Dynamics of cytokines, immune cell counts and disease severity in patients with community-acquired pneumonia – Unravelling potential causal relationships
Introduction
Community-acquired pneumonia (CAP) is a serious disease with high morbidity and mortality, often requiring hospitalization. Even in case of proper clinical treatment, the disease course can be highly dynamic with rapid serious deterioration requiring intensive care treatment. Cytokines released during the inflammatory response of the host to the infection have been shown to be predictors of treatment failure and mortality [1], [2], and factors influencing the patterns of cytokine concentrations in CAP have been investigated [3]. However, the complex interaction of cytokines, immune cells and disease severity is still only partly understood and analyses in humans are hampered by the lack of detailed time series data. While cross-sectional data only allow the analysis of associations, time series data can be used to analyze not only the mean levels as is usually done, but also the rates of change over time of cytokines and other parameters and their predictive value regarding disease severity or single organ failure. A further limitation of the current research on CAP is that correlations computed across patients do not necessarily reflect the dynamic relations that link two or more parameters within patients over time [4], [5], and do not provide information about the direction of the inferred relationships, i.e., the causality between players of the immune response.
In our PROGRESS study, we close this gap by collecting comprehensive characteristics of CAP patients at baseline and over 4–5 days of hospitalization, including daily measurements of several cytokines, immune cell counts and parameters of disease severity included in the Sequential organ failure assessment score (SOFA), which was identified as an excellent operationalization of CAP severity [6]. Making use of this rich resource, in the present analysis of these data, we aim at answering the following questions of increasing complexity: First, we are interested in which time-invariant factors (e.g., age, sex, BMI) correlate with cytokine dynamics, immune cells, and SOFA parameters. This will reveal potential confounders of associations of cytokines with disease progression or provide hints towards modifiable or physiological risk factors such as age. Second, we study correlations between the time courses of any two cytokines, immune cell counts or SOFA parameters to identify relationships between immune response and organ function over time. Third, we establish potentially causal lagged relationships between parameters, in the sense that the value of one parameter influences the value of another parameter on the next day. This insight is relevant, e.g., to identify valid therapy targets.
Section snippets
Study subjects
Study subjects were recruited in the framework of the PROGRESS study (clinicaltrials.gov identifier: NCT02782013), which is a multi-center clinical observational study of hospitalized patients with CAP. In brief, these patients were recruited, if they had a working diagnosis of pneumonia, were 18 or more years old, and they or their legal representatives signed a consent to participate. To exclude nosocomial infections (hospital-acquired pneumonia), patients staying in hospital during the
Clinical characteristics of study population
We analyzed data of 403 CAP patients for whom measurements of circulating cytokines were available. Baseline characteristics of these patients are shown in Table 1.
Factors determining SOFA components and cytokines
First, we analyzed each of the repeatedly measured parameters (10 cytokines, SOFA components and blood parameters, see Additional file 2, Table S1) separately regarding their dependence on a set of patient characteristics and risk factors for severe disease courses. These factors include age, sex, BMI, (number of years of) smoking,
Discussion
Understanding disease dynamics in pneumonia patients is of high importance to develop successful surveillance and intervention strategies but requires appropriate time series data of patients. Here, we present and analyze data of the PROGRESS cohort for which a sufficiently rich set of time series of cytokines, immune cells and disease states is available.
In the first step of the present study, we analyzed associations of several baseline characteristics of our CAP patients with their time
Ethics approval and consent to participate
Ethics approval was issued by the ethics committee of the University of Jena (2403-10/08) and by locally responsible ethics committees of each study center.
Availability of data and materials
The data that support the findings of this study are available from PROGRESS steering committee but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of PROGRESS steering committee.
Funding
The PROGRESS study is funded by the German Federal Ministry of Education and Research, grant numbers 01KI07110 (Giessen), 01KI07111 (Jena), 01KI07113 (Leipzig), 01KI07114 (Berlin), 01KI1010I (Leipzig), and 01KI1010D (Greifswald). The German Federal Ministry of Education and Research has no influence on the design of the study, on collection, analysis, and interpretation of data, or on writing the manuscript.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
We thank the patients, their relatives and legal guardians very much for participation in the PROGRESS study and The PROGRESS Study Group for recruiting and data collection.
References (53)
- et al.
Bivariate linear mixed models using SAS proc MIXED
Comput. Methods Programs Biomed.
(2002) - et al.
Association of serum bilirubin with aging and mortality
J. Clin. Exp. Hepatol.
(2014) - et al.
The effect of age on the systemic inflammatory response in patients with community-acquired pneumonia
Clin. Microbiol. Infect.
(2014) - et al.
Host and Environmental Factors Influencing Individual Human Cytokine Responses
Cell
(2016) - et al.
IL-6 and Its Soluble Receptor Orchestrate a Temporal Switch in the Pattern of Leukocyte Recruitment Seen during Acute Inflammation
Immunity
(2001) - et al.
Effect of pro-inflammatory cytokines on spontaneous apoptosis in leukocyte sub-sets within a whole blood culture
Cytokine
(2005) - et al.
Molecular inflammatory responses measured in blood of patients with severe community-acquired pneumonia
Clin. Diagn. Lab. Immunol.
(2003) - et al.
Causes and predictors of nonresponse to treatment of intensive care unit-acquired pneumonia
Crit. Care Med.
(2004) - et al.
Factors associated with inflammatory cytokine patterns in community-acquired pneumonia
Eur. Respir. J.
(2011) - E.L. Hamaker, Why researchers should think “within-person”: A paradigmatic rationale, in: Handbook of research methods...
The separation of between-person and within-person components of individual change over time: A latent curve model with structured residuals
J. Consult. Clin. Psychol.
Sequential organ failure assessment score is an excellent operationalization of disease severity of adult patients with hospitalized community acquired pneumonia - results from the prospective observational PROGRESS study
Crit. Care
PROGRESS - prospective observational study on hospitalized community acquired pneumonia
BMC Pulm. Med.
The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure
On behalf of the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine, Intensive Care Med.
A Two-Compartment Mathematical Model of Endotoxin-induced Inflammatory and Physiologic Alterations in Swine
Crit. Care Med.
Dynamics of a Cytokine Storm
PLoS ONE
Toward computational identification of multiscale “tipping points” in acute inflammation and multiple organ failure
Ann. Biomed. Eng.
The acute inflammatory response in diverse shock states
Shock
Interactions between coagulation and complement–their role in inflammation
Semin. Immunopathol.
The function of neutrophils in sepsis
Curr. Opin. Infect. Dis.
A Random-Effects Model for Multiple Characteristics With Possibly Missing Data
J. Am. Stat. Assoc.
Studying Multivariate Change Using Multilevel Models and Latent Curve Models
Multivariate Behav. Res.
Estimating correlation by using a general linear mixed model: evaluation of the relationship between the concentration of HIV-1 RNA in blood and semen
Stat. Med.
Random-effects models for multivariate repeated measures
Stat. Methods Med. Res.
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing, Journal of the Royal Statistical Society
Series B
Cited by (6)
Direct bilirubin: A predictor of hematoma expansion after intracerebral hemorrhage
2023, American Journal of Emergency MedicineCYTOKINE PROFILE OF BRONCHOALVEOLAR SECRETION IN PROLONGED COURSE OF COMMUNITY ACQUIRED PNEUMONIA
2022, Medicni PerspektiviUlinastatin plus biapenem for severe pneumonia in the elderly and its influence on pulmonary function and inflammatory cytokines
2021, American Journal of Translational Research