1. Introduction
Cytokines are signaling proteins secreted primarily by immune cells such as macrophages, T cells, dendritic cells, and other cell types in response to stimuli. These proteins play a critical role in regulating blood cell development, activation, and communication. The interaction between cytokines and blood cells is a cornerstone of immune system regulation and coordination. The interplay between blood cells and cytokine levels is an area of active research in immunology and systemic health. These associations provide insights into the interactions between the cytokine system and blood cells. The local and systemic inflammatory processes lead to cell activation, but the interactions between cytokines and blood cell counts are two-way communications [1,2,3].
High levels of white blood cells are often frequently associated with increased levels of pro-inflammatory cytokines (e.g., IL-1β, IL-6, TNF-α) in the blood [4,5]. This correlation indicates a systemic inflammatory response, where pro- and anti-inflammatory cytokines interrelate with a complex impact on the immune system and eventually cause multiple organ effects [6]. Elevated neutrophil counts, often seen in bacterial infections, can correspond to higher levels of cytokines, which attract more neutrophils to the site of infection or inflammation [7,8]. Neutrophil cells possess the ability to form neutrophil extracellular traps, which are released extracellularly and enable neutrophils to help counteract not only bacteria but also several other microbes, playing a crucial role in immune defense. However, excessive neutrophil extracellular entrapment may drive a massive inflammatory response and contribute to morbidity and mortality in many infectious diseases. Neutrophil extracellular traps also occur in noninfectious conditions and function as inflammatory mediators by degrading proinflammatory cytokines and chemokines [9,10,11].
Changes in lymphocyte counts, particularly T cells, can influence the levels of regulatory cytokines [12], which help to maintain immune balance and prevent excessive inflammation. Monocytes, which differentiate into macrophages and dendritic cells, are associated with cytokines like IL-1β, IL-6, and TNF-α [13]. Higher monocyte counts can lead to elevated levels of these cytokines, reflecting an active immune surveillance and response [14,15].
Even though leukocytes are the main type of cells associated with the cytokine network, erythrocytes are involved in both cytokine expression and signaling [16,17], as are platelets, which store and release a large number of both cytokines and chemokines [18,19,20]. Erythrocytes are involved in a multitude of actions, and apart from being oxygen carriers, erythrocytes may also act as cytokine reservoirs, enabling crosstalk with the immune system. Mature erythrocytes lack both nuclei and membrane-bound organelles and are thought to rely on their finite proteome throughout their ~120-day circulatory lifespan. Still, multiple lines for translation occur in mature erythrocytes, e.g., globin mRNAs are translated in mature erythrocytes, which are required for the maintenance of a normal level of globin proteins [21,22].
Cytokine families, their interactions, and their relations are crucial for the understanding of both physiological and pathophysiological reactions and events [23,24,25].
Systemic conditions such as diabetes, cardiovascular diseases, and autoimmune disorders often display altered blood cell counts and increased pro-inflammatory cytokine levels. For example, patients with diabetes may have elevated blood neutrophils and higher levels of IL-6 and TNF-α in saliva, indicating both systemic and local inflammation [26,27,28,29]. Psychological stress can also alter both blood cell counts and cytokine levels. Stress-induced leukocytosis can be accompanied by elevated levels of stress-related cytokines (e.g., IL-6) [30,31].
In summary, blood cell counts and plasma cytokine levels are interrelated, with changes in systemic immune parameters often mirrored by local cytokine production. These associations help in understanding the interplay between systemic and local immune responses and may be used in the diagnosis and monitoring of various health conditions. Although cytokines are a very crucial biological network, there is still a lack of understanding of the interactions between blood cells and the cytokine network. An important step in understanding the role of these biological mediators is to simultaneously study several cytokines to achieve a broader picture of the cytokine network rather than characterize individual cytokine responses. The aim of this study was to investigate the association between a broad range of cytokines, chemokines, growth factors, and enzymes in plasma with blood cell counts.
2. Results
2.1. Patient Characteristics
The cohort consisted of 165 individuals (53 males) with complete blood cell counts and PEA data. Mean age was 29 years, and interquartile range was 23–35 years.
The basic characteristics of the study cohort are presented in Table 1.
Numeric values for correlations and p-values between cytokines and related blood cells and blood cell markers are seen in Table 2. Analyses were performed by a proximity extension assay (PEA) (see Section 3 and Section 4.4).
The cytokines, chemokines, and growth factors analyzed by the Proseek Multiplex Inflammation kit are seen in Table S1.
Table S2 presents the correlations between measured cytokines and clinical variables. Statistically significant correlations were subsequently analyzed using multivariate adjustment (see Section 4.5).
2.2. Correlations Between Platelet Count and Cytokine and Chemokine Levels
The platelet counts were significantly associated with FGF-23, IL-10RB, LAP TGF-beta-1, CCL19, SIRT2, IL6, VEGFA, AXIN1, CASP-8, CD40, PD-L1, CXCL5, MCP-3, STAMBP, CCL3, NRTN, HGF, CD5, TRAIL, CCL4, ADA. TNFSF14, and MCP-4 in increasing the Benjamini–Hochberg p-values order (Table 2). In the String plot for the cytokines that were significantly related to platelet counts, IL6 had a central role (Figure 1). The strongest associations with the platelet count were observed for MCP-3 and STAMBP (p = 0.052, both) and for CCL3 (p = 0.054), respectively.
2.3. Correlations Between White Blood Cell Count and Cytokine Levels
The white blood cell counts were significantly associated with OSM, IL6, HGF, TNFSF14, EN-RAGE, TGF-alpha, CCL19, and CCL11in order of increasing p-values (Table 2). The greatest number of associations in the String plot was between OSM and IL6 (Figure 2). The strongest associations with the WBC were observed for OSM (p = 1.28 × 10−7), IL-6 (p = 0.002), and HGF (p = 0.017), respectively.
We included both the white blood cell count and the neutrophil cell count, as they are important clinical biomarkers. The white blood cell count comprised eight significant associations with cytokines, whereas the neutrophil count was associated with six out of these cytokines. TGF-alpha and CCL11 were not associated with the neutrophil count; otherwise, the associations that were found between both the white blood cell count and the neutrophil count, respectively, were in the same magnitude.
The neutrophil count showed similar associations as white blood cell counts and were significantly associated with OSM, IL6, TNFSF14, HGF, EN-RAGE, and SCF (Table 2). Similar to white blood cells, the most associations in the String plot were observed between OSM and IL6 (Figure 3). The strongest associations with the neutrophil cell count were observed for OSM (p = 4.73 × 10−9), IL-6 (p = 0.002), and TNFSF14 (p = 0.040), respectively.
2.4. Correlations Between Cytokine Levels and Red Blood Cell Count
The red blood cell counts were significantly associated with TRAIL, IL-18R1, HGF, MCP-1, TRANCE, TNFRSF9, ADA, EN-RAGE, and CASP-8 (Table 2). The greatest number of associations in the String plot was noted between TNFSF10 and Casp-8 (Figure 4). The strongest associations with the red blood cell count were observed for TRAIL, IL-18R1, and HGF, respectively (p = 0.017, all).
2.5. Correlations Between Cytokine Levels and the Erythrocyte Volume Fraction
The erythrocyte volume fraction was significantly associated with TRAIL, MCP-1, HGF, TNFRSF9, ADA, DNER, CCL19, IL-10RB, IL18, TWEAK, CST5, PD-L1, CCL3, uPA, EN-RAGE, TRANCE, IL-18R1, TGF-alpha, and IL8 (Table 2). The most expressed associations were noted between TRAIL (p = 0.001), MCP-1 (p = 0.002), and HGF (p = 0.006), respectively (Figure 5).
The hemoglobin values were significantly associated with TRAIL, MCP-1, HGF, ADA, DNER, TNFRSF9, IL18, uPA, EN-RAGE, TWEAK, and TRANCE (Table 2).
There were also significant associations between MCHC and IL-12B and between MCV and IL-18R1.
3. Discussion
All studied PEA markers in this comparison have previously been proposed as either markers of inflammation or cardiovascular risk or both. The PEA assay unites high specificity, sensitivity, and low sample consumption and is therefore well suited for the detection of protein biomarkers [32]. We found several significant associations between blood cell parameters and cytokine levels, not only for the white blood cells but also for red blood cells and platelets. We also showed that within each cell type, there were significant associations between different cytokines, even when they did not belong to the same cytokine family (e.g., erythrocytes were associated with TNFSF9/10/11/12, IL-18, and CCL2), which were interrelated. It is noteworthy that the ten highest correlations between cell types and associated cytokine levels also had the lowest significance levels. There was no particular cell type, cytokine, or even cytokine family that constituted any link between these cells or cytokines. Since the most significant association was noted between the neutrophil count and OSM, followed by white blood cells and OSM, it is tempting to speculate that the main effect of white blood cells on OSM refers to neutrophils. We also noted associations, in decreasing order, between the number of white blood cells, neutrophil count, platelet count, and IL-6. We did not find any association between the platelet count and OSM, even though this cytokine belongs to the same family as IL-6 [33]. TRAIL, one of the cytokines with high correlation and low significance level, belonging to the TNF family [34], was, in decreasing order, significantly correlated to the erythrocyte volume fraction, hemoglobin, erythrocyte count, and, to some extent, also to the platelet count.
Our findings align with prior research. Several of the cytokines studied showed associations with platelet counts. FGF-23 has previously been shown to have a positive association with mean platelet volume [35]. Patients treated with rhuIL-10 developed decreased hemoglobin values and platelet counts [36]. Platelets were a major source of TGF-beta1 in the blood [37]. Regulation of protein acetylation by SIRT2 had an important role for platelet function [38]. IL-6 was shown to stimulate megakaryocytes to increase platelet production [39]. The interaction between CD40 and CD40L contributed to the binding of platelets to white blood cells [40]. Thus, there were several links between the platelet counts and the significantly associated cytokines in this study.
Cytokine–blood cell interactions have played a crucial role in developing therapeutic applications for various diseases, particularly in immunology, oncology, and inflammatory conditions. Some examples of these therapeutic applications are: Recombinant human erythropoietin (EPO) is used to treat anemia associated with chronic kidney disease and has improved the quality of life for kidney dialysis patients. G-CSF analogs (filgrastim) are used to treat neutropenia, especially in chemotherapy-induced neutropenia and congenital neutropenia, reducing the risk of infections. IL-6 inhibitors (tocilizumab, sarilumab) are used to treat rheumatoid arthritis, giant cell arteritis, and cytokine release syndrome from CAR-T cell therapy. TNF inhibitors (infliximab, adalimumab, etanercept) are used in treating autoimmune diseases like rheumatoid arthritis, Crohn’s disease, ulcerative colitis, psoriasis, and ankylosing spondylitis [41,42,43,44].
Limitations
This study has several limitations. Firstly, it was a single-site study. Secondly, due to geographic factors, the majority of the patients were Caucasians. Thirdly, those who were recruited were within a limited age span. Furthermore, the patients were not subjected to laboratory screening for co-existing diseases; instead, they were assessed clinically and deemed to be in good health. Thus, we cannot deduce to what extent, if any, either acute or chronic diseases may affect the associations between blood cell parameters, cytokines, chemokines, and growth factors. No protease inhibitor was used during sample collection, which means some degree of proteolysis may have occurred in the samples.
4. Materials and Methods
4.1. Population
This study included 165 essentially healthy patients, aged 18 to 44 years, weighing between 50 and 115 kg. All participants were previously referred to the Oral and Maxillofacial Surgery Clinic at Falun County Hospital, Sweden, for the surgical removal of an impacted lower third molar. Subjects with well-compensated systemic disease were accepted for inclusion in this study. The exclusion criteria were described in a previous publication [45].
4.2. Sampling Procedures
Blood samples were collected in vacutainer tubes while the patient was in the supine position. A peripheral venous catheter was used for sample collection. Complete blood cell counts were analyzed at the routine laboratory of Falun Hospital, Sweden. All blood samples were obtained prior to surgery. Plasma cytokine samples (EDTA) were frozen and stored at −80 °C until analysis.
4.3. Ethics
This study was conducted in accordance with the principles of the Helsinki Declaration [46]. It received approval from the Swedish Ethical Review Authority (Dnr 2015/378) and was registered in the European Union Regulating Authorities Clinical Trials Database (EudraCT) under number 2014-004235-39. Additionally, it is listed on
4.4. Proximity Extension Assay (PEA)
The proximity extension assay (PEA) was performed using the Proseek Multiplex Inflammation kit (Olink Bioscience, Uppsala, Sweden) [32]. In brief, 1 µL of plasma was combined with 3 µL of incubation mix, containing two probes (antibodies conjugated with unique DNA oligonucleotides). The mixture was incubated at 8 °C overnight.
Following incubation, 96 µL of extension mix, which included the PEA enzyme and PCR reagents, was added. The samples were then incubated at room temperature for 5 min before undergoing 17 cycles of DNA amplification in a thermal cycler.
A 96.96 Dynamic Array IFC (Fluidigm, South San Francisco, CA, USA) was prepared and primed according to the manufacturer’s guidelines. In a separate plate, 2.8 µL of the sample mixture was combined with 7.2 µL of detection mix, and 5 µL of this solution was loaded into the right side of the primed 96.96 Dynamic Array IFC. Unique primer pairs for each cytokine were loaded into the left side of the array. The protein expression analysis was then performed using the Fluidigm Biomark reader, following the Proseek protocol. Each Proseek kit quantified 92 biomarkers, resulting in a total analysis of 194 biomarkers in the study.
4.5. Statistical Analysis
Coefficients of variation were analyzed using Spearman rank correlations with Statistica (StatSoft (Version 14), Tulsa, OK, USA). Cytokine values that were above or below the highest and lowest standard points were assigned the values of these standards. To address the increased risk of false discoveries when analyzing a large number of correlations, p-values were adjusted for multiplicity using the false discovery rate (FDR) approach [47]. Adjusted p-values less than 0.10, corresponding to an expected false discovery rate of at most 10%, were considered significant. Results were adjusted for multiple testing, with a significance threshold of p < 0.05.
5. Conclusions
The observed associations between blood cell counts and circulating cytokines suggest potential applications in diagnostic and monitoring strategies for immune-mediated diseases and inflammatory conditions. For example, platelet count and IL-6, which showed strong associations in this study, have previously been linked to cardiovascular risk and inflammatory disorders [48]. Moreover, the identification of intercytokine interactions across different families may reflect coordinated inflammatory pathways that are relevant for disease progression, stratification, therapeutic response, and novel biomarker medicine approaches [49]. Future studies are warranted to validate these findings in disease cohorts and to explore their clinical utility in monitoring treatment response or predicting disease progression.
L.B.E.: conceptualization, methodology, investigation, data curation, formal analysis, visualization, writing—original draft, writing—review and editing, project administration, and resources. A.O.L.: conceptualization, methodology, formal analysis, writing—original draft, and writing—review and editing. M.B.E.: conceptualization, methodology, formal analysis, writing—original draft, and writing—review and editing. T.G.: conceptualization, methodology, formal analysis, writing—review and editing, and resources. All authors have read and agreed to the published version of the manuscript.
The study was conducted in accordance with the ethical principles originating from the 1975 Declaration of Helsinki, which was revised in 2013 [
All patients signed the informed consent form at screening before any study-specific procedures commenced.
Under Swedish Law, the authors cannot share the data used in this study and cannot conduct any further research other than what is specified in the ethical permissions application. For inquiries about the data, researchers should first contact the owner of the database, the University of Uppsala, Sweden. Please contact the corresponding author with requests for and assistance with data. If the university approves the request, researchers can submit an application to the Regional Ethical Review Board for the specific research question that the researcher wants to examine.
We are grateful to Charina Brännström for skilled technical assistance.
The authors declare no conflicts of interest.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Figure 1 The figure represents a network of interactions among cytokines, demonstrating significant associations between circulating cytokine levels and platelets. The nodes (cytokines) are connected by edges of varying thickness, indicating interaction strength.
Figure 2 The figure represents a network of interactions among cytokines, demonstrating significant associations between circulating cytokine levels and white blood cells. The nodes (cytokines) are connected by edges of varying thickness, indicating interaction strength.
Figure 3 The figure represents a network of interactions among cytokines, demonstrating significant associations between circulating cytokine levels and neutrophils. The nodes (cytokines) are connected by edges of varying thickness, indicating interaction strength.
Figure 4 The figure represents a network of interactions among cytokines, demonstrating significant associations between circulating cytokine levels and the erythrocyte count. The nodes (cytokines) are connected by edges of varying thickness, indicating interaction strength.
Figure 5 The figure represents a network of interactions among cytokines, demonstrating significant associations between circulating cytokine levels and the erythrocyte volume fraction. The nodes (cytokines) are connected by edges of varying thickness, indicating interaction strength.
Number, weight (kg), and height (cm).
| Valid N | Geometric Mean | Minimum | Maximum | Std. Dev. | CV | |
|---|---|---|---|---|---|---|
| Weight | 165 | 73 | 48 | 111 | 13 | 17 |
| Height | 165 | 171 | 153 | 195 | 8.6 | 5 |
| BMI | 165 | 25 | 15 | 41 | 4.4 | 17 |
| hsCRP | 165 | 0.8 | 0.2 | 10 | 2.0 | 250 |
| Hb | 165 | 137 | 94 | 167 | 12.1 | 9 |
| RBC | 165 | 5 | 4 | 6 | 0.41 | 9 |
| EVF | 165 | 0.41 | 0.14 | 0.48 | 0.04 | 10 |
| Platelets | 165 | 238 | 100 | 430 | 52 | 21 |
| WBC | 165 | 5.7 | 3.4 | 13.1 | 1.8 | 31 |
| Neutrophils | 165 | 3.1 | 0.5 | 9.5 | 1.4 | 42 |
Abbreviations: Body mass index (BMI; kg × m−2), high sensitivity C-reactive protein (hsCRP; mg × L−1), hemoglobin (Hb; g × L−1), erythrocyte count (RBC; 1012 × L−1), erythrocyte volume fraction (EVF; %), platelet count (platelets; 109 × L−1), white blood cell count (WBC; 109 × L−1), and neutrophil count (neutrophils; 109 × L−1). Standard deviation (Std. Dev.), CV (coefficients of variation).
Cytokines, chemokines, and growth factors and their relations to cell types and red blood cell parameters, determined by both Benjamini–Hochberg p-values and Spearman rank correlations.
| Blood Cell Marker | Cytokine | UniProt ID | N | Spearman Rank Correlations | Benjamini–Hochberg p-Value |
|---|---|---|---|---|---|
| EVF | TRAIL | P50591 | 165 | 0.349 | 0.001 |
| EVF | MCP-1 | P13500 | 165 | 0.321 | 0.002 |
| EVF | HGF | P14210 | 165 | 0.304 | 0.006 |
| EVF | TNFRSF9 | Q07011 | 165 | 0.259 | 0.022 |
| EVF | ADA | P00813 | 165 | 0.251 | 0.031 |
| EVF | DNER | Q8NFT8 | 165 | 0.236 | 0.046 |
| EVF | CCL19 | Q99731 | 165 | 0.23 | 0.051 |
| EVF | IL-10RB | Q08334 | 165 | 0.229 | 0.051 |
| EVF | IL18 | Q14116 | 165 | 0.228 | 0.051 |
| EVF | TWEAK | O43508 | 165 | 0.224 | 0.053 |
| EVF | CST5 | P28325 | 165 | 0.217 | 0.061 |
| EVF | PD-L1 | Q9NZQ7 | 165 | 0.213 | 0.067 |
| EVF | CCL3 | P10147 | 165 | 0.212 | 0.069 |
| EVF | uPA | P00749 | 165 | 0.211 | 0.069 |
| EVF | EN-RAGE | P80511 | 165 | 0.205 | 0.076 |
| EVF | TRANCE | O14788 | 165 | 0.205 | 0.076 |
| EVF | IL-18R1 | Q13478 | 165 | 0.202 | 0.081 |
| EVF | TGF-alpha | P01135 | 165 | 0.198 | 0.088 |
| EVF | IL8 | P10145 | 165 | 0.196 | 0.092 |
| Hb | TRAIL | P50591 | 165 | 0.337 | 0.001 |
| Hb | MCP-1 | P13500 | 165 | 0.291 | 0.011 |
| Hb | HGF | P14210 | 165 | 0.276 | 0.017 |
| Hb | ADA | P00813 | 165 | 0.233 | 0.049 |
| Hb | DNER | Q8NFT8 | 165 | 0.232 | 0.049 |
| Hb | TNFRSF9 | Q07011 | 165 | 0.229 | 0.051 |
| Hb | IL18 | Q14116 | 165 | 0.205 | 0.076 |
| Hb | uPA | P00749 | 165 | 0.200 | 0.084 |
| Hb | EN-RAGE | P80511 | 165 | 0.199 | 0.087 |
| Hb | TWEAK | O43508 | 165 | 0.196 | 0.092 |
| Hb | TRANCE | O14788 | 165 | 0.193 | 0.099 |
| MCHC | IL-12B | P29460 | 165 | −0.214 | 0.066 |
| MCV | IL-18R1 | Q13478 | 165 | −0.229 | 0.051 |
| Neutroph | OSM | P13725 | 165 | 0.505 | 0 |
| Neutroph | IL6 | P05231 | 165 | 0.327 | 0.002 |
| Neutroph | TNFSF14 | O43557 | 165 | 0.241 | 0.040 |
| Neutroph | HGF | P14210 | 165 | 0.232 | 0.049 |
| Neutroph | EN-RAGE | P80511 | 165 | 0.214 | 0.067 |
| Neutroph | SCF | P21583 | 165 | −0.204 | 0.079 |
| Plt | FGF-23 | Q9GZV9 | 165 | 0.282 | 0.016 |
| Plt | IL-10RB | Q08334 | 165 | 0.269 | 0.017 |
| Plt | LAP TGF-beta-1 | P01137 | 165 | 0.269 | 0.017 |
| Plt | CCL19 | Q99731 | 165 | 0.263 | 0.021 |
| Plt | SIRT2 | Q8IXJ6 | 165 | 0.261 | 0.022 |
| Plt | IL6 | P05231 | 165 | 0.26 | 0.022 |
| Plt | VEGFA | P15692 | 165 | 0.257 | 0.024 |
| Plt | AXIN1 | O15169 | 165 | 0.244 | 0.037 |
| Plt | CASP-8 | Q14790 | 165 | 0.239 | 0.042 |
| Plt | CD40 | P25942 | 165 | 0.231 | 0.050 |
| Plt | PD-L1 | Q9NZQ7 | 165 | 0.227 | 0.051 |
| Plt | CXCL5 | P42830 | 165 | 0.227 | 0.051 |
| Plt | MCP-3 | P80098 | 165 | 0.225 | 0.052 |
| Plt | STAMBP | O95630 | 165 | 0.225 | 0.052 |
| Plt | CCL3 | P10147 | 165 | 0.223 | 0.054 |
| Plt | NRTN | Q99748 | 165 | 0.223 | 0.054 |
| Plt | HGF | P14210 | 165 | 0.218 | 0.059 |
| Plt | CD5 | P06127 | 165 | 0.216 | 0.063 |
| Plt | TRAIL | P50591 | 165 | 0.214 | 0.067 |
| Plt | CCL4 | P13236 | 165 | 0.210 | 0.070 |
| Plt | ADA | P00813 | 165 | 0.210 | 0.071 |
| Plt | TNFSF14 | O43557 | 165 | 0.208 | 0.072 |
| Plt | MCP-4 | Q99616 | 165 | 0.193 | 0.099 |
| RBC | TRAIL | P50591 | 165 | 0.277 | 0.017 |
| RBC | IL-18R1 | Q13478 | 165 | 0.270 | 0.017 |
| RBC | HGF | P14210 | 165 | 0.270 | 0.017 |
| RBC | MCP-1 | P13500 | 165 | 0.240 | 0.042 |
| RBC | TRANCE | O14788 | 165 | 0.227 | 0.051 |
| RBC | TNFRSF9 | Q07011 | 165 | 0.224 | 0.053 |
| RBC | ADA | P00813 | 165 | 0.211 | 0.069 |
| RBC | EN-RAGE | P80511 | 165 | 0.209 | 0.071 |
| RBC | CASP-8 | Q14790 | 165 | 0.199 | 0.086 |
| WBC | OSM | P13725 | 165 | 0.463 | 0 |
| WBC | IL6 | P05231 | 165 | 0.325 | 0.002 |
| WBC | HGF | P14210 | 165 | 0.277 | 0.017 |
| WBC | TNFSF14 | O43557 | 165 | 0.265 | 0.020 |
| WBC | EN-RAGE | P80511 | 165 | 0.222 | 0.054 |
| WBC | TGF-alpha | P01135 | 165 | 0.212 | 0.069 |
| WBC | CCL19 | Q99731 | 165 | 0.203 | 0.079 |
| WBC | CCL11 | P51671 | 165 | −0.197 | 0.090 |
Abbreviations: erythrocyte count (RBC; 1012 × L−1), erythrocyte volume fraction (EVF; %), hemoglobin (Hb; g × L−1), mean corpuscular hemoglobin (MCHC; g × L−1), mean corpuscular volume (MCV; fl, 10−15), erythrocyte volume fraction white blood cell count (WBC; 109 × L−1), neutrophil count (Neutroph; 109 × L−1), and platelet count (Plt; 109 × L−1).
Supplementary Materials
The supporting information can be downloaded at:
1. Hegde, R.; Awan, K.H. Effects of periodontal disease on systemic health. Dis. Mon.; 2019; 65, pp. 185-192. [DOI: https://dx.doi.org/10.1016/j.disamonth.2018.09.011]
2. Plachokova, A.S.; Andreu-Sánchez, S.; Noz, M.P.; Fu, J.; Riksen, N.P. Oral Microbiome in Relation to Periodontitis Severity and Systemic Inflammation. Int. J. Mol. Sci.; 2021; 22, 5876. [DOI: https://dx.doi.org/10.3390/ijms22115876]
3. Bayani, M.; Pourali, M.; Keivan, M. Possible interaction between visfatin, periodontal infection, and other systemic diseases: A brief review of literature. Eur. J. Dent.; 2017; 11, pp. 407-410. [DOI: https://dx.doi.org/10.4103/ejd.ejd_284_16] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28932156]
4. Liu, C.; Chu, D.; Kalantar-Zadeh, K.; George, J.; Young, H.A.; Liu, G. Cytokines: From Clinical Significance to Quantification. Adv. Sci.; 2021; 8, e2004433. [DOI: https://dx.doi.org/10.1002/advs.202004433] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34114369]
5. Fajgenbaum, D.C.; June, C.H. Cytokine Storm. N. Engl. J. Med.; 2020; 383, pp. 2255-2273. [DOI: https://dx.doi.org/10.1056/NEJMra2026131] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33264547]
6. Jaffer, U.; Wade, R.G.; Gourlay, T. Cytokines in the systemic inflammatory response syndrome: A review. HSR Proc. Intensive Care Cardiovasc. Anesth.; 2010; 2, pp. 161-175.
7. Brinkmann, V.; Reichard, U.; Goosmann, C.; Fauler, B.; Uhlemann, Y.; Weiss, D.S.; Weinrauch, Y.; Zychlinsky, A. Neutrophil extracellular traps kill bacteria. Science; 2004; 303, pp. 1532-1535. [DOI: https://dx.doi.org/10.1126/science.1092385]
8. Branzk, N.; Lubojemska, A.; Hardison, S.E.; Wang, Q.; Gutierrez, M.G.; Brown, G.D.; Papayannopoulos, V. Neutrophils sense microbe size and selectively release neutrophil extracellular traps in response to large pathogens. Nat. Immunol.; 2014; 15, pp. 1017-1025. [DOI: https://dx.doi.org/10.1038/ni.2987]
9. Meier, A.; Sakoulas, G.; Nizet, V.; Ulloa, E.R. Neutrophil Extracellular Traps: An Emerging Therapeutic Target to Improve Infectious Disease Outcomes. J. Infect. Dis.; 2024; 230, pp. 514-521. [DOI: https://dx.doi.org/10.1093/infdis/jiae252]
10. Jorch, S.K.; Kubes, P. An emerging role for neutrophil extracellular traps in noninfectious disease. Nat. Med.; 2017; 23, pp. 279-287. [DOI: https://dx.doi.org/10.1038/nm.4294]
11. Wang, H.; Kim, S.J.; Lei, Y.; Wang, S.; Wang, H.; Huang, H.; Zhang, H.; Tsung, A. Neutrophil extracellular traps in homeostasis and disease. Signal Transduct. Target. Ther.; 2024; 9, 235.
12. Roy, S.; Rizvi, Z.A.; Awasthi, A. Metabolic Checkpoints in Differentiation of Helper T Cells in Tissue Inflammation. Front. Immunol.; 2018; 9, 3036. [DOI: https://dx.doi.org/10.3389/fimmu.2018.03036]
13. Boyette, L.B.; Macedo, C.; Hadi, K.; Elinoff, B.D.; Walters, J.T.; Ramaswami, B.; Chalasani, G.; Taboas, J.M.; Lakkis, F.G.; Metes, D.M. Phenotype, function, and differentiation potential of human monocyte subsets. PLoS ONE; 2017; 12, e0176460. [DOI: https://dx.doi.org/10.1371/journal.pone.0176460] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28445506]
14. Austermann, J.; Roth, J.; Barczyk-Kahlert, K. The Good and the Bad: Monocytes’ and Macrophages’ Diverse Functions in Inflammation. Cells; 2022; 11, 1979. [DOI: https://dx.doi.org/10.3390/cells11121979] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35741108]
15. Arango Duque, G.; Descoteaux, A. Macrophage cytokines: Involvement in immunity and infectious diseases. Front. Immunol.; 2014; 5, 491. [DOI: https://dx.doi.org/10.3389/fimmu.2014.00491]
16. Antonelli, A.; Scarpa, E.S.; Magnani, M. Human Red Blood Cells Modulate Cytokine Expression in Monocytes/Macrophages Under Anoxic Conditions. Front. Physiol.; 2021; 12, 632682. [DOI: https://dx.doi.org/10.3389/fphys.2021.632682] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33679443]
17. Karsten, E.; Herbert, B.R. The emerging role of red blood cells in cytokine signalling and modulating immune cells. Blood Rev.; 2020; 41, 100644. [DOI: https://dx.doi.org/10.1016/j.blre.2019.100644] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31812320]
18. Bakogiannis, C.; Sachse, M.; Stamatelopoulos, K.; Stellos, K. Platelet-derived chemokines in inflammation and atherosclerosis. Cytokine; 2019; 122, 154157. [DOI: https://dx.doi.org/10.1016/j.cyto.2017.09.013]
19. Maouia, A.; Rebetz, J.; Kapur, R.; Semple, J.W. The Immune Nature of Platelets Revisited. Transfus. Med. Rev.; 2020; 34, pp. 209-220. [DOI: https://dx.doi.org/10.1016/j.tmrv.2020.09.005]
20. Habets, K.L.; Huizinga, T.W.; Toes, R.E. Platelets and autoimmunity. Eur. J. Clin. Investig.; 2013; 43, pp. 746-757. [DOI: https://dx.doi.org/10.1111/eci.12101]
21. Anastasiadi, A.T.; Arvaniti, V.Z.; Hudson, K.E.; Kriebardis, A.G.; Stathopoulos, C.; D’Alessandro, A.; Spitalnik, S.L.; Tzounakas, V.L. Exploring unconventional attributes of red blood cells and their potential applications in biomedicine. Protein Cell; 2024; 15, pp. 315-330. [DOI: https://dx.doi.org/10.1093/procel/pwae001] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/38270470]
22. Kumar, S.D.; Kar, D.; Akhtar, M.N.; Willard, B.; Roy, D.; Hussain, T.; Rajyaguru, P.I.; Eswarappa, S.M. Evidence for low-level translation in human erythrocytes. Mol. Biol. Cell; 2022; 33, br21. [DOI: https://dx.doi.org/10.1091/mbc.E21-09-0437]
23. Kaczmarski, R.S.; Mufti, G.J. The cytokine receptor superfamily. Blood Rev.; 1991; 5, pp. 193-203. [DOI: https://dx.doi.org/10.1016/0268-960X(91)90036-C]
24. Kelso, A. Cytokines and their receptors: An overview. Ther. Drug Monit.; 2000; 22, pp. 40-43. [DOI: https://dx.doi.org/10.1097/00007691-200002000-00008] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/10688256]
25. Dinarello, C.A. Historical insights into cytokines. Eur. J. Immunol.; 2007; 37, pp. S34-S45. [DOI: https://dx.doi.org/10.1002/eji.200737772] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/17972343]
26. Rathnayake, N.; Akerman, S.; Klinge, B.; Lundegren, N.; Jansson, H.; Tryselius, Y.; Sorsa, T.; Gustafsson, A. Salivary biomarkers for detection of systemic diseases. PLoS ONE; 2013; 8, e61356. [DOI: https://dx.doi.org/10.1371/journal.pone.0061356]
27. Balmasova, I.P.; Lomakin, Y.A.; Babaev, E.A.; Tsarev, V.N.; Gabibov, A.G.; Smirnov, I.V.; Knorre, V.D.; Ovchinnikova, L.A.; Gnuchev, N.V.; Khurs, E.N.
28. Galhardo, L.F.; Ruivo, G.F.; de Oliveira, L.D.; Parize, G.; Santos, S.; Pallos, D.; Leão, M.V.P. Inflammatory markers in saliva for diagnosis of sepsis of hospitalizes patients. Eur. J. Clin. Investig.; 2020; 50, e13219. [DOI: https://dx.doi.org/10.1111/eci.13219]
29. Chen, I.L.; Huang, H.C.; Ou-Yang, M.C.; Chen, F.S.; Chung, M.Y.; Chen, C.C. A novel method to detect bacterial infection in premature infants: Using a combination of inflammatory markers in blood and saliva. J. Microbiol. Immunol. Infect.; 2020; 53, pp. 892-899. [DOI: https://dx.doi.org/10.1016/j.jmii.2019.11.002]
30. Wang, Z.; Mandel, H.; Levingston, C.A.; Young, M.R.I. An exploratory approach demonstrating immune skewing and a loss of coordination among cytokines in plasma and saliva of Veterans with combat-related PTSD. Hum. Immunol.; 2016; 77, pp. 652-657. [DOI: https://dx.doi.org/10.1016/j.humimm.2016.05.018]
31. von Majewski, K.; Kraus, O.; Rhein, C.; Lieb, M.; Erim, Y.; Rohleder, N. Acute stress responses of autonomous nervous system, HPA axis, and inflammatory system in posttraumatic stress disorder. Transl. Psychiatry; 2023; 13, 36. [DOI: https://dx.doi.org/10.1038/s41398-023-02331-7] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36732491]
32. Assarsson, E.; Lundberg, M.; Holmquist, G.; Björkesten, J.; Thorsen, S.B.; Ekman, D.; Eriksson, A.; Rennel Dickens, E.; Ohlsson, S.; Edfeldt, G.
33. Rose, T.M.; Bruce, A.G. Oncostatin M is a member of a cytokine family that includes leukemia-inhibitory factor, granulocyte colony-stimulating factor, and interleukin 6. Proc. Natl. Acad. Sci. USA; 1991; 88, pp. 8641-8645. [DOI: https://dx.doi.org/10.1073/pnas.88.19.8641]
34. MacFarlane, M.; Ahmad, M.; Srinivasula, S.M.; Fernandes-Alnemri, T.; Cohen, G.M.; Alnemri, E.S. Identification and molecular cloning of two novel receptors for the cytotoxic ligand TRAIL. J. Biol. Chem.; 1997; 272, pp. 25417-25420. [DOI: https://dx.doi.org/10.1074/jbc.272.41.25417]
35. Takeda, Y.; Fujita, S.; Ikemoto, T.; Okada, Y.; Sohmiya, K.; Hoshiga, M.; Ishizaka, N. The relationship of fibroblast growth factors 21 and 23 and α-Klotho with platelet activity measured by platelet volume indices. Clin. Chem. Lab. Med.; 2015; 53, pp. 1569-1574. [DOI: https://dx.doi.org/10.1515/cclm-2014-1251] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25781694]
36. Sosman, J.A.; Verma, A.; Moss, S.; Sorokin, P.; Blend, M.; Bradlow, B.; Chachlani, N.; Cutler, D.; Sabo, R.; Nelson, M.
37. Blakytny, R.; Ludlow, A.; Martin, G.E.; Ireland, G.; Lund, L.R.; Ferguson, M.W.; Brunner, G. Latent TGF-beta1 activation by platelets. J. Cell Physiol.; 2004; 199, pp. 67-76. [DOI: https://dx.doi.org/10.1002/jcp.10454]
38. Moscardó, A.; Vallés, J.; Latorre, A.; Jover, R.; Santos, M.T. The histone deacetylase sirtuin 2 is a new player in the regulation of platelet function. J. Thromb. Haemost.; 2015; 13, pp. 1335-1344. [DOI: https://dx.doi.org/10.1111/jth.13004]
39. Senchenkova, E.Y.; Komoto, S.; Russell, J.; Almeida-Paula, L.D.; Yan, L.S.; Zhang, S.; Granger, D.N. Interleukin-6 mediates the platelet abnormalities and thrombogenesis associated with experimental colitis. Am. J. Pathol.; 2013; 183, pp. 173-181. [DOI: https://dx.doi.org/10.1016/j.ajpath.2013.03.014]
40. Sonmez, O.; Sonmez, M. Role of platelets in immune system and inflammation. Porto Biomed. J.; 2017; 2, pp. 311-314. [DOI: https://dx.doi.org/10.1016/j.pbj.2017.05.005]
41. Leone, G.M.; Mangano, K.; Petralia, M.C.; Nicoletti, F.; Fagone, P. Past, Present and (Foreseeable) Future of Biological Anti-TNF Alpha Therapy. J. Clin. Med.; 2023; 12, 1630. [DOI: https://dx.doi.org/10.3390/jcm12041630] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36836166]
42. Kishimoto, T. Discovery of IL-6 and Development of Anti-IL-6R Antibody. Keio J. Med.; 2019; 68, 96. [DOI: https://dx.doi.org/10.2302/kjm.68-007-ABST]
43. Dale, D.C.; Crawford, J.; Klippel, Z.; Reiner, M.; Osslund, T.; Fan, E.; Morrow, P.K.; Allcott, K.; Lyman, G.H. A systematic literature review of the efficacy, effectiveness, and safety of filgrastim. Support. Care Cancer; 2018; 26, pp. 7-20. [DOI: https://dx.doi.org/10.1007/s00520-017-3854-x] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28939926]
44. Bunn, H.F. Erythropoietin. Cold Spring Harb. Perspect. Med.; 2013; 3, a011619. [DOI: https://dx.doi.org/10.1101/cshperspect.a011619] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23457296]
45. Eriksson, L.B.; Gordh, T.; Karlsten, R.; LoMartire, R.; Thor, A.; Tegelberg, Å. Intravenous S-ketamine’s analgesic efficacy in third molar surgery. A randomized placebo-controlled double-blind clinical trial. Br. J. Pain.; 2024; 18, pp. 197-208. [DOI: https://dx.doi.org/10.1177/20494637231222327]
46. WMA Declaration of Helsinki—Ethical Principles for Medical Research Involving Human Subjects. Available online: https://www.wma.net/policies-post/wma-declaration-of-helsinki-ethical-principles-for-medical-research-involving-human-subjects/ (accessed on 23 March 2022).
47. Benjamini, Y.; Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J. R. Stat. Soc. Ser. B (Methodol.); 2018; 57, pp. 289-300. [DOI: https://dx.doi.org/10.1111/j.2517-6161.1995.tb02031.x]
48. Ridker, P.M.; Everett, B.M.; Thuren, T.; MacFadyen, J.G.; Chang, W.H.; Ballantyne, C.; Fonseca, F.; Nicolau, J.; Koenig, W.; Anker, S.D.
49. Tanaka, T.; Narazaki, M.; Kishimoto, T. IL-6 in inflammation, immunity, and disease. Cold Spring Harb. Perspect. Biol.; 2014; 6, a016295. [DOI: https://dx.doi.org/10.1101/cshperspect.a016295]
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
; Eriksson, Mats B 2
; Gordh Torsten 1 ; Larsson, Anders O 3
1 Department of Surgical Sciences, Uppsala University, Uppsala University Hospital, SE-751 85 Uppsala, Sweden; [email protected] (L.B.E.); [email protected] (T.G.)
2 Department of Surgical Sciences, Uppsala University, Uppsala University Hospital, SE-751 85 Uppsala, Sweden; [email protected] (L.B.E.); [email protected] (T.G.), NOVA Medical School, New University of Lisbon, 1099-085 Lisbon, Portugal
3 Department of Medical Sciences, Uppsala University, Uppsala University Hospital, SE-751 85 Uppsala, Sweden; [email protected]