Obesity is associated with a distinct innate immune phenotype in severe COVID-19 patients

doi:10.21203/rs.3.rs-1967394/v1

Download PDF

Article

Obesity is associated with a distinct innate immune phenotype in severe COVID-19 patients

https://doi.org/10.21203/rs.3.rs-1967394/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background/Objectives: Obesity is a chronic inflammatory disease that may impair immunity against SARS-Cov-2 in severe disease states. This study investigated whether the innate immune compartment is associated with obesity in severe COVID-19.

Methods: This cross-sectional study enrolled twenty-seven ICU COVID-19 patients that were assigned to obese (BMI≥30 kg/m², n=9) and non-obese (BMI<30kg/m², n=18) groups. Thirteen non-obese healthy adults composed the control group. Monocytes, NK, and both Low-Density (LD) and High-Density (HD) neutrophils were isolated from peripheral blood samples and analyzed by flow cytometry to evaluate the frequency and expression patterns of surface receptors associated with innate immune effector responses. sTREM-1 and IL-6 levels from patients’ sera were quantified by ELISA.

Results: The frequency of monocytes was negatively correlated with BMI, while NK and neutrophils were positively associated (p<0.05). A reduction of peripheral monocytes was observed in obese patients, and these cells showed a higher expression of PD-L1 than non-obese (p=0.04). HLA-DR expression on CD14+ monocytes was negatively associated with BMI (p=0.03) and total leukocytes (p=0.005). Patients with obesity also demonstrated a higher frequency of NK cells (p=0.01) and a hyperactivated phenotype (CD16+CD182+TREM-1+) in HD neutrophils (p=0.01). This phenotype was positively correlated with total leukocytes (p=0.01) and negatively correlated with the total numbers of HLA-DR+ monocytes in obese group (p=0.001). An increased expression of CD16 (p=0.01) and TREM-1+ (p=0.005) on mature HD neutrophils was detected in obese patients. In this group, CD16 was negatively correlated with IL-6 levels (p=0.03) and TREM-1+ positively correlated with PD-L1 on neutrophils (p=0.03). Furthermore, sTREM-1 levels correlated with PD-L1 of monocytes and LD neutrophils (p=0.02). Finally, aging may exacerbate cytotoxic and suppressive activity.

Conclusion: Our data support that obesity differentially affected innate immune response in severe COVID-19 and highlight immune markers that should be further investigated as predictors for monitoring treatment in severe patients with comorbidities.

Coronavirus disease-2019

severe acute respiratory coronavirus 2

obesity

body mass index

Obesity is a global health issue directly associated with higher susceptibility to viral infections¹. The coronavirus (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2), resulted in increased patient admittance into intensive care units (ICU) requiring respiratory support and increased time of hospitalization. Collectively, this resulted in higher mortality rates among obese individuals compared to healthy-weight patients^2–4.

The low-grade inflammation, a consequence of adipose tissue expansion, dysregulates the cross-talk between innate and adaptive immune cells^5,6. Additionally, it has been suggested that adipose tissue may be a reservoir for viruses shedding, perpetuating inflammatory responses, which favors exhaustion of immune cells^1,6. Suppression of T cell function to viral infections and alterations in leukocyte development^7,8, as well as a reduced immune response to vaccination against influenza and SARS-Cov-2, has been demonstrated in individuals with obesity^9–12. In autopsy lungs of COVID-19 patients, 17 genes related to suppression, impaired antigen-presentation, cytokine signaling, and lipid metabolism correlated with BMI, independently of the presence of hypertension and type 2 diabetes¹³. Dysregulation of innate immune activity can impair the ability to clear infections and/or complicate the pathology by worsening chronic inflammatory states without a resolution¹⁴. Despite this importance, it remains elusive whether peripheral innate immune responses are differentially affected by obesity in SARS-Cov-2 infection^15,16.

The present study performed phenotypic and functional analysis of peripheral monocytes, natural killer (NK) cells, and both Low-Density (LD) and High-Density (HD) neutrophils using blood samples from severe cases of COVID-19. Importantly, the COVID-19 cases included non-obese and obese groups to comparatively investigate the altered innate immune and inflammatory functions in severe COVID-19 patients with obesity. Our findings demonstrate a distinct innate immune response, marked by suppressive and inflammatory functions, which is strongly associated with obesity in severe COVID-19 states. The present study also highlights immune markers that can help monitor patients with comorbidities.

Ethical statement

This cross-sectional study is part of a head project, which was approved by the ethical committee of the Federal University of Sergipe (CAAE: 34240620.7.0000.5546, accession number: 4.168.255). All procedures were conducted by the principles of the Declaration of Helsinki and written informed consent was provided by patients or legal guardians.

Study design and participants

The study’s cohort was composed of severe patients, according to the World Health Organization COVID-19 technical guidance (https://apps.who.int/iris/handle/10665/330854), from two government Hospitals named Sergipe Emergency Hospital and Surgery Hospital (Sergipe, Brazil) between September 08, 2020, and December 10, 2020. Individuals with obesity have not been vaccinated so far. Consecutively, patients admitted to ICU with a diagnosis of COVID-19 by a positive result for SARS-Cov-2 nuclei acid on real-time polymerase chain reaction assays of nasopharyngeal and throat swabs and by a typical chest CT-scan findings were included. One peripheral blood sample was collected during the hospital routine between days 0 and 5 after ICU admission.

Demographic (age and sex), clinical [BMI, comorbidities, and usual vital signs as heart rate, body temperature, and arterial oxygen partial pressure/factional inspired oxygen (PaO₂/FiO₂)] and laboratory [white and red blood cells count, platelets, and blood biochemicals as alanine aminotransferase (ALT), aspartate aminotransferase (AST), urea and creatinine, sodium and potassium, total bilirubin and C-reactive protein (CRP)] data were retrieved from inpatient health records and are presented in Supplementary Table 1. N/L ratio was calculated from the absolute numbers of neutrophils and lymphocytes (leukocytes). BMI was documented by a nutritionist upon admission. Based on BMI classification by WHO¹⁷, patients were divided into an obese (≥30 kg/m²) and non-obese (<30 kg/m²) group. Healthy adult individuals with a negative rt-PCR were recruited as a reference to our COVID-19 study groups. Exclusion criteria were as follows: under 18 years old, BMI of underweight, taking immunosuppressants, having any kind of cancer or auto-immune disease, pregnant women, and subjects that presented issues during flow cytometry experiments.

Flow cytometry

Peripheral blood mononuclear cells (PBMC) and polymorphonuclear (PMN) cells were isolated from fresh EDTA blood samples by centrifugation (1500 rpm, 25ºC, 35 min) with Ficoll-Hystopaque (Ficoll-Paque PLUS™, GE Healthcare, USA). After wash steps, fluorescent-labeled antibodies were used to identify and determine frequencies of the circulating innate immune subsets and expression of surface receptors. 1x10⁶ of these cells were stained with the following monoclonal antibodies: CD14 PerCP 5.5 and CD274 PE (BD Biosciences) and CD80 FITC, CD163 PE-Cy7, and HLA-DR Alexa 700 (Invitrogen) for Monocytes; CD3 PE, CD11c PerCP 5.5, CD16 FITC and CD56 PE-Cy7 (BD Biosciences) for Natural Killer (NK) cells; CD14 PerCP 5.5 and CD274 PE PE (BD Biosciences), and HLA-DR Alexa 700 (Invitrogen) for Low-Density (LD) Neutrophils; and CD11b APC and CD16 FITC (BD Biosciences), CD182 PE-Cy 5.5 (Invitrogen), TREM-1 PE (BioLegend), CD11c PerCP 5.5, CD274 PE and CD279 FITC (BD Biosciences) and HLA-DR Alexa 700 (Invitrogen) for High-Density (HD) Neutrophils. Samples were acquired in Attune™ NxT Flow Cytometer software version 2.4 (Life Technologies, CA, USA). Data were analyzed using FlowJo software version V10 (Treestar Inc, Ashland, USA). Statistic values were used to report the relative frequency of each immune cell population and fluorescence intensity values are reported as MFI, which were determined by FlowJo according to intensity values of events gated from live cells. Gating strategies and additional details were shown in Supplementary Methodology.

Soluble immune measurements

Serum aliquots blood was isolated by centrifugation of tubes without anti-coagulants (3000 rpm, 10 minutes, 25ºC). Then, samples were frozen at -80ºc until analysis. Soluble TREM-1 (sTREM-1; R&D Systems, Minneapolis, USA) and IL-6 (Invitrogen, ThermoFisher, Waltham, USA) levels were measured in serum using human DuoSet enzyme-linked immunosorbent assay (ELISA) kits according to the manufacturer’s instructions. The absorbance was read at 450 nm using a microplate reader with a wavelength correction set at 570 nm (Epoch, BioTek, Luzern, Switzerland). A standard curve was generated for each set of samples assayed using the manufacturer’s instructions.

Statistical Analysis

Shapiro-Wilk test was used to evaluate normality distribution and, based on that premise, data are presented as mean and standard deviation or median and 25-75^th interquartile range. An unsupervised principal component analysis (PCA) was performed to investigate whether immune variables clustered severe patients with obesity. It was identified which components (linear combinations of variables) better-explained variance and a Principal Component Regression (PCR) by least squares was applied to model the relationship between BMI and immune variables. Then, comparisons among obese patients, non-obese patients, and healthy controls were performed using the Kruskal-Wallis test followed by the Benjamin, Krieger, and Yekutieli False Discovery Rate test for multiple testing correction. Significant differences were visualized in the volcano plot and heatmap. Spearman’s rank correlation coefficient was computed between non-Gaussian immune variables, that previously showed a significant difference between groups, with all other immune variables, adjusted by BMI, and visualized by a heatmap with clustering to investigate their relationship within groups. It was also performed a Spearman’s rank correlation between those significant immune variables with clinical variables (only variables that we get from all patients, such as white blood cells, red blood cells, platelets, serum urea, and creatinine, sTREM-1, and IL-6), and adjusted by BMI. Finally, we applied Spearman’s rank correlation adjusted by age. P values less than 0.05 were considered significant. Statistical analyses and figures were conducted in GraphPad Prism software version 9.4 (San Diego, USA) and R Studio version 4.1.2.

Participants’ characteristics

Twenty-seven patients enrolled in this study have arrived at the hospital with signs of acute respiratory distress syndrome (ARDS), dyspnea, fever, and dry cough. Each of the patients was admitted into ICU requiring invasive oxygen support and diagnosed with COVID-19-associated pneumonia. At the time of blood collection, all patients were taking corticoids, antibiotics, antifebrile, and drugs for gastric protection as routine. As shown in Figure 1A, one patient with obesity had a BMI of obesity class III (>40 kg/m²). In the non-obese group, five patients were overweight (25-29.9 kg/m²; Figure 1A). Mean BMI and mean age between groups were significantly different (p<0.01; Table 1). Five patients with obesity (55.5%) and four non-obese (22.2%) were over 65 years of age. One patient in the obese group and one patient in the non-obese group were habitual smokers. Moreover, 13 healthy non-obese individuals (HC) were comprised of our control group (46.15% of women; 33.85±7.16 years old; BMI: 24.73±3.24 kg/m²). They presented significant lower mean levels of sTREM-1 (6.09±16.25 pg/ml) and IL-6 (0.09±0.34 pg/ml) compared to COVID-19 patients (p<0.05). In general, clinical outcomes were similar between groups, except circulating monocytes that showed a significantly lower frequency among total leukocytes in patients with obesity (p<0.05; Table 1), and this guided us to the following analyses.

Innate immune markers differentiated severe COVID-19 patients with obesity from non-obese

In our study population of COVID-19 patients, the frequency of peripheral monocytes, NK, and both LD and HD neutrophils were significantly correlated with BMI (Figure 1B). Then, a PCA plot of all flow cytometric parameters showed that innate immunity clustered in the majority of obese patients (Figure 1C). PCR analysis identified a significant association between BMI and MFI of CD274+ on CD14+ monocytes, CD11b+ LD neutrophils, HLA-DR+CD11b+ LD neutrophils, total NK cells, CD56+ NK cells, CD16+CD11b+ HD neutrophils, TREM-1+CD11b+ HD neutrophils and MFI of CD279+ on CD11c+ HD neutrophils (R²=0.49, p=0.04). Based on the effect size, the volcano plot confirmed that the MFI of TREM-1 on CD11b+ HD neutrophils, total NK cells, the frequency of CD16+CD182+TREM-1+ HD neutrophils, the MFI of CD274 on CD14+ monocytes and the expression of CD16 on CD11b+ HD neutrophils were the major statistical discriminators between COVID-19 patients with obesity and non-obese (Figure 1D).

Comparatively, figure 2A shows a higher frequency of circulating total NK cells and an inflammatory phenotype of HD neutrophils in patients with obesity than in non-obese and HC. Kruskal-Wallis confirmed significant differences only between COVID-19 groups, and non-obese patients were not statistically different from healthy controls in neither % NK cells (p=0.57) or CD16+CD182+TREM-1+ of HD neutrophils (p=0.76). Moreover, patients with obesity showed a higher frequency of both LD (OB: 8.96 [5.58-30.25] vs. N-OB: 5.72 [2.14-15.38] vs. HC: 0.34 [0.28-0.60], p=0.07) and HD neutrophils (OB: 93.50 [91.00-95.10] vs. N-OB: 81.60 [66.15-92.50] vs. HC: 83.05 [74.35-87.45], p=0.02) than non-obese and HC. Additionally, figure 2B shows an immunophenotype significantly enriched by markers of suppression (CD274), phagocytosis (CD16), and inflammatory response (TREM-1) in patients with obesity. To gain sensitivity to a possible obesity-underlying immunophenotype, we investigated whether these significant outcomes were associated with other immune markers and clinical variables in patients with obesity.

The associations among immune markers within the obese group were differently clustered and stronger than in the non-obese group, suggesting a distinct innate immune response, as shown in Figure 3. For instance, only in patients with obesity, it was observed that both the frequency (rho=-0.95, p=0.001) and the expression (rho=-0.78, p=0.02) of HLA-DR on CD14+ monocytes were negatively correlated with the CD16+CD182+TREM-1+ phenotype of HD neutrophils (Figure 3A). Congruently, the expression of HLA-DR on CD14+ monocytes was negatively correlated with BMI (rho=-0.44, p=0.03; Supplementary Table 2). Moreover, the MFI of TREM-1 showed a negative association with the frequency of peripheral monocytes (rho=-0.90, p=0.005; Figure 3A) and with total numbers of PD-L1+PD-1+ HD neutrophils (rho=-0.76, p=0.04). Additionally, the MFI of TREM-1 was positively associated with the MFI of CD274 on both LD (rho=0.71, p=0.05) and HD neutrophils (rho=0.76, p=0.03) in obese patients (Figure 3A).

Interestingly, only patients with obesity showed a substantial number of associations between immune markers and clinical variables, as shown in Figure 4. For example, total leukocytes were negatively correlated with both the frequency (rho=-0.88, p=0.007) and expression (rho=-0.90, p=0.005) of HLA-DR+ on CD14+ monocytes, positively correlated with the inflammatory phenotype of HD neutrophils (CD16+CD182+TREM-1+; rho=0.83, p=0.01) and positively correlated with antibody-dependent cytotoxic NK cells (CD16+CD56+, rho=0.71, p=0.05; Figure 4A). As the frequency of total NK cells increases in patients with obesity, Neutrophil-to-Lymphocyte (N/L) ratio also increases, as observed by a strong positive correlation between them (rho=0.76, p<0.05; Figure 4A). Despite both obese and non-obese patients being compromised, the increased N/L ratio in patients with obesity suggests a poorer prognosis (Table 1). Additionally, patients with obesity showed a negative correlation between CD56+ NK cells and IL-6 levels (rho=-0.83, p=0.05; Figure 4A), while no significant association was found between NK cells and clinical parameters in the non-obese group (Figure 4B). IL-6 was lower in patients with obesity compared to non-obese individuals (p=0.06; Table 1) and showed a negative correlation with CD16 expression in HD neutrophils (rho=-0.88, p=0.03, Figure 3A). While no difference in sTREM-1 levels was observed between COVID-19 groups (Table 1), unique correlations occurred between sTREM-1 and frequency of PD-L1 on monocytes (rho=-0.94, p=0.02) and LD neutrophils in obese patients (rho=-0.94, p=0.02; Figure 4A). Considering that all COVID-19 patients are in corticosteroid treatment, it seems that inflammatory signaling may be differently affected in ICU patients with obesity.

Furthermore, red blood cell parameters and immune markers also showed a different relationship among patients with obesity (Figure 4). Monocytes from patients with obesity expressing haptoglobin-hemoglobin scavenger receptor (CD163+) showed a positive association with hematocrit blood levels (rho=0.78, p=0.03). Besides, several LD and HD neutrophils-related markers, including PD-L1+HLA-DR+ phenotype in LD neutrophils and PD-L1+PD-1+ in HD neutrophils, were positively correlated with red blood cell parameters (i.e., hemoglobin and erythrocytes) (p<0.05; Figure 4A).

Obesity-associated changes in innate immunity may be exacerbated by aging

Age was positively associated with BMI (Supplementary Table 2) and ICU patients with obesity were significantly older than non-obese individuals (Table 1). Thus, we performed Spearman’s correlation matrix with groups adjusted by age. BMI of older individuals (greater than 65 years of age) was positively correlated with the frequency of Total NK and the expression of CD16 on NK cells, with the levels of non-classical monocytes (CD163+HLA-DR+) and negatively correlated with a suppressor phenotype in HD neutrophils (p<0.05; Figure 5). Then, within the obese group, young (< 65 years old) and older (> 65 years old) patients were compared and no significant differences were found in any immunological or clinical variable included in this investigation (p>0.05; data not shown). In the young group, BMI was positively correlated with CD16 expression on HD neutrophils and total frequency of HD neutrophils and negatively correlated with antibody-dependent cytotoxic NK cells (CD16+CD56-) and with the frequency of CD14+ monocytes. Thus, these data reinforce the influence of obesity on innate immune response, although aging may exacerbate cytotoxic and suppressive activity.

Obesity is a chronic inflammatory disease that results in dysregulation of immunometabolism, especially of alveolar and adipose-resident macrophages¹⁸. In agreement, Santos e Silva et al.¹³ found that obesity was strongly associated with an innate immune gene signature from autopsy lung samples from COVID-19 patients. Similar to our findings, this signature involved a higher expression of suppressive marker CD274 (PD-L1) and altered gene expression of markers of cytokine signaling and immune cell migration, such as CXCR2, FCGR3A, and FCG3B. Thus, it has been questioned whether this may occur in systemic innate immunity.

Alterations of peripheral innate immune function were documented in individuals with obesity without an infection, mainly, characterized by non-classical monocytes¹⁹, NK cells with impaired cytotoxic and imbalanced expression of activating and inhibiting receptors²⁰, and increased N/L ratio with a predominance of inflammatory activity by neutrophils compared to non-obese individuals²¹. Accordingly, this present study revealed a distinct peripheral innate immune profile in severe COVID-19 patients with obesity marked by lower circulating monocytes expressing high levels of suppressive PD-L1, higher frequency of peripheral NK cells, and HD neutrophils, the latter demonstrating an increased inflammatory activity. It was observed a substantial number of associations, particularly strong and unique, between immune and clinical markers in patients with obesity. These associations involved innate immune subsets expressing markers of suppression and loss of HLA-DR, especially in monocytes, cytotoxic activity by NK cells, and inflammation and chemotaxis in neutrophils, which also had subsets expressing PD-L1/PD-1. Therefore, even in the absence of a different clinical profile, our data supports the obesity-associated modulation of innate immunity in severe COVID-19 that may interfere with the treatment and recovery and, thus, should be further discussed.

In accordance with our study, Zulu et al.¹⁵ have observed an abnormal innate immune response among COVID-19 patients with obesity, mainly a negative correlation between BMI and frequency of peripheral monocytes. In these patients, it is plausible that monocytes are being recruited to inflamed tissues, especially due to obesity-associated chronic inflammation. Still in line with the authors, we also find a negative association between BMI and the expression of HLA-DR on circulating CD14+ monocytes. Previous studies that broadly investigated immunopathogenesis in SARS-Cov-2 infection demonstrated that monocytes suffer dynamic changes according to severity^22–24 and a decrease of circulating monocytes implicated in less effective and highly suppressive functions^22,23,25, such as an impaired response to viral stimulation²⁵. Besides, the upregulation of the PD-L1/PD-1 axis has been reported to be dysfunctional in chronic infections such as HIV and hepatitis B and C virus²⁶, as well as in severe cases of COVID-19²⁷.

Monocytes are crucial as an antigen-presenting cell, in the clearance of pathogens, as an inflammatory regulator, and are closely related to T-cell responses^22,27. PD-L1/PD-1 axis is also important for the balance in immune activation, preventing a hyperinflammatory response of cytotoxic cells. However, persistent stimulation by the virus reduces T-cell numbers and function and induces them to exhaustion²⁶. In our study, although not significantly different, there was a reduction in the frequency of peripheral lymphocytes in patients with obesity (Table 1), who also presented an important alteration in peripheral monocytes levels towards a suppressive activity and loss of HLA-DR expression. Moreover, these phenotypic changes in monocytes may be related to the inflammatory activity of HD neutrophils (CD16+CD182+TREM-1+). Whether these alterations in monocytes have regulatory or negative implications on the immune response of severe patients with obesity needs further investigation. Besides, studies demonstrated that deceased patients presented monocytes unable to dampen T-cell proliferation^28,29, while PD-1 and other checkpoint molecules were upregulated in adaptive immune cells from critical COVID-19 patients who died^27,30. These changes can negatively impact all immunity and response to therapies^7,8,11,31. In addition, the soluble form of PD-L1 has been recently discussed²⁷ and should be considered in further studies encompassing individuals with obesity.

Interestingly, a significantly higher peripheral frequency of total NK cells was observed in patients with obesity (Figure 2A) and it was also reported in severe obesity without infections³². In COVID-19, studies demonstrated the opposite with a significant reduction, or at least sustained, in the frequency of NK cells associated with COVID-19 severity^24,33,34. In Figure 2A, it is visible that the non-obese group had a similar median as the healthy group and when patients were re-grouped in young or aged, these NK cells positively associated with BMI in aged ones, who had a significantly higher BMI than young patients (p=0.03; Figure 5). Moreover, this increase of NK cells in our population with obesity was strongly and positively associated with the N/L ratio. An increase in this severity biomarker indicates a higher inflammatory response and it is implicated not only in infection diseases³⁵, but obesity-associated features may also affect the N/L ratio²¹. In COVID-19, NK cells function, measured by the expression of granzymes and perforins was influenced by higher levels of IL-6^33,34. Although we did not evaluate NK cell activity, it was observed a strong association between IL-6 levels and CD56 expression in the obese group (Figure 4B). Besides, a positive association between CD16+CD56+ NK cells and total leukocytes supports the relationship between inflammatory response and NK cell functionality and suggests a likely different immune response to corticosteroid treatment in the obese group.

Neutrophil activity may also coordinate inflammatory signaling in patients with obesity as observed by the association between CD16 (FcγRIII) of mature HD neutrophils and IL-6 levels and a hyperactivated phenotype (CD16+CD182+TREM-1+) highly frequent among total leukocytes. One previous study including patients with obesity has shown that HD neutrophils became hyperactivated with enhanced phagocytosis, respiratory oxidative burst, degranulation, and neutrophil extracellular (NET) formation in COVID-19 severe patients³⁵. TREM-1 is a triggering receptor expressed on myeloid cell type 1 able to amplify inflammatory responses and its soluble form (sTREM-1) is a biomarker of severity and mortality in COVID-19³⁶. Consistent with our previous results from patients with obesity shown in Figures 1 and 2, as the expression of TREM-1 increases in HD neutrophils, the frequency of monocytes decreases. TREM-1 expression also showed a positive relationship with LD and HD neutrophils expressing PD-L1. As PD-L1 expression increases in monocytes and LD neutrophils, sTREM-1 levels decrease. Moreover, the frequency of CD16+CD182+TREM-1+ HD neutrophils may also be affecting HLA-DR expression in monocytes of the obese group. Taken together, these outcomes suggest a strong association among peripheral innate cells and an alteration towards a sustained inflammatory response in patients with obesity, not seen in the non-obese group, which was already reported in previous studies during COVID-19 treatment ^13,25,28,35. Therefore, patients with obesity need careful monitoring during SARS-CoV-2 treatment.

Finally, red blood cell parameters were strongly associated with both LD and HD neutrophils expressing PD-L1 in patients with obesity. Consistently, there is a higher risk for endothelial damage and related complications in this population³⁷, and neutrophils are suggested to be involved in coagulation and phagocytosis of red blood cells in COVID-19^35,38. As we were not able to analyze endothelial functions, LD and HD neutrophils should be further explored in future studies, especially in populations with higher cardiovascular risk than individuals with obesity.

Some limitations should be addressed in future studies. First, we did not perform a longitudinal analysis on survival rates, which limits our understanding of whether immune cells from patients with obesity are responsive to treatment or dysfunctional. We only get information from some patients that point to more deaths among patients with obesity (Supplementary Table 1). Second, our small sample size is another limitation. However, as this field of immunity is little explored in patients with obesity, we support further studies considering the markers we studied here. Third, even though we did not find any differences when comparing aged and young patients, we recognize that the obese group had more elderly people in their proportion. Fourth, we did not get information on obesity measurements other than BMI due to the hospital routine at beginning of the pandemic. Our outcomes should be carefully interpreted as they reflect peripheral innate immune response involving two complex diseases, COVID-19 and obesity, under corticosteroid treatment and due to our observational design.

In summary, obesity influenced peripheral innate immune cells' distribution in severe COVID-19 patients, as observed in the frequency of monocytes, neutrophils, and NK cells. Furthermore, obesity seems to impact immune response, even in the absence of clear differences in severity-associated clinical parameters. A simultaneous suppressive and hyperactivated phenotype was observed in different innate immune populations only in patients with obesity, which may underlie the disease course, complications, and lower recovery rates in this population. Questions raised by the previous reviews^6,18 corroborates our outcomes, as we observed an obesity-associated innate immune response. We also highlighted immune markers that can help for monitoring, as flow cytometry has become useful in clinical practice, and for therapeutic decisions and vaccine development, as studies have shown that responses highly vary according to comorbidities^11,12,31,34.

Competing interests

The authors declare that they have no conflict of interest.

Declaration of competing interest

The authors declare that there is no competing interest. All authors have approved the manuscript for submission.

Author contributions

ASR was responsible forconceptualization, flow cytometry experiments, analyses and interpretation of results, and original draft preparation. MNFF and YLMO helped in blood collection, laboratory processing, methodology design, and obtaining demographic, clinical, and laboratory data. KFF was responsible for statistical analysis and graphs. MWL and CBC helped in flow cytometry protocol, its analysis, and interpretation. TRM was the supervisor of the study and helped in the conceptualization, methodology design, and critical review of the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding statement

Not applicable.

Ethics approval

This study was carried out in Sergipe (Brazil) by applicable rules concerning the local ethical committee. All patients or legal guardians were informed about the study details and could decline to participate.

Data availability statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Pugliese G, Liccardi A, Graziadio C, Barrea L, Muscogiuri G, Colao A. Obesity and infectious diseases: pathophysiology and epidemiology of a double pandemic condition. Int J Obes 2022; 46: 449–465.
Cai Z, Yang Y, Zhang J. Obesity is associated with severe disease and mortality in patients with coronavirus disease 2019 (COVID-19): a meta-analysis. BMC Public Health 2021; 21: 1505.
Malik VS, Ravindra K, Attri SV, Bhadada SK, Singh M. Higher body mass index is an important risk factor in COVID-19 patients: a systematic review and meta-analysis. Environ Sci Pollut Res 2020; 27: 42115–42123.
Simonnet A, Chetboun M, Poissy J, Raverdy V, Noulette J, Duhamel A et al. High Prevalence of Obesity in Severe Acute Respiratory Syndrome Coronavirus-2 (SARS‐CoV‐2) Requiring Invasive Mechanical Ventilation. Obesity 2020; 28: 1195–1199.
Chiappetta S, Sharma AM, Bottino V, Stier C. COVID-19 and the role of chronic inflammation in patients with obesity. Int J Obes 2020; 44: 1790–1792.
Khwatenge CN, Pate M, Miller LC, Sang Y. Immunometabolic Dysregulation at the Intersection of Obesity and COVID-19. Front Immunol 2021; 12. doi:10.3389/fimmu.2021.732913.
Green WD, Beck MA. Obesity Impairs the Adaptive Immune Response to Influenza Virus. Ann Am Thorac Soc 2017; 14: S406–S409.
Green WD, Beck MA. Obesity altered T cell metabolism and the response to infection. Curr Opin Immunol 2017; 46: 1–7.
Kara Z, Akçin R, Demir AN, Dinç HÖ, Taşkın HE, Kocazeybek B et al. Antibody Response to SARS-CoV-2 Vaccines in People with Severe Obesity. Obes Surg 2022. doi:10.1007/s11695-022-06181-y.
Discacciati MG, Siani S, Campa A, Nakaya HI. Why should obese youth be prioritized in COVID-19 vaccination programs? A nationwide retrospective study. Lancet Reg Heal - Am 2022; 7: 100167.
Piernas C, Patone M, Astbury NM, Gao M, Sheikh A, Khunti K et al. Associations of BMI with COVID-19 vaccine uptake, vaccine effectiveness, and risk of severe COVID-19 outcomes after vaccination in England: a population-based cohort study. Lancet Diabetes Endocrinol 2022. doi:10.1016/S2213-8587(22)00158-9.
Neidich SD, Green WD, Rebeles J, Karlsson EA, Schultz-Cherry S, Noah TL et al. Increased risk of influenza among vaccinated adults who are obese. Int J Obes 2017; 41: 1324–1330.
Santos e Silva JC, Vasconcelos AP, Noma IHY, Noronha NY, Aquino R, Giddaluru J et al. Gene signatures of autopsy lungs from obese patients with COVID-19. Clin Nutr ESPEN 2021; 44: 475–478.
Schultze JL, Aschenbrenner AC. COVID-19 and the human innate immune system. Cell 2021; 184: 1671–1692.
Zulu MZ, Sureshchandra S, Pinski AN, Doratt B, Shen W, Messaoudi I. Obesity Correlates With Pronounced Aberrant Innate Immune Responses in Hospitalized Aged COVID-19 Patients. Front Immunol 2021; 12: 1–11.
Kooistra EJ, de Nooijer AH, Claassen WJ, Grondman I, Janssen NAF, Netea MG et al. A higher BMI is not associated with a different immune response and disease course in critically ill COVID-19 patients. Int J Obes 2021; 45: 687–694.
Organization WH. Obesity: preventing and managing the global epidemic. Report of a WHO consultation. Tech Rep Ser 2000; 894: 1–253.
Gleeson LE, Roche HM, Sheedy FJ. Obesity, COVID-19 and innate immunometabolism. Br J Nutr 2021; 125: 628–632.
Devêvre EF, Renovato-Martins M, Clément K, Sautès-Fridman C, Cremer I, Poitou C. Profiling of the Three Circulating Monocyte Subpopulations in Human Obesity. J Immunol 2015; 194: 3917–3923.
Bähr I, Spielmann J, Quandt D, Kielstein H. Obesity-Associated Alterations of Natural Killer Cells and Immunosurveillance of Cancer. Front Immunol 2020; 11. doi:10.3389/fimmu.2020.00245.
Uribe-Querol E, Rosales C. Neutrophils Actively Contribute to Obesity-Associated Inflammation and Pathological Complications. Cells 2022; 11: 1883.
Qin S, Jiang Y, Wei X, Liu X, Guan J, Chen Y et al. Dynamic changes in monocytes subsets in COVID-19 patients. Hum Immunol 2021; 82: 170–176.
Laing AG, Lorenc A, del Molino del Barrio I, Das A, Fish M, Monin L et al. A dynamic COVID-19 immune signature includes associations with poor prognosis. Nat Med 2020; 26: 1623–1635.
Wilk AJ, Rustagi A, Zhao NQ, Roque J, Martínez-Colón GJ, McKechnie JL et al. A single-cell atlas of the peripheral immune response in patients with severe COVID-19. Nat Med 2020; 26: 1070–1076.
Parackova Z, Zentsova I, Bloomfield M, Vrabcova P, Smetanova J, Klocperk A et al. Disharmonic Inflammatory Signatures in COVID-19: Augmented Neutrophils’ but Impaired Monocytes’ and Dendritic Cells’ Responsiveness. Cells 2020; 9: 2206.
Schönrich G, Raftery MJ. The PD-1/PD-L1 Axis and Virus Infections: A Delicate Balance. Front Cell Infect Microbiol 2019; 9: 207.
Sabbatino F, Conti V, Franci G, Sellitto C, Manzo V, Pagliano P et al. PD-L1 Dysregulation in COVID-19 Patients. Front Immunol 2021; 12: 695242.
Bost P, De Sanctis F, Canè S, Ugel S, Donadello K, Castellucci M et al. Deciphering the state of immune silence in fatal COVID-19 patients. Nat Commun 2021; 12: 1428.
Agrati C, Sacchi A, Bordoni V, Cimini E, Notari S, Grassi G et al. Expansion of myeloid-derived suppressor cells in patients with severe coronavirus disease (COVID-19). Cell Death Differ 2020; 27: 3196–3207.
Kong Y, Wang Y, Wu X, Han J, Li G, Hua M et al. Storm of soluble immune checkpoints associated with disease severity of COVID-19. Signal Transduct Target Ther 2020; 5: 192.
Hoxha M. What about COVID-19 and arachidonic acid pathway? Eur J Clin Pharmacol 2020; 76: 1501–1504.
Wijngaarden LH, van der Harst E, Klaassen RA, Dunkelgrun M, Kuijper TM, Klepper M et al. Effects of Morbid Obesity and Metabolic Syndrome on the Composition of Circulating Immune Subsets. Front Immunol 2021; 12: 1–10.
Maucourant C, Filipovic I, Ponzetta A, Aleman S, Cornillet M, Hertwig L et al. Natural killer cell immunotypes related to COVID-19 disease severity. Sci Immunol 2020; 5. doi:10.1126/sciimmunol.abd6832.
Mazzoni A, Salvati L, Maggi L, Capone M, Vanni A, Spinicci M et al. Impaired immune cell cytotoxicity in severe COVID-19 is IL-6 dependent. J Clin Invest 2020; 130: 4694–4703.
McLeish KR, Shrestha R, Vashishta A, Rane MJ, Barati MT, Brier ME et al. Differential Functional Responses of Neutrophil Subsets in Severe COVID-19 Patients. Front Immunol 2022; 13. doi:10.3389/fimmu.2022.879686.
de Oliveira YLM, de Sá Resende A, Martins-Filho PR, de Moura TR. Role of triggering receptor expressed on myeloid cells-1 (TREM-1) in COVID-19 and other viral pneumonias: a systematic review and meta-analysis of clinical studies. Inflammopharmacology 2022; 30: 1037–1045.
Dragon-Durey M-A, Chen X, Kirilovsky A, Ben Hamouda N, El Sissy C, Russick J et al. Differential association between inflammatory cytokines and multiorgan dysfunction in COVID-19 patients with obesity. PLoS One 2021; 16: e0252026.
Morrissey SM, Geller AE, Hu X, Tieri D, Ding C, Klaes CK et al. A specific low-density neutrophil population correlates with hypercoagulation and disease severity in hospitalized COVID-19 patients. JCI Insight 2021; 6. doi:10.1172/jci.insight.148435.

Table 1. Characteristics of ICU COVID-19 patients at the time of blood collection.

	Obese (BMI ≥ 30; n = 9)	Non-obese (BMI < 30; n = 18)
Characteristics
Women (%)	7 (77.78)	9 (42.85)
Age (years) ^#	67.67 ± 11.08 *	50.67 ± 20.62
BMI (kg/m²) ^#	34.56 ± 3.63 *	23.78 ± 1.85
Medical history
Hypertension (%)	3 (33.33)	6 (33.33)
Cardiovascular disease (%)	1 (11.11)	1 (5.55)
Type 2 DM (%)	4 (44.44)	3 (16.66)
COPD (%)	1 (11.11)	1 (5.55)
CLD (%)	0	1 (5.55)
Laboratory data
Erythrocytes 10⁶/Ul^§	3.50 [3.17 – 3.66]	3.19 [2.83 – 3.51]
Hemoglobin g/Dl ^#	9.23 ± 1.06	8.88 ± 1.17
Hematocrit % ^#	28.71 ± 4.97	26.24 ± 4.12
Platelets 10³/Ul^#	184.30 ± 117.20	218.60 ± 141.00
Total Leukocytes 10³/Ul^§	13790 [12215 – 18185]	13100 [9650 – 25135]
Neutrophils % ^#	85.30 ± 9.75	83.23 ± 10.17
Eosinophils % ^§	1.80 [0.20 – 5.06]	0.72 [0.18 – 2.97]
Basophils % ^§	0.50 [0.40 – 0.67]	0.40 [0.28 – 0.66]
Lymphocytes% ^§	3.94 [2.67 – 9.57]	6.64 [2.05 – 14.40]
Monocytes % ^§	1.40 [1.30 – 3.80] *	4.90 [2.08 – 6.86]
N/L ratio ^§	23.39 [5.08 – 34.49]	12.23 [5.34 – 45.51]
Serum Urea mg/Dl^§	94.00 [57.00 – 99.50]	79.50 [38.50 – 120.30]
Serum Creatinine mg/Dl^§	0.71 [0.57 – 0.93]	1.11 [0.65 – 1.95]
sTREM-1 (pg/ml) ^§	631.70 [184.40 - 719.90]	403.00 [138.90 – 763.70]
IL-6 (pg/ml)^§	34.86 [25.40 – 43.11]	49.82 [22.71 – 70.97]

Legend: ICU: intensive care unit; DM: Diabetes Mellitus; COPD: Chronic Obstructive Pulmonary Disease; CLD: Chronic Liver Disease; N/L ratio: Neutrophils/Lymphocytes ratio; ALT: alanine aminotransferase; AST: aspartate aminotransferase; CRP: C-Reactive Protein; sTREM-1: soluble triggering expressed on myeloid cells-1; IL-6: interleukin 6.

Percentage of white blood cells was calculated according to total leukocytes of each patient.

^#Data presented as mean and standard deviation.

^§Data presented as median and 25-75 percentiles.

*p < 0.05 by Student’s T test with Welch’s correction or Mann-Whitney

There is NO conflict of interest to disclose

Download PDF

Version 1

posted

You are reading this latest preprint version

Obesity is associated with a distinct innate immune phenotype in severe COVID-19 patients

Status:

Version 1

Abstract

Figures

Introduction

Materials And Methods

Results

Discussion

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1