokine concentrations in two cross-sectional analyses. To this end we applied two linear multivariate mixed-effects models: the first at 3 months (serum collected at 9–12 weeks) and the second at 6 months (serum collected at 21–24 weeks) after COVID-19 onset. We modelled log concentrations of cytokines with time since COVID-19 onset as a random effect (to account for variability in the timing of serum sampling around the 3- and 6-month time points) and as fixed effects: sex, age and clinical characteristics (i.e. body mass index [BMI] and comorbidities, defined at COVID-19 onset), PASC status (at 12 and 24 weeks after COVID-19 onset in each model, respectively), and recent (<4 weeks) vaccination. The selection of effects was chosen following published risk factors for long COVID (reviewed by [3]). We substituted PASC status (based on self-reported symptoms) for impaired diffusion capacity (D_LCO) in an additional model at 6 months after COVID-19 onset. Condition indices were computed to ensure that there was no collinearity among the variables (i.e., condition index<10).

The secondary outcome was the identification of predictive determinants at COVID-19 onset of later PASC. We performed a random forest regression, a model-free machine-learning approach, to identify the early predictors of: (1) PASC at 24 weeks and (2) higher levels, at 21–24 weeks after COVID-19 onset, of CRP and IL-6. We performed k-fold cross validation (k = 5) to tune the hyperparameters of each random forest regressor. We used F1 scores and mean squared error as scoring functions of the random forest regressors used in (1) and (2)/(3) respectively. We then computed Shapley additive explanation values as measures of importance for the different predictors [24]. CRP and IL6 at 0–4 weeks were not individually included as predictors of their measurements at 21–24 weeks.

All data were collected and stored in a secure database and exported for statistical analyses to Python. Analyses were performed using the statsmodels package (v. 0.13.2) [25], whilst the random forest regression analyses were performed in Python using the scikit-learn package (v. 1.1.3).

Results

Baseline characteristics of the study population

Of 349 RECoVERED participants, 186 (53.3%) had at least two serum sampling moments and were included in the present study. Included participants were more likely to be male and have initially mild COVID-19 (S1 Table in S1 File). 101/186 (54%; 45/101[45%] female, median age 55 years [IQR = 45–64]) included participants reported PASC at 12 weeks after COVID-19 onset (Table 1 and S2 Table in S1 File), of whom none reported their symptoms resolving or were lost to follow-up between weeks 12 and 24. A subgroup (72/186; 38.7%) of participants had a diffusion capacity at 6 months after COVID-19 onset. Among the 22/72 who exhibited impaired diffusion capacity, more than half (12/22; 54.5%) also reported PASC at 6 months (p = 0.031).

Table 1. Characteristics of study participants with and without PASC at 12 weeks after COVID-19 onset.

	Total	Symptoms resolved within 12 weeks (no PASC)	Ongoing symptoms at 12 weeks (PASC)	p-value^*
	N = 186	N = 85	N = 101	p-value^*
Sex				0.022
Male	117 (63%)	61 (72%)	56 (55%)
Female	69 (37%)	24 (28%)	45 (45%)
Age, years	52.0 (37.0–62.0)	48.0 (33.0–60.0)	55.0 (45.0–64.0)	0.006
BMI, kg/m²	25.7 (23.2–29.3)	25.4 (23.1–27.6)	26.2 (23.2–29.7)	0.16
ANA positive ^**		8 (14%)	7 (18%)	-
Clinical severity score				<0.001
Mild	64 (34%)	44 (52%)	20 (20%)
Moderate	79 (42%)	33 (39%)	46 (46%)
Severe/critical	43 (23%)	8 (9%)	35 (35%)
COVID-19 vaccination status (primary series)				0.27
Not vaccinated	1 (1%)	1 (1%)	0 (0%)
Vaccinated	185 (99%)	84 (99%)	101 (100%)
Time from COVID-19 onset to first vaccination, days	244 (151–363)	226 (142–303)	270 (158–386)	0.014

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Abbreviations: BMI, body mass index; COVID-19, coronavirus disease 2019; HR, heart rate; ICU, intensive care unit; LTFU, lost to follow-up; OECD, Organisation for Economic Co-operation and Development; NA, not applicable; PCR, polymerase chain reaction; SpO2, oxygen saturation on room air; RR, respiratory rate; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.

* Continuous variables presented as median (IQR) and compared using the Kruskal-Wallis test; categorical and binary variables presented as n(%) and compared using the Pearson χ² test (or Fisher exact test if n <5).

Clinical severity groups defined as: mild as having an RR <20/min and SpO2 on room air >94% at both D0 and D7; moderate disease as having a RR 20–30/minutes, SpO2 90–94% and/or receiving oxygen therapy at D0 or D7; severe disease as having a RR >30/minutes or SpO2 <90% at D0 or D7; critical disease as requiring ICU admission. COVID-related comorbidities are based on WHO Clinical Management Guidelines and include: cardiovascular disease (including hypertension), chronic pulmonary disease (excluding asthma), renal disease, liver disease, cancer, immunosuppression (excluding HIV, including previous organ transplantation), previous psychiatric COVID-19 and dementia. Physical measurements at D0 and D7 study visits. Oxygen saturation measured on room air if possible or retrieved from ambulance records for hospitalized participants admitted on oxygen on day of enrollment. Physical measurements not displayed for individuals with critical disease due to unreliability of measurements at admission for critically-ill patients.

** Of all participants with PASC there were 59 samples available for ANA testing, of all participants without PASC there were 39 samples available for ANA testing.

We included reference samples from 37 individuals with no history of SARS-CoV-2 infection (median (IQR) age 49 years [IQR = 39.5–55.5]; 17/37 [45,9%] female). Seven (18.9%) reference samples were collected within 6 months after a primary COVID-19 vaccination series.

Primary outcome: Cytokine levels at 9–12 and 21–24 weeks after COVID-19 onset and their determinants

S1 Fig and S3 Table in S1 File demonstrate aberrant cytokine levels in RECoVERED study participants, both with and without PASC, compared to the reference group. Cytokine correlation matrices are shown in S2 Fig in S1 File. In univariable analyses, individuals with PASC tended to have lower levels of sCD14, IL10, IL17, IL1β, IL6 and TNFα (Fig 1) compared to participants without PASC at 9–12 weeks after COVID-19 onset. By 21–24 weeks, participants with PASC had significantly higher concentrations of IL10, IL1β and sCD14 than those without PASC. When restricting our analyses to participants with initially mild or moderate COVID-19, no difference in IL10 levels between participants with and without PASC remained at 21–24 weeks; however, the higher levels of IL1β and sCD14 observed among participants with PASC compared to those without PASC remained (S3 Fig in S1 File). Individuals with an impaired diffusion capacity (D_LCO) had significantly higher log-concentrations of IP10, IL10, IL6 and TNFα than participants with normal diffusion capacity at 21–24 weeks (S4 Fig in S1 File).

Fig 1 — Individuals were stratified by whether or not they reported post-acute sequelae of COVID-19 (PASC) at 12 (for measurements at 9–12 weeks) or 24 (for measurements at 21–24) weeks after COVID-19 onset. Each dot represents an individual coloured by initial severity of COVID-19. The mean value is plotted for each individual with multiple measurements within the binned time period. A Mann-Whitney U test was used to test if measurements between those with and without PASC were significantly different. Multiple testing correction was performed using the Bonferroni method and comparisons with family-wise error rate < 0.05 were marked with an “*”.

In multivariable analyses, participants with PASC had significantly lower levels of IL10 and TNF-α compared to participants who had recovered from their symptoms at 9–12 weeks. At 9–12 weeks after disease onset, participants who had initially severe COVID-19 tended to have significantly higher levels of IP10 and sCD163 and lower levels of IL10, IL6, TNFα, IL17 and IL13 (Fig 2) compared to those with mild or moderate disease, when adjusting for other covariates. Independent of initial disease severity, having received dexamethasone during acute COVID-19 was associated with higher levels of IL6, IL10, sCD14 and CRP compared to those who did not receive dexamethasone, in multivariable analyses. Age ≥ 60 years and BMI ≥ 30 kg/m² at COVID-19 onset were associated with higher levels of IL2 and IP10, and MCP1 and CRP, respectively, when adjusting for other covariates.

Fig 2 — For each inflammatory marker, we performed a mixed effects linear regression of various factors: characteristics present prior to COVID-19 onset, COVID-19-related factors, and post-COVID-19 related factors. Characteristics present prior to COVID-19 onset included sex, age (≥60 or <60 years old), body mass index (BMI) ≥ 30, and the presence of comorbidities (including cardiovascular disease, diabetes mellitus, chronic pulmonary disease or current cancer). COVID-19-related factors included the severity of initial COVID-19 disease, if dexamethasone was administered, and current (at 12 weeks) presence of post-acute sequelae of COVID-19 (PASC). Post-COVID-19 related factors included measurement of inflammatory markers within four weeks after SARS-CoV-2 vaccination. Statistically significant negative effects (associations with lower cytokine concentrations) are shown in red whilst positive effects (associations with higher cytokine levels) are shown in blue.

By 21–24 weeks after COVID-19 onset, participants with ongoing PASC at 24 weeks after COVID-19 onset had higher concentrations of CRP in multivariable analyses compared to participants without PASC (Fig 3). Of note, when instead of PASC, impaired diffusion capacity was added to the analysis, we found that impaired diffusion capacity was associated with higher levels of CRP, IL6, TNFα, IP10, IL10 and IL17 in multivariable analyses (S5 Fig in S1 File). In addition, the effect of obesity and of older age on cytokine concentrations became more pronounced at 21–24 weeks compared to at 9–12 weeks (Fig 3). Individuals who received dexamethasone had significantly lower levels of TNFα, IL6 and IL1β by 21–24 weeks in multivariable analyses, in contrast to findings at 9–12 weeks after COVID-19 onset.

Fig 3 — For each inflammatory marker, we performed a mixed effects linear regression of various factors: characteristics present prior to COVID-19 onset, COVID-19-related factors, and post-COVID-19 related factors. Characteristics present prior to COVID-19 onset included sex, age (≥60 or <60 years old), body mass index (BMI) ≥ 30, and the presence of comorbidities (including cardiovascular disease, diabetes mellitus, chronic pulmonary disease or current cancer). COVID-19-related factors included the severity of initial COVID-19 disease, if dexamethasone was administered, and current (at 24 weeks) presence of post-acute sequelae of COVID-19 (PASC). Post-COVID-19 related factors included measurement of inflammatory markers within four weeks after SARS-CoV-2 vaccination. Statistically significant negative effects (associations with lower cytokine concentrations) are shown in red whilst positive effects (associations with higher cytokine levels) are shown in blue.

Secondary outcome: Early predictors of PASC and ongoing inflammation 6 months after COVID-19 onset

We found early IL1β and BMI at COVID-19 onset to be the strongest predictors of PASC at 21–24 weeks (Fig 4a), using Shapley additive explanation values as measures of importance [24]. Higher levels of sCD14, and to a lesser extent IL10, at 0–4 weeks were important predictors of higher levels of CRP at 21–24 weeks (Fig 4b). IL1β and TNFα measurements at 0–4 weeks were key predictors of IL6 levels at 21–24 weeks (Fig 4c). Total ANA titers were less important than BMI, initial disease severity and age in the prediction of ongoing PASC at 21–24 weeks after COVID-19 onset, but more important than sex and early levels of CRP, IL6 and IL10 (Fig 4a).

Discussion

In our prospective cohort study of participants with mild to critical COVID-19, we explored the evolution of cytokine concentrations from disease onset and the effect of PASC on cytokine concentrations up to 6 months after COVID-19 onset. Participants with PASC displayed significantly higher concentrations of pro-inflammatory CRP at 21–24 weeks after COVID-19 onset compared to those without PASC. In addition, when defining PASC as having an impaired diffusion capacity at 6 months after COVID-19 onset, we identified an association with raised levels of several other pro-inflammatory cytokines, also when adjusting for comorbidities and initial COVID-19 severity. This suggests that individuals with objectifiable pathology in the context of PASC may experience more pronounced immune dysregulation. Raised IL1β at 0–4 weeks after COVID-19 onset was strongly predictive of persistent PASC at 24 weeks after COVID-19 onset, and warrants further exploration as a possible predictive biomarker for PASC.

In our study, participants with PASC, as defined by self-reported symptoms, had raised CRP at 6 months after COVID-19 onset when adjusting for age, sex, comorbidities (including obesity) at COVID-19 onset, initial COVID-19 severity, dexamethasone treatment and recent COVID-19 vaccination. We did not, however, identify increased levels of numerous other pro-inflammatory cytokines including IL6, IL1β and TNFα among individuals with PASC, as has been described in other studies [9, 16, 26–28]. Interestingly, however, when substituting self-reported PASC status with impaired diffusion capacity as a more objective measure, the more complex pattern of raised cytokines (including IL10, IL6, IL17, IP10 and TNFα) observed in other studies was echoed in our cohort, also when adjusting for possible confounders such as age and comorbidities. This observation may reflect two possible explanations. Firstly, that confirming PASC status with an objective measure increased the precision of the definition, reducing misclassification bias resulting from self-reported symptoms not related to PASC and thus allowing an association with pro-inflammatory cytokines to be revealed. Alternatively, PASC is an umbrella term for multiple conditions, within which individuals with measurable lung abnormalities experience a persistent hyperinflammatory process whilst the underlying cause of those with other symptom clusters [3, 29] cannot be explained by aberrant cytokines. Our findings thus suggest that immune dysregulation plays an important role in PASC pathogenesis in some individuals, and that those with measurable persistent pathology following COVID-19 may exhibit more pronounced hyperinflammation.

We also observed that higher levels of IL1β (and to a lesser extent sCD14, IL13, IL17 and TNFα) at 0–4 weeks were strongly associated with ongoing PASC at 24 weeks after COVID-19 onset. This is of interest because we did not find an association of self-reported PASC with IL1β at 21–24 weeks, suggesting that early IL1β induced tissue damage [30] or endothelial dysfunction [31] may predispose individuals to later symptomatology. Given that IL1β has been implicated as a marker of neuroinflammation [32] and profound neurological symptoms are part of PASC [33], further exploration of the role of this cytokine in PASC pathogenesis is warranted. In addition, we found that elevated early sCD14 and IL10 levels were most strongly predictive of raised CRP at 21–24 weeks, whilst IL1β and TNFα levels were strongly congruent with persistently elevated IL6 at 24 weeks. These findings help provide insight into the possible immunopathogenesis of PASC. First, the association between high sCD14 levels at 0–4 weeks and persistently elevated CRP and PASC at 24 weeks suggests the key role of monocyte-macrophage activation in the acute phase [34], driving long-term inflammation. Second, our findings imply that IL1β-mediated acute inflammation contributes to ongoing symptomatology and hyperinflammation many months after infection. Taken together, our data add to an increasing number of studies [26, 27] that observe inflammation as reflection of immune dysregulation in PASC. However, it currently remains unclear which factors (for instance ongoing antigen persistence [35]) may drive immune dysregulation, and if immune dysregulation is a result rather than a cause of PASC pathology [27]. Consistent with previous findings(34,35), higher BMI at COVID-19 onset was also an independent predictor of ongoing PASC at 24 weeks, also when adjusting for concentrations of numerous pro-inflammatory cytokines. This suggests that the effect of BMI on PASC risk is not only mediated by inflammation (as indicated by the association of obesity at COVID-19 onset with raised TNFα, sCD163 and CRP at 21–24 weeks), but also by additional physiological and metabolic processes [36]. It is crucial that ongoing studies on the role of inflammation in PASC pathogenesis account for the central role played by BMI.

Our study benefits from its prospective design, thus limiting selection bias, detailed symptom data, representation of a wide range of COVID-19 severity, and the uniformity of sample processing and analysis procedures conducted in a central laboratory. However, our study also has limitations. Firstly, we did not have symptom data pre-dating SARS-CoV-2 infection. We therefore cannot be certain that reported symptoms were a result of COVID-19 or due to pre-existing comorbidities, which may have resulted in misclassification bias. However, we attempted to overcome this bias by defining long COVID symptoms as those with a date of onset within 1 month from overall COVID-19 onset. In addition, when we considered lung function results as an objective measure of PASC symptoms related to respiratory sequelae, this allowed us to reduce noise from self-reported symptoms. We did not collect pre-COVID serum samples available to distinguish any pre-existing aberrant cytokine levels resulting from underlying comorbidities. Finally, the vast majority of our cohort were infected with the wild-type variant of SARS-CoV-2, limiting the external validity to individuals developing long COVID following infection with currently-circulating sub-variants. However, our findings continue to shed light on the (immuno-) pathogenesis of long COVID which will inform future research among different populations.

Conclusions

Our study indicates that immune dysregulation is associated with PASC, especially when defining PASC as having impaired pulmonary function. In addition, early raised IL1β levels were strongly predictive of ongoing PASC at 6 months in our analyses. Our findings therefore suggest that immune dysregulation plays an important role in the pathogenesis of ongoing symptoms. An essential further question is if immune dysregulation is causally related to PASC and, if so, what the driving pathological processes may be. Next steps in addressing this question may include focusing on immune dysregulation in individuals with well-defined clusters of PASC symptoms in relation to organ involvement.

Supporting information

S1 File

(DOCX)

pone.0304990.s001.docx^{(1.9MB, docx)}

Acknowledgments

RECoVERED Study Group: From the Public Health Service of Amsterdam: Ivette Agard, Jane Ayal, Floor Cavdar, Marianne Craanen, Udi Davinovich, Annemarieke Deuring, Annelies van Dijk, Maartje Dijkstra, Ertan Ersan, Laura del Grande, Joost Hartman, Nelleke Koedoot, Romy Lebbink, Tjalling Leenstra, Dominique Loomans, Agata Makowska, Tom du Maine, Ilja de Man, Amy Matser, Lizenka van der Meij, Marleen van Polanen, Maria Oud, Clark Reid, Leeann Storey, Marije de Wit, Marc van Wijk. From Amsterdam University Medical Centers: Joyce van Assem, Marijne van Beek, Orlane Figaroa, Leah Frenkel, Agnes Harskamp-Holwerda, Mette Hazenberg, Soemeja Hidad, Nina de Jong, Hans Knoop, Lara Kuijt, Anja Lok, Eric Moll van Charante, Colin Russell, Annelou van der Veen, Bas Verkaik, Gerben-Rienk Visser.

The authors wish to thank all RECoVERED study participants.

Data Availability

Data cannot be shared publicly because of confidentiality agreements. Relevant data can be made available upon request via the RECoVERED Study Steering Committee. Please contact the AMC Medical Ethical Committee for additional information: https://metc.amsterdamumc.org/contact/.

Funding Statement

This work was supported by the Netherlands Organization for Health Research and Development (ZonMw) [10150062010002 to M.D.d.J. and 10430072110003 to G.J.d.B] and the Public Health Service of Amsterdam [R&D grants in 2021 and 2022 to M.P.]. The funders had no role in study design, data collection, data analysis, data interpretation or data reporting. ZonMw website: https://projecten.zonmw.nl/nl/project/recovered GGD Amsterdam website: https://www.ggd.amsterdam.nl/ggd/.

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PLoS One. doi: 10.1371/journal.pone.0304990.r001

Decision Letter 0

Mickael Essouma

11 Sep 2023

PONE-D-23-20788Inflammatory profiles are associated with long COVID up to 6 months after illness onset: a prospective cohort study of individuals with mild to critical COVID-19PLOS ONE

Dear Dr. Wynberg,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Mickael Essouma, M. D.

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PLOS ONE

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Additional Editor Comments:

General comment

You have a good study to provide great insights into the pathogenesis of long COVID as your study is a prospective cohort, which is the best study design in observational epidemiology to inform on disease aetiology. In addition, the assessment of pro-inflammatory cytokines gives you the opportunity to discuss underlying inflammatory mechanisms of long COVID. However, you missed your opportunities with this version of the manuscript. I there make the following specific comments to further help you improve your manuscript:

1-Major comments

The title of the manuscript needs a revision, taking into considerations revisions suggested for the main manuscript. I would say: "Evolution of serum levels of pro-inflammatory cytokines in individuals with long COVID and its predictors: data from a 6-month prospective follow-up of individuals with COVID-19". Same thing for keywords that should be taken from the tile. The abstract will also need revision.

Introduction: Call it Background as recommended in the journal`s policy. I suggest revising it to provide within one page: a definition of long COVID (see https://apps.who.int/iris/bitstream/handle/10665/366126/WHO-2019-nCoV-Post-COVID-19-condition-CA-Clinical-case-definition-2023.1-eng.pdf and https://apps.who.int/iris/bitstream/handle/10665/345824/WHO-2019-nCoV-Post-COVID-19-condition-Clinical-case-definition-2021.1-eng.pdf) as well as its main manifestations (https://doi.org/10.1038/s41598-021-95565-8) in both paediatric and adult populations in the first paragraph, a discussion of the pathophysiology of long COVID highlighting gaps in knowledge notably regarding immune dysregulation: you have these details here https://doi.org/10.1038/s41579-022-00846-2. The third paragraph should then clearly, simply and succinctly state the aim of your study: assess the evolution of serum levels of some pro-inflammatory cytokines in individuals with PASC as from the early period of COVID-19 infection, as well as predictors of PASC, with overarching goal to contribute to the advancement of knowledge on the pathophysiology of PASC.

Methods: please, conform to the STROBE guidelines and state that you conformed to the STROBE guidelines at the beginning of the Methods section. Desired sub-sections of this section include: " Study design, setting and participants (where you describe the RECOVERED study, For the description of the RECOVERED study which is very important, keywords are: when was it launched? It is a cohort study with prospectively collected data, what are inclusion [e.g., age, gender, individuals with mild to critical COVID-19: how do you define mild to critical COVID-19 with references?]and exclusion criteria of the study population? You present data from which period? If the cohort has already been extensively presented elsewhere as it seems, end this brief description with a sentence to orientate the readers towards that/those paper[s]), Data collection (where you describe the data collected [clinical [e.g., BMI] imaging [e.g., HRCT], functional [e.g., DLCO], and cytokines, with emphasis on cytokines: list the cytokines of interest here], how you collected those data with clear specification of time points of data collection, how blood samples for cytokine assessment were transported to the laboratory and conserved before the assessment of cytokines, and how cytokines were assessed in the laboratory: this subsection should be the longest of the methods section), exposition and outcome measures (specify the exposure [COVID], outcome measures [primary: serum levels of pro-inflammatory cytokines; list them at t...], secondary outcomes?), statistical analysis (important elements of this sub-section that should be clearly and succinctly delineated include: where did you store data collected on questionnaires? Did you export them to another software for analysis? Which are the main variables analysed? Which statistical tests did you use to assess the predictors [see https://doi.org/10.1093/ejendo/lvac012 and doi: 10.1093/ndt/gfw459]? Which are the candidate predictors [this is where you need to define the predictors such as high BMI]? For which PASC manifestations do you assess the predictive role of those pro-inflammatory cytokines? How did you define statistical significance of your results?), and ethical issues.

Results and discussion are biased because of methodological biases. Notably, a population without a history COVID-19 is not necessary to assess predictors of PASC as you can read from the articles on predictor assessment provided above. I can understand that you used the blood samples of individuals without a history of COVID-19 as control samples in the laboratory, but this should be clearly

and only explained under the data collection sub-section of the methods section as mentioned above. It should be very clear in the revised methods section as suggested above, that your study population includes only participants from the RECOVERED cohort who were all exposed to COVID-19 infection. Furthermore, you included individuals aged >= 16 years, and disease in adolescents is not exactly the same as in adults (see the literature from the WHO on the definition of post-covid syndrome mentioned above. Some consider paediatric patients up to 21 years. So, it would be interesting to disentangle data from adults to those from adolescents, or in case the sample of adolescents does not allow this because it is too small, acknowledge this issue in the discussion as a limitation. Moreover, it will be interesting to disentangle data from patients who had persistent covid manifestations to those who developed post-covid manifestations after a period free of symptoms of COVID-19. The reviewers have raised a similar comment. The results section would better be split into three sub-sections: general characteristics of the study population, Evolution of serum levels pro-inflammatory cytokines in individuals with PASC [the study population that developed the outcome] compared to the COVID-19 infected population from the RECOVERED that did not develop the PASC outcome throughout the study period (this is where figures on the evolution of pro-inflammatory cytokines need to be linked), and predictors of PASC/PASC manifestations (the manifestations clustered and disentangled).

In the discussion: the first paragraph to summarize succinctly your main findings (regarding the evolution of levels of serum pro-inflammatory cytokines and predictors of PASC/PASC manifestations). Other paragraphs to compare your results with data from the literature, a paragraph to summarize how your study advances knowledges in the pathophysiology of PASC based on inflammatory mechanisms (reflect on potential mechanisms based on data from the literature and your results on pro-inflammatory cytokine assessment: you may add a figure to better highlight the potential mechanisms), and finally a paragraph on strengths and limitations of your study (do not forget to focus on limitations of a prospective cohort study as well as limitations with blood sample collection, conservation and analyses in the laboratory, as these are the limitations that have the potential to affect the interpretation of your study results. Other limitations are not worth mentioning).

The conclusion should be a take-home message with a mention of the perspective of your study for future studies on PASC.

2-Minor comments

Statements (conflicts of interests, authors` contributions, acknowledgments, funding) should come after the conclusion, and before the references section.

Format references according to the journal`s policy. References 1 to 4: I would not mention them as the study is about post-COVID (long COVID) syndrome. Reference 9 needs to be revised or replaced by another well identifiable reference, same thing for all references with print and electronic in brackets. Make sure there is no citation gaming in your manuscript. I will revise it next time.

Revise tables and figures as required

Language editing also needed.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The manuscript analyze the relationship between cytokine profiles and persistent COVID-19. The authors compares cytokine levels in a follow-up cohort of COVID-19 patients recovered from acute infection. They explore if there is a relation between cytokine profile and the presence of persistent COVID-19 assessed with clinical questionnaires. They use healthy non-infected individuals as a controls for cytokine profiles.

The paper includes a limited number of patients (n=186), with a total of 101 patients reporting persistent COVID-19.

Interesting results may be outlined, as the relation between persistent COVID-19 and CRP levels and in the follow-up period and the early IL-1b levels as a predictor of persistent COVID-19.

Multicentric prospective study with a systematic work and a nice and detailed presentation of the results.

Reviewer #2: In this manuscript, Wynberg et al. investigated the role of immune dysregulation in the development of Post-acute sequelae of SARS-CoV-2. They analyzed pro and anti-inflammatory cytokine levels in serum longitudinally for 6 months to establish the correlation with PASC. The work is well done, but there are a few comments that should be addressed:

Did the authors investigate viral genes or infectious virus? It has been reported that persistent viral RNA could prolong the inflammatory response leading to long COVID.

Previous articles (PMID: 35846757, PMID: 35732153) show elevated TNF-a in long COVID patients. The disparity in data should be discussed.

Did the authors compare in the cytokines level in presence and absence of the vaccine? Did the authors analyze data in different strains of SARS-CoV-2?

**********

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Reviewer #1: No

Reviewer #2: No

**********

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PLoS One. 2024 Jul 15;19(7):e0304990. doi: 10.1371/journal.pone.0304990.r002

Author response to Decision Letter 0

13 Oct 2023

Please see a detailed point-by-point response in the attached file.

PLoS One. doi: 10.1371/journal.pone.0304990.r003

Decision Letter 1

Mickael Essouma

27 Nov 2023

PONE-D-23-20788R1Inflammatory profiles are associated with long COVID up to 6 months after illness onset: a prospective cohort study of individuals with mild to critical COVID-19PLOS ONE

Dear Dr. Wynberg,

Please submit your revised manuscript by Jan 11 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Mickael Essouma, M. D.

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

The manuscript has been improved. There are still important rooms for improvement.

The title needs to be free of ambiguity: "Inflammatory profiles are associated with long COVID up to 6 months after mild to critical COVID-19: data from the RECOVERED cohort"

Introduction: the second paragraph needs to be reduced. I would mention the current pathophysiologic hypotheses for long COVID (viral persistence, chronic inflammation, hypercoagulability and autonomic dysfunction: see https://doi.org/10.1038/s41577-023-00966-7) and then briefly describe what is known and unknown about the chronic inflammation theory (because your study dealt with the chronic inflammation theory). The third paragraph also needs to be reduced. You could say something like: "The aim of our study was to further explore the chronic inflammation theory about the pathophysiology of long COVID through a longitudinal assessment of inflammatory cytokines present in individuals with COVID-19 at disease onset."

Methods

Page 7 line 100: it will be helpful for the readers to see the reference for STROBE guidelines.

Line 103: before describing RECOVERED, you would like to state something like: "This is a subset of the RECOVERED study."

"We understand that a COVID-negative control group is not necessary to assess the impact of PASC on cytokine concentrations. However, for clinical relevance it is not only useful to know the relative

effect of PASC on cytokine concentrations but also whether the levels reported are of clinical significance. This can only be determined by observing cytokine levels in an uninfected, healthy reference population." I still do not see the importance of that group. You start the study already knowing the physiologic and abnormal levels of the serum cytokines you assessed in the study. You only need to mention the healthy individuals as laboratory controls when reporting about laboratory assay of serum cytokines. THERE IS NO CONTROL GROUP IN A COHORT STUDY. THE COHORT STUDY HAS TWO STUDY POPULATIONS: THOSE WHO GO ON TO DEVELOP THE DISEASE OF INTEREST (LONG COVID FOR THIS STUDY) AFTER CONTACT WITH THE EXPOSURE (COVID-19 FOR THIS STUDY), AND THOSE WHO DO NOT DEVELOP THE DISEASE (FOR THIS STUDY: THOSE WHO HAD covid-19 AND DID NOT DEVELOP LONG COVID).

After the sub-section "Study design and participant enrolment", please mention a section " Data collection " under which you detail the clinical and laboratory (do not forget to specify the cytokines assessed [ideally classified by families such as IFN-related cytokines, chemokines, TNF cytokines, interleukins: I am surprised I do not see IL-6 a very important pro-inflammatory cytokine on line 140] and say why you assessed specifically those cytokines since there are many pro-inflammatory cytokines; CD14 and CD163 are monocyte/macrophage surface markers, not cytokines I therefore do not understand why they are reported and CRP on line 138) data collected, the methods of data collection used, and the timings of data collection (to, at 3 months, at 6 months). So, this sub-section will contain information from the line 119 to the line 145. The title states that the total duration of the follow up for each participant was six months, so I do not understand why on line 131 you mention that you collected blood samples at 12, 18 , 24 months. After the data collection sub-section, add a sub-section termed "Outcome" which is very important for a cohort. The main outcome here being the serum levels of pro-inflammatory cytokines at 6 months post-COVID infection. After the outcome sub-section, then follow the definitions and statistical analysis sub-sections as you did.

Line 153 "COVID-19 clinical severity was categorised according to WHO criteria[20]". Please, describe those grades of severity.

Lines 154-156: "Diffusion capacity (DLCO) at 6 months after illness onset was defined as impaired according to American Thoracic Society (ATS) European Respiratory Society guidelines[21], as described previously described[19]". Is this not a secondary outcome of the study? If it is the case, then do not forget to mention that in the outcome sub-section.

Lines 159-161: "Socio-demographic, clinical and study characteristics were compared between included participants with and without PASC at 12 weeks after illness onset. To assess selection bias, we compared features of included and excluded participants." Please, delete.

Generally speaking, your statistical analysis sub-section is long, difficult to read and confusing. Please, state the software where you stored the data collected, whether you needed to export data to another software for statistical analysis, the statistical tests used for the different study outcomes, the statistical tests used to assess the factors associated with the study outcomes, the candidate factors

associated with the different outcomes as assessed in the statistical models, and the definition of statistical significance you used.

A cohort based on STROBE should start with the sub-section " Baseline characteristics of the study population", Then follow the sub-sections "primary outcome(s)" and "secondary outcomes". Then you can end the results section with the sub-section " factors associated with persistence of a pro-inflammatory profile in individuals with COVID-19".

Discussion: last time, I proposed a discussion plan. Please, stick to it. The paragraph about study's strengths and limitations should be shortened: regarding limitations, please stick to comments about the shortcomings of a 6-month prospective cohort study.

Conclusion

Line 380 "In summary" needs to be deleted. The conclusion should match with the main objective stated in the introduction, and call for further studies on the topic of your study.

Ultimately, the abstract will also need a revision, and should not exceed 300 words. The keywords should contain terms from the title and eventually the abstract.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #2: Yes

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PLoS One. 2024 Jul 15;19(7):e0304990. doi: 10.1371/journal.pone.0304990.r004

Author response to Decision Letter 1

2 Feb 2024

Dear Dr. Mickael Essouma,

Thank you for taking the time to review our article.

Please see our response to each specific comment in the attached file.

Attachment

Submitted filename: Response to the editor.docx

pone.0304990.s002.docx^{(19.4KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0304990.r005

Decision Letter 2

Mickael Essouma

12 Feb 2024

PONE-D-23-20788R2Inflammatory profiles are associated with long COVID up to 6 months after illness onset: a prospective cohort study of individuals with mild to critical COVID-19PLOS ONE

Dear Dr. Wynberg,

Please submit your revised manuscript by Mar 28 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Mickael Essouma, M. D.

Academic Editor

PLOS ONE

Journal Requirements:

Additional Editor Comments:

The manuscript has been improved, but there are some revisions that need to be made so that the published report matches the level of field work made.

Ref 16 should be revised.

Tables should come at the end of the full-text manuscript, to facilitate the assessment of the manuscript.

Lines 100-102: the citation of the WHO is needed.

Line 136: the reference provided is not right. Is there a link with reference 18 cited in line 160?

Lines 153 and 158: 21 or 20?

I advise against citing preprints. If you have to cite a preprint, specify that the reference is a preprint.

Line 175: consider writing "non-specific antinuclear antibodies" instead of "total antinuclear antibodies". Line 176: please rephrase "as indicators of possible auto-immunity" as "as markers of autoimmunity". Indeed, ANA are the best markers of autoimmunity.

Lines 182-183. I suggesting deleting "when assessing possible early biomarkers for later PASC" as it brings confusion to the message you want to give.

Line 191: ref [14] is not the WHO reference.

Line 198 American Thoracic Society European Respiratory Society should read American Thoracic Society/European Respiratory Society.

Line 199: "described" should be deleted.

Lines 204 and 205: "To assess selection bias, we compared features of included and excluded participants." This sentence is not right and should be deleted. In fact, all the paragraph in lines 203-207 is not necessary. The paragraph in lines 233-240 is difficult to understand. Are the early predictors of PASC you are talking about different from the pre-COVID and COVID-related factors you are talking about in the paragraph in lines 219-231? You say that you have measured the levels of non-specific ANA within 4 weeks of COVID onset. However, I do not see how you used that data in the predictor analysis. When describing the statistical analysis, focus on the description of: 1) The statistical tests used and specify the variables that were analysed using those statistical tests. Here again, focus on the study outcomes so that readers will be able to have similar results using those statistical tests. This is why I said that the paragraph in lines 203-207 is not necessary. 2) The definition of statistical significance for the outcomes of interest, 3) then how you reported the results. 4) finally, the software where you stored the data before the analysis and how you exported the data from that software to the statistical analysis software. It reads something like this:

All data were collected and stored in a secure database and exported for statistical analyses to Python. Analyses were performed using the statsmodels package (v. 0.13.2)[21], whilst the random forest regression analyses were performed in Python using the scikit-learn package (v.2441.1.3)."

Paragraph in lines 248-255: state the specific numbers, avoid the adverbs.

Table 1 is confusing and needs to be revised because it is supposed to display the baseline characteristics, but I see the 3-month data in that table. Follow-up data should not be in that table. I also do not understand why there is a word recovered in that table whereas study participants belong to the RECOVERED cohort. This may be confusing for readers. The table is unacceptably long. A shorter title such as "Baseline characteristics of study participants with and without PASC after COVID-19 infection" would be better. Then, you see that the relevant comparisons is that between the two study populations reported in the table's title. If you want to show the baseline characteristics of the other study population, do this in tables that you send to the supplementary material. Variables in table 1 should be those that you found relevant predictors of PASC, along with demographic characteristics. I would organize its arrows like this: demographic (age, sex, level of education) and epidemiological (e.g., COVID-19 vaccination status, comorbidities) data, followed by clinical data (symptomatic/asymptomatic at diagnosis, cardiorespiratory features, disease severity: mild, moderate, critical), and then management data (ambulatory/hospital/ICU care, drugs used: steroids, other immunomodulatory and immunosuppressive drugs). I would not mention the migration status. You can also merge all the comorbidities in a unique variable "comorbidities". I am happy to see the vaccination status in table 1, but I do not see it among the candidate predictors of PASC in the methods section. Please, make a comment about this in the statistical analysis sub-section of the Methods section.

I suggest deleting the text in lines 274-276 which may be confusing. It is already clear in the methods that you used reference samples in the laboratory when assessing serum levels of cytokines.

Lines 277-363: there should be consistency between the way you report these results and the report on study methods. For example, I see "in univariate analysis", "in multivariate analysis", but I have not seen that in the statistical analysis sub-section. You report that determinants of serum cytokine levels are part of the primary outcome, but this is not what I have read in the methods section. There is this problem of whether you make a difference between early predictors of PASC and pre-COVID and COVID-related factors highlighted above that is also apparent here. Instead of saying after "illness onset", state "after COVID-19 onset" throughout the text. Focus this report of results on data from participants with and without PASC after COVID-19 please. No need to emphasize on the population from which you collected reference samples. When you state in the text that this result is avilable in supplementary tables...Readers will themselves go and find in those tables results of the population from which you collected reference samples. This is the most crucial part of the result section. Write it in the simplest and most intelligible way referring readers to the figures that are very beautiful Draw our attention to the figures as much as you can.

A more exact name of Figure 1 is "Box and Whisker plots of serum cytokine levels in the study population at 9-12 and 21-24 weeks post-COVID". I can not assess the other figures until you resolve the inconsistency across the methods section and lines 277-363 of the results section.

Lines 366-367: do you want to say that you assessed the evolution of cytokines in individuals with PASC up to 6 months (24 weeks) post-COVID? The study objective, methods, results and discussion should be consistent. Please, take your time to fix these issues before submitting the revised version of the manuscript. You have strong results. Strengthen your discussion. The first paragraph of the discussion should summarize the study results in a way that is consistent with the objective stated in the introduction, as well as methods and results sections. The second paragraph should explain the results of Figure 1 taking into consideration data from the literature, and you know that this is an ever-evolving field. So, try as much as possible to have the latest evidence when making this discussion. In the subsequent paragraph, explain your results on predictors of high levels of pro-inflammatory serum cytokines and how those predictors and the pro-inflammatory cytokines could influence the pathogenesis of PASC.

Then discuss the implications of your research on this currently very famous research area. How do you advance our knowledge? What remains to be done to further elucidate the hypotheses you elaborated while interpreting your results. Finally, state the limitations and strengths of your study.

I am surprised that you measure non-specific ANA, and you did not report fhe titers of ANA in your study population (I would put them among clinical data in table 1. COVID-19 is a confirmed risk factor of autoimmune diseases. It is not clear whether autoimmunity is involved in the pathogenesis of PASC. I thought that may be you will provide an insight into the involvement of autoimmunity in the pathogenesis of PASc, especially since you know that interferons (e.g. IP-10) are involved in the pathogenesis of autoimmune diseases and autoantibody production s a downstream reaction of interferon signaling.

It is in the conclusion that it becomes very clear that you defined PASC as impaired DLCo in some circumstances. This is not so clear in the methods (lines 186-199) and results section, unfortunately. Clearly state in the methods section. The conclusion should be strong. Waht message would you want to remember if you forgot everything about your manuscript? Put that message in the conclusion. I guess it will fit with the study's main objective.

Format references according to the PLOS ONE policy.

[Note: HTML markup is below. Please do not edit.]

PLoS One. 2024 Jul 15;19(7):e0304990. doi: 10.1371/journal.pone.0304990.r006

Author response to Decision Letter 2

16 May 2024

Reviewer’s / editorial comments:

The manuscript has been improved, but there are some revisions that need to be made so that the published report matches the level of field work made.

Ref 16 should be revised: it is unclear how this reference should be revised, this reference refers to the STROBE guideline and checklist that is available on the mentioned website.

Tables should come at the end of the full-text manuscript, to facilitate the assessment of the manuscript: following this comment, table 1 is moved to the end of the manuscript, see p23

Lines 100-102: the citation of the WHO is needed: this reference was added

Line 136: the reference provided is not right. Is there a link with reference 18 cited in line 160? indeed reference 1 is incorrect: the right reference is indeed no 18: this is changed accordingly

Lines 153 and 158: 21 or 20? We are not sure where these numbers refer to, I cannot find them in the mentioned lines.

I advise against citing preprints. If you have to cite a preprint, specify that the reference is a preprint: we checked the manuscript for references to pre-prints and updated this (ref 15) to the publication

Line 175: consider writing "non-specific antinuclear antibodies" instead of "total antinuclear antibodies": at this point we disagree with the reviewer: antinuclear antibodies encompass specific antibodies (for instance adsDNA) and therefore cannot be named “non-specific”.

Line 176: please rephrase "as indicators of possible auto-immunity" as "as markers of autoimmunity". Indeed, ANA are the best markers of autoimmunity: this was changed accordingly.

Lines 182-183. I suggesting deleting "when assessing possible early biomarkers for later PASC" as it brings confusion to the message you want to give: this was changed accordingly.

Line 191: ref [14] is not the WHO reference. this was changed accordingly.

Line 198 American Thoracic Society European Respiratory Society should read American Thoracic Society/European Respiratory Society: this was changed accordingly.

Line 199: "described" should be deleted: this was changed accordingly.

Lines 204 and 205: "To assess selection bias, we compared features of included and excluded participants." This sentence is not right and should be deleted. In fact, all the paragraph in lines 203-207 is not necessary: in our opinion ot is relevant to assess selection bias therefore we decided to leave this section in (of note selection bias data are presented in table S1 and in the first section of the results paragraph).

The paragraph in lines 233-240 is difficult to understand. Are the early predictors of PASC you are talking about different from the pre-COVID and COVID-related factors you are talking about in the paragraph in lines 219-231? With earlier predictors of PASC we mean predicting factors at illness onset. This was clarified in the text (see l239).

You say that you have measured the levels of non-specific ANA within 4 weeks of COVID onset. However, I do not see how you used that data in the predictor analysis: please refer to results section lines 344- 346 and figure 4A: here we list the data on ANA as predictive factor for PASC.

When describing the statistical analysis, focus on the description of: 1) The statistical tests used and specify the variables that were analysed using those statistical tests. Here again, focus on the study outcomes so that readers will be able to have similar results using those statistical tests. This is why I said that the paragraph in lines 203-207 is not necessary. 2) The definition of statistical significance for the outcomes of interest, 3) then how you reported the results. 4) finally, the software where you stored the data before the analysis and how you exported the data from that software to the statistical analysis software. It reads something like this:

"We determined box and whisker plots of serum concentrations of cytokines over time. During this analysis, data of study participants with and without PASC at 12 and 24 weeks after COVID-onset were compared with data from the uninfected healthy control population using the Mann-Whitney and Bonferroni correction tests. We segregated serum cytokine levels at 21-24 weeks post-COVID by DLCO status (normal vs abnormal). We subsequently used the linear mixed-effects test to assess the effect of pre-COVID and COVID-related factors on serum concentrations of cytokines at 12 and 24 weeks post-COVID. The pre-COVID factors assessed included.... [reference important to show that you chose them based on the literature]. The COVID-related factors assessed included ....[reference again]. We used a correlation matrix to help assess the reciprocal associations between cytokines at 9-12 and 21-24 weeks post-COVID. Add a comprehensive synthesis of the paragraph in lines 233-240 here if this paragraph is still necessary. All data were collected and stored in a secure database and exported for statistical analyses to Python. Analyses were performed using the statsmodels package (v. 0.13.2)[21], whilst the random forest regression analyses were performed in Python using the scikit-learn package (v.2441.1.3).": in our opinion the statics paragraph does explain in a logical order the test used and is displayed in analogy to the results section. However, we added the suggestions of the reviewer and also more clearly highlighted which sections belong to the different study outcomes.

Paragraph in lines 248-255: state the specific numbers, avoid the adverbs: I am unsure what the reviewer means, in this section we do list the numbers and I do not see irrelevant adverbs.

Table 1 is confusing and needs to be revised because it is supposed to display the baseline characteristics, but I see the 3-month data in that table: this may be a misunderstanding: table 1 does show the baseline characteristics (second column) and the baseline characteristics of groups stratified for participants that later in the study develop PASC (third and fourth column). To clarify this information was added to the legend of the table.

Follow-up data should not be in that table: please refer to our previous answer

I also do not understand why there is a word recovered in that table whereas study participants belong to the RECOVERED cohort. This may be confusing for readers: it is difficult to find a proper synonym for recovered (from illness symptoms). We changed it for now in restored symptoms and ongoing symptoms.

The table is unacceptably long. A shorter title such as "Baseline characteristics of study participants with and without PASC after COVID-19 infection" would be better: this was changed accordingly

Then, you see that the relevant comparisons is that between the two study populations reported in the table's title. If you want to show the baseline characteristics of the other study population, do this in tables that you send to the supplementary material. Variables in table 1 should be those that you found relevant predictors of PASC, along with demographic characteristics: according to the comment the table 1 was shortened and the following data: sex, BMI, initial disease severity, vaccination and demographic predictors were included. All other baseline data that we consider relevant in order to have insight in the study population characteristics were moved to a new supplementary table (s2).

I would organize its arrows like this: demographic (age, sex, level of education) and epidemiological (e.g., COVID-19 vaccination status, comorbidities) data, followed by clinical data (symptomatic/asymptomatic at diagnosis, cardiorespiratory features, disease severity: mild, moderate, critical), and then management data (ambulatory/hospital/ICU care, drugs used: steroids, other immunomodulatory and immunosuppressive drugs). I would not mention the migration status. You can also merge all the comorbidities in a unique variable "comorbidities": since the data displayed in the table are no re-arranged (see previous answer) some of the here mentioned factors are now in suppl table 2.

I am happy to see the vaccination status in table 1, but I do not see it among the candidate predictors of PASC in the methods section. Please, make a comment about this in the statistical analysis sub-section of the Methods section: vaccination is already mentioned in the statistics section: see lines 230 and 231.

I suggest deleting the text in lines 274-276 which may be confusing. It is already clear in the methods that you used reference samples in the laboratory when assessing serum levels of cytokines: we suggest to leave this in since the text here (in the current revised ms (v3) l261-263) lists the characteristics of the reference samples which belong in the results section.

You report that determinants of serum cytokine levels are part of the primary outcome, but this is not what I have read in the methods section: this was added to the methods section (l212).

There is this problem of whether you make a difference between early predictors of PASC and pre-COVID and COVID-related factors highlighted above that is also apparent here. Instead of saying after "illness onset", state "after COVID-19 onset" throughout the text: accordingly, illness onset is replaced by COVID-19 onset throughout the text of the manuscript.

Focus this report of results on data from participants with and without PASC after COVID-19 please. No need to emphasize on the population from which you collected reference samples. When you state in the text that this result is available in supplementary tables...Readers will themselves go and find in those tables results of the population from which you collected reference samples. This is the most crucial part of the result section. Write it in the simplest and most intelligible way referring readers to the figures that are very beautiful Draw our attention to the figures as much as you can: we thank the reviewer for this compliment

A more exact name of Figure 1 is "Box and Whisker plots of serum cytokine levels in the study population at 9-12 and 21-24 weeks post-COVID": the title was changed accordingly

I can not assess the other figures until you resolve the inconsistency across the methods section and lines 277-363 of the results section: please refer to our answer at the top of page 2.

Lines 366-367: do you want to say that you assessed the evolution of cytokines in individuals with PASC up to 6 months (24 weeks) post-COVID?: indeed, that was also stated as such in the abstract: “We described longitudinal trajectories of cytokines in adults up to 6 months following SARS-CoV-2 infection and explored early predictors of PASC”.

The study objective, methods, results and discussion should be consistent. Please, take your time to fix these issues before submitting the revised version of the manuscript. You have strong results. Strengthen your discussion. The first paragraph of the discussion should summarize the study results in a way that is consistent with the objective stated in the introduction, as well as methods and results sections: we re-read this first paragraph and checked the consistence with the abstract as well as our primary and secondary aims and feel that as the text is now there is consistence throughout the manuscript. The reviewer suggests to “strengthen the discussion”, if that is indeed needed then specific suggestions would be welcome.

The second paragraph should explain the results of Figure 1 taking into consideration data from the literature, and you know that this is an ever-evolving field. So, try as much as possible to have the latest evidence when making this discussion: following this comment a few recent landmark papers were added as reference (Peluso Semin Imm 2024; Yin Nat Imm 2023; Thaweethai JAMA 2023).

In the subsequent paragraph, explain your results on predictors of high levels of pro-inflammatory serum cytokines and how those predictors and the pro-inflammatory cytokines could influence the pathogenesis of PASC: we added this to the discussion (lines 492-498).

Then discuss the implications of your research on this currently very famous research area. How do you advance our knowledge? What remains to be done to further elucidate the hypotheses you elaborated while interpreting your results: this was added to the final part of the conclusion (lines 528-532)

Finally, state the limitations and strengths of your study: We think the reviewer has overlooked the limitation as listed in the discussion see lines 508-520

I am surprised that you measure non-specific ANA, and you did not report the titers of ANA in your study population (I would put them among clinical data in table 1. COVID-19 is a confirmed risk factor of autoimmune diseases. It is not clear whether autoimmunity is involved in the pathogenesis of PASC. I thought that may be you will provide an insight into the involvement of autoimmunity in the pathogenesis of PASc, especially since you know that interferons (e.g. IP- 10) are involved in the pathogenesis of autoimmune diseases and autoantibody production s a downstream reaction of interferon signaling: we agree with the reviewer that there are several studies that show a potential role for auto-immunity in development of PASC. There are however different categories of autoimmunity that each have a different pathophysiological background (Bodansky JCI Insight 2023, Peluso CID 2022, ). ANA abs for instance are a different category than antibodies against type 1 IFNs or cytokines. It was beyond the scope of our study to generate a comprehensive analysis of all types of auto-antibodies and we only included ANA abs. Following the suggestion, we added the titers of ANA to table 1.

It is in the conclusion that it becomes very clear that you defined PASC as impaired DLCo in some circumstances. This is not so clear in the methods (lines 186-199) and results section, unfortunately. Clearly state in the methods section: the suggestion that we define PASC by impaired DLCo seems a misinterpretation of the reviewer. We define PASC based on self-reported symptoms (l 159 ev “PASC was defined as reporting at least one COVID-19 symptom that, from COVID-19 onset, occurred within one month and continued beyond 12 weeks”). The section lines 383-385 in the results section may have been confusing: “when substituting PASC status for impaired diffusion capacity, we found that impaired diffusion capacity was associated with higher levels of CRP, IL6, TNFα, IP10, IL10 and IL17 in multivariable analyses.”. For clarification we reformulated this sentence and omitted PASC status (see adjustment in results l383). Finally, we reformulated lines 514-516 in the discussion to better align with our previous explanation of the approach taken.

The conclusion should be strong. What message would you want to remember if you forgot everything about your manuscript? Put that message in the conclusion. I guess it will fit with the study's main objective: see adjustment in the conclusion (l534-538).

Format references according to the PLOS ONE policy: the references in text and reference list were adjusted.

Attachment

Submitted filename: Response to Reviewers.docx

pone.0304990.s003.docx^{(26.5KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0304990.r007

Decision Letter 3

Mickael Essouma

22 May 2024

Inflammatory profiles are associated with long COVID up to 6 months after COVID-19 onset: a prospective cohort study of individuals with mild to critical COVID-19

PONE-D-23-20788R3

Dear Dr. Wynberg,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Mickael Essouma, M. D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

I am unable to open the website (https://www.strobe478statement.org/checklists/) provided with reference 17.

Line 324: consider replacing ref 20 with ref 24 and removing the appended comment in the red box.

Consider removing the comment appended to line 400.

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0304990.r008

Acceptance letter

Mickael Essouma

27 May 2024

PONE-D-23-20788R3

PLOS ONE

Dear Dr. Wynberg,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team.

At this stage, our production department will prepare your paper for publication. This includes ensuring the following:

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on behalf of

Dr. Mickael Essouma

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 File

(DOCX)

pone.0304990.s001.docx^{(1.9MB, docx)}

Attachment

Submitted filename: Response to the editor.docx

pone.0304990.s002.docx^{(19.4KB, docx)}

Attachment

Submitted filename: Response to Reviewers.docx

pone.0304990.s003.docx^{(26.5KB, docx)}

Data Availability Statement

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PERMALINK

Inflammatory profiles are associated with long COVID up to 6 months after COVID-19 onset: A prospective cohort study of individuals with mild to critical COVID-19

Elke Wynberg

Alvin X Han

Hugo D G van Willigen

Anouk Verveen

Lisa van Pul

Irma Maurer

Ester M van Leeuwen

Joost G van den Aardweg

Menno D de Jong

Pythia Nieuwkerk

Maria Prins

Neeltje A Kootstra

Godelieve J de Bree

Roles

Abstract

Background

Methods

Results

Conclusions

Background

Methods

Study design and participant enrolment

Data collection

Outcomes

Definitions

Statistical analysis

Results

Baseline characteristics of the study population

Table 1. Characteristics of study participants with and without PASC at 12 weeks after COVID-19 onset.

Primary outcome: Cytokine levels at 9–12 and 21–24 weeks after COVID-19 onset and their determinants

Fig 1. Box and Whisker plots of serum cytokine levels in the study population at 9–12 and 21–24 weeks post-COVID.

Fig 2. Multivariate linear regression analysis of factors associated with inflammatory marker concentrations at 9–12 weeks after COVID-19 onset.

Fig 3. Multivariate linear regression analysis of factors associated with inflammatory marker concentrations at 21–24 weeks after COVID-19onset.

Secondary outcome: Early predictors of PASC and ongoing inflammation 6 months after COVID-19 onset

Fig 4. Early (0–4 week) predictors of PASC and CRP/IL-6 levels at 21–24 weeks after COVID-19 onset.

Discussion

Conclusions

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Mickael Essouma

Roles

Author response to Decision Letter 0

Decision Letter 1

Mickael Essouma

Roles

Author response to Decision Letter 1

Decision Letter 2

Mickael Essouma

Roles

Author response to Decision Letter 2

Decision Letter 3

Mickael Essouma

Roles

Acceptance letter

Mickael Essouma

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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