Association of soluble PD-L1 and NLR combination with 1-Year mortality in patients with COVID-19

Abstract

Purpose
Understanding the relationship between patient immune characteristics, disease severity, and mortality represents a critical step in the fight against COVID-19. Elevated levels of programmed death ligand-1 (PD-L1) and Neutrophil-lymphocyte ratio (NLR) are linked to increased severity of acute COVID-19 in patients. This study aimed to investigate the association of the combination of sPD-L1 and NLR with 1-year Mortality in patients with COVID-19.

Methods
A prospective study was conducted involving patients with COVID-19 in Karaganda, Kazakhstan. The level of sPD-L1 in the blood serum was evaluated by ELISA. The effect of biomarkers on the development of mortality was analyzed with multivariate regression.

Results
The risk of mortality within one year HR was 2.46 if the plasma sPD-L1 value of more than 277.13 pg/ml, and for NLR more than 2.46 HR was 2.87. The model of combining sPD-L1 and NLR resulted in an improvement in the predictive accuracy of the Hazard Ratio 7.6 (95 % CI: 3.02–19.11).

Conclusion
The combination of two immune-mediated markers (sPD-L1 and NLR), which reflect the systemic inflammatory balance of activation and exhaustion, can complement each other and improve the assessment of the risk of death in patients with COVID-19.

Full text

International Immunopharmacology 129 (2024) 111600
Available online 6 February 2024
1567-5769/© 2024 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-
nc-nd/4.0/).
Association of soluble PD-L1 and NLR combination with 1-Year mortality in
patients with COVID-19
Lyudmila Akhmaltdinova
a
, Irina Mekhantseva
a
,
*
, Lyudmila Turgunova
a
, Mikhail Kostinov
b
,
Zhibek Zhumadilova
a
, Anar Turmukhambetova
a
a
Karaganda Medical University, Scientic and Research Center, Karaganda, Kazakhstan
b
I.I. Mechnikov Research Institute of Vaccines and Sera, Sechenov First Moscow State Medical University, Department of Epidemiology and Modern Vaccination
Technologies, Moscow, Russia
ARTICLE INFO
Keywords:
COVID-19
Post-COVID Mortality
sPDL-1
Neutrophil-to-Lymphocyte Ratio
Humoral and cellular immune response
ABSTRACT
Purpose: Understanding the relationship between patient immune characteristics, disease severity, and mortality
represents a critical step in the ght against COVID-19. Elevated levels of programmed death ligand-1 (PD-L1)
and Neutrophil-lymphocyte ratio (NLR) are linked to increased severity of acute COVID-19 in patients. This study
aimed to investigate the association of the combination of sPD-L1 and NLR with 1-year Mortality in patients with
COVID-19.
Methods: A prospective study was conducted involving patients with COVID-19 in Karaganda, Kazakhstan. The
level of sPD-L1 in the blood serum was evaluated by ELISA. The effect of biomarkers on the development of
mortality was analyzed with multivariate regression.
Results: The risk of mortality within one year HR was 2.46 if the plasma sPD-L1 value of more than 277.13 pg/ml,
and for NLR more than 2.46 HR was 2.87. The model of combining sPD-L1 and NLR resulted in an improvement
in the predictive accuracy of the Hazard Ratio 7.6 (95 % CI: 3.02–19.11).
Conclusion: The combination of two immune-mediated markers (sPD-L1 and NLR), which reect the systemic
inammatory balance of activation and exhaustion, can complement each other and improve the assessment of
the risk of death in patients with COVID-19.
1. Introduction
SARS-CoV-2 has had a signicant impact on public health and has
forced a re-evaluation of traditional epidemic management approaches.
Data from multiple epidemiological studies indicate that individuals
previously infected with COVID-19 are at a signicantly higher risk of
death within the next 12 months [1,2]. Predictors of COVID-19 severity
and mortality in the acute period are well-studied; long-term conse-
quences, including mortality, are still under investigation. Understand-
ing the relationship between patient immune characteristics, disease
severity, and mortality represents a critical step in the ght against
COVID-19. Severe and critical manifestations of COVID-19 are caused by
a dysregulated immune response, in which the adaptive immune system
driven by T and B lymphocytes plays a fundamental role [3].
Previous studies have established the importance of elevated levels
of programmed death ligand-1 (PD-L1) and its receptor PD-1 in the
pathogenesis of cancer [4–7] and various infectious diseases, including
AIDS and hepatitis [8–10] There is evidence to suggest that elevated
levels of PD-L1 might be linked to increased severity of acute COVID-19
in patients [11,12]. The interaction between the checkpoint molecules
PD-1 and its ligand PD-L1 helps to reduce inammation and tissue
damage in severe and critical cases of COVID-19 [13,14]. However, due
to increased levels of PD-1 expression, T cells can become exhausted and
their effector functions may decrease, leading to the progression of the
disease. Beserra et al. analyzed the levels of sPD-1 and sPD-L1 in the
serum of patients and observed an increase in sPD-L1 in patients with
severe and critical symptoms [15]. Additionally, it has been reported
that increased levels of sPD-L1 were associated with lower lymphocyte
counts and higher CRP levels, and were also linked to longer hospital
stays and higher in-hospital mortality rates [16]. However, the mecha-
nisms underlying increased PD-L1 levels in SARS-CoV-2 are not fully
understood, and studies on soluble PD-L1 in COVID-19 patients with
* Corresponding author.
E-mail addresses: immunol.lab@gmail.com (L. Akhmaltdinova), mehanceva@qmu.kz (I. Mekhantseva), turgunova@qmu.kz (L. Turgunova), monolit.96@mail.ru
(M. Kostinov), Zhumadilova.Z@qmu.kz (Z. Zhumadilova), turmuhambetova@qmu.kz (A. Turmukhambetova).
Contents lists available at ScienceDirect
International Immunopharmacology
journal homepage: www.elsevier.com/locate/intimp
https://doi.org/10.1016/j.intimp.2024.111600
Received 12 December 2023; Received in revised form 16 January 2024; Accepted 24 January 2024

International Immunopharmacology 129 (2024) 111600
2
varying disease severity are scarce.
It has been established that systemic inammation is a key factor in
COVID-19 development [17–19]. Baseline Neutrophil-lymphocyte ratio
(NLR) level has been proposed as a prognostic marker in COVID-19 [20].
Compelling evidence of immune dysfunction associated with COVID-
19 and its long-term impact on mortality is essential for effective patient
stratication in planning preventive measures and clinical management
in the post-covid period. Therefore, this study aimed to investigate the
association of the combination of immune-mediated mediators such as
sPD-L1 and NLR with 1-year Mortality in patients with COVID-19.
2. Materials and Methods
2.1. Study design and participants
A prospective study was conducted at the infectious diseases clinics
of Karaganda Regional Clinical Hospital and Karaganda Medical Uni-
versity Hospital between May and August 2021. The main group for this
study included patients over 18 years of age who had tested positive for
COVID-19. Pregnant and lactating women, and individuals with
compromised immune systems, such as those affected by human im-
munodeciency virus (HIV) infection, or those currently undergoing
treatment for malignancies, were excluded from the study. The study
involved 314 COVID-19-positive patients conrmed by RT-PCR analysis
of nasopharyngeal swabs for genomic RNA with or without co-existing
conditions, undergoing treatment at an infectious diseases hospital.
Clinical data and laboratory results were collected from patients’
electronic medical records. All laboratory tests, including complete
blood count, plasma C-reactive protein (CRP), ferritin, and D-dimer
concentrations, were collected on the same day as plasma sampling.
Clinical outcomes (intensive care unit admission, length of hospital stay,
mortality) were recorded until hospital discharge. To assess the severity
of COVID-19 in patients, WHO criteria were used [21]. Two groups of
patients were identied: the rst group included those with moderate
severity, while the second group consisted of patients with severe and
critical disease severity. Electronic medical records were used to collect
data on the disease progression, comorbidities, anthropometric in-
dicators, heart rate (HR), oxygen saturation, and percentage of lung
tissue damage. Body mass index and Charlson comorbidity index were
calculated for all patients [22]. All examinations were carried out on the
rst day of admission to the hospital. After discharge, patients were
monitored via phone and recorded in the medical information system for
treatment and death. The endpoint considered all deaths, including
cause and date. The observation period for COVID-19 patients was one
year from hospital admission, with survivors dened as those alive after
this time.
2.2. Laboratory analysis
Blood samples were collected on admission day by venipuncture into
2 vacuum tubes containing 5 ml EDTA. Plasma was collected and ali-
quoted, and serum aliquots were stored at −80 ◦C. Enzyme-linked
immunosorbent assays (ELISA) were used to quantify plasma sPD-L1
concentrations with a research-only reagent kit (Abcam, #ab277712).
Colourimetric determinations were recalculated using a four-parameter
logistic curve corresponding to each plate. A complete blood count was
performed on a hematology analyzer (Mindray, China).
2.3. Statistical analysis
The data from the study was analyzed statistically using SPSS version
21.0. (IBM Corp., Armonk, NY, USA) и MedCalc version 22 (MedCalc
Software Ltd, Ostland, Belgium). Data presented in tables and graphs
using GraphPad Prism software version 9.5 (GraphPad Software, Inc.,
San Diego, California, USA). The normality of the distribution was
assessed using the Kolmogorov-Smirnov test. Quantitative measures
with non-normal distribution are described by median (Me) and inter-
quartile range. Qualitative characteristics are described with percent-
ages. Comparative analysis of quantitative data between groups utilized
the Mann-Whitney score for nonparametric distributions and Fisher’s
exact tests for categorical variables. The discrimination score was
determined by the area under the curve (AUC) of the receiver operating
characteristic (ROC). AUCs with 95 % condence intervals were calcu-
lated to estimate the diagnostic value of NLR and sPD-L1. Optimal cut-
off values for these biomarkers were determined based on the
maximum Youden’s J index and further used to estimate survival
considering high and low biomarker concentrations using Kaplan-Meier
survival analysis. Hazard ratios were based on the log-rank (Mantel-Cox)
test. Using multivariate regression, the effect of biomarkers on the
development of mortality was analyzed, adjusting for already proven
risk variables such as gender, age, comorbidity, and disease severity.
Statistical test differences were considered signicant if p values were <
0.05.
2.4. Ethical declare
This study was conducted in accordance with the Declaration of
Helsinki and approved by the Bioethics Committee of Karaganda Med-
ical University No. 18, dated 14 April 2021. Written informed consent
was obtained from the participants.
3. Results
3.1. Demographic data and clinical and laboratory characteristics of
patients
The study population (n =314) was divided into groups based on
disease severity (moderate disease [n =245] vs. severe disease [n =69]:
49 severe patients (71 %) and 20 patients (29 %) with critical disease
severity) and outcome (survivors [n =275] vs. non-survivors [n =39]).
Table 1 displays patient characteristics, grouped by disease severity and
mortality. The patients who contracted COVID-19 had an average age of
63 years. There was no signicant difference in the median age between
patients with moderate and severe severity. However, the median age of
those who died was 72 years, while the median age of survivors was 61
years (p =0.0001). Patients with severe disease and those who died had
a higher prevalence of comorbidities. Out of the 184 patients (58.5 %)
had chronic diseases, including hypertension (57.6 %), diabetes mellitus
(26.1 %), cardiovascular diseases (42 %), chronic pulmonary diseases
(2.5 %), and chronic kidney disease (4.7 %). The group of patients with
severe COVID-19 and the group of deceased patients had higher levels of
leukocytes, neutrophils, NLR, and ESR, as well as inammatory markers
such as CRP, ferritin, and D-dimer (Table 1). The median hospitalization
time for patients with COVID-19 differed between the moderate and
severe disease groups (p =0.0001). Moreover, patients with severe
illness and those who unfortunately passed away had a higher chance of
receiving respiratory assistance and being admitted to the intensive care
unit (ICU) (p =0.0001). Comparable ndings were observed while
comparing the groups of survived and deceased patients (p =0.0001).
3.2. sPD-L1 concentrations in patients with COVID-19
Severe COVID-19 patients have been found to have higher plasma
concentrations of sPD-L1 NLR levels compared to patients with moder-
ate disease. Fig. 1A shows that sPD-L1 levels were 279.1 [183.4–401.3]
pg/mL for severe patients and 225.9 [150.7–329.5] pg/mL for moderate
patients, indicating a statistically signicant difference (p =0.011).
Similarly, Fig. 1B shows that the NLR levels were 3.4 [2.4–5.3] pg/ml for
severe patients and 2.4 [1.5–3.5] pg/mL for moderate patients (p =
0.0001). Similarly, non-survivors had higher plasma sPD-L1 concen-
trations (Fig. 1C: 295.6 [177.8–390.4] pg/ml vs 228.6 [154.4–336.2]
pg/ml, p =0.045) and NLR (Fig. 2D: 3.4 [2.4–8.4] pg/ml vs. 2.4
L. Akhmaltdinova et al.

International Immunopharmacology 129 (2024) 111600
3
[1.6–3.7] pg/ml, p =0.0001) compared with survivors. Additionally,
the relationship between markers and disease severity was separately
assessed in subgroups of survivors and deceased to provide evidence that
plasma concentrations of sPD-L1 and NLR are associated with mortality,
independent of disease severity (Supplementary Fig. A1). In the
deceased group, there were no statistically signicant differences in the
concentrations of sPD-L1 and NLR in patients with moderate and severe
COVID-19.
3.3. Discriminatory impact of sPD-L1 and NLR integration on mortality
The diagnostic value of sPD-L1 and NLR in patients with COVID-19
was assessed using ROC curves. To assess discrimination between sur-
vivors and deceased based on concentrations, the results are presented
as ROC curves (Fig. 2). Table 2 presents the characteristics of the ROC
analyses. The calculated AUC for sPD-L1 was 0.609 (95 % CI:
0.552–0.663, p =0.033), indicating moderate discrimination between
survivors and deceased. The discriminatory power of NLR was slightly
higher and also statistically signicant (AUC =0.684, 95 % CI:
0.642–0.723; p =0.0001). Threshold values for these markers were
based on the maximum Youden’s J index obtained from the ROC curves.
Survival of patients was stratied based on these markers. Hazard ratios
were calculated using the log-rank test (Mantel-Cox). The study found
that patients with plasma sPD-L1 levels greater than 277.13 pg/mL had
a higher risk of mortality within a year (HR =2.46, 95 % CI: 1.27–4.74,
shown in Fig. 3A). Similarly, patients with a high NLR greater than 2.46
(shown in Fig. 3B) had a hazard ratio for mortality of 2.87 (95 % CI:
1.49–5.53; p =0.0015). When creating the model, combining the cutoff
values of sPD-L1 and NLR, the Hazard Ratio was 7.6 (95 % CI:
3.02–19.11, Fig. 3C), thereby proving a synergistic relationship between
sPD-L1 and NLR in deceased.
3.4. Multivariate regression analysis to predict mortality in patients with
COVID-19
A multivariate regression model was developed to forecast the pos-
sibility of death in patients admitted to the hospital with COVID-19. For
this model, patients were classied with high or low levels of sPD-L1 and
NLR according to cutoff values (Table 2). The model that included high
levels of sPD-L1 and NLR demonstrated a high predictive variable (OR
2.359, p =0.044), even after adjusting the regression model by gender
(OR 1.334, p =0.480), age over 60 years (OR 4.183, p =0.006),
Charlson Comorbidity Index (OR 2.489, p =0.147) and severity of
illness (OR 1.990, p =0.108) (Table 3).
4. Discussion
Our study ndings provide evidence to support the hypothesis that
sPD-L1 levels are increased in patients with severe COVID-19 and those
who have died. These results indicate that sPD-L1 may contribute to the
progression of COVID-19 and could be a potential target for further
immunotherapy.
Table 1
Demographic and clinical characteristics of COVID-19 patients.
All patients
(n =314)
Moderate illness
(n =245)
Severe illness
(n =69)
p-
value*
Survivors (n =275) Non-survivors (n =
39)
p-
value**
Demographic
Age (year) 63 (51–71) 61 (50–71) 67 (56–72) 0.082 61 (49–70) 72 (63–82) 0.0001
Sex, n (%)
Male
Female
121 (38.6)
193 (61.4)
97 (39.6)
148 (60.4)
24 (34.8)
45 (65.2)
0.469 170 (61.8)
105 (38.2)
16 (41)
23 (59)
0.733
BMI (kg/m
2
) 29.4 (25.4–34.1) 28.9 (25.4–33.6) 30.8 (25.7–35.5) 0.152 29.4 (25.5–34.2) 28.8 (23.6–33.9) 0.368
Comorbidities or coexisting disorders, n (%)
Hypertension 181 (57.6) 134 (54.6) 47 (68.1) 0.046 158 (57.4) 23 (58.9) 0.857
Cardiovascular disease 132 (42) 92 (37.5) 40 (57.9) 0.002 112 (40.7) 20 (51.2) 0.211
Diabetes mellitus 82 (26.1) 60 (24.4) 22 (31.8) 0.217 72 (26.1) 10 (25.6) 0.943
Pulmonary disease 8 (2.5) 7 (2.8) 1 (1.4) 0.883 6 (2.18) 2 (5.1) 0.443
Chronic renal failure 15 (4.7) 6 (2.4) 9 (13.01) 0.002 13 (4.7) 2 (5.1) 0.913
Charlson Comorbidity Index, grade
0–1 201 (64) 166 (68) 35 (51) 0.015 183 (67) 18 (46) 0.002
2–3 85 (27) 61 (25) 24 (35) 0.015 74 (27) 11 (28) 0.002
>4 28 (9) 18 (7) 10 (14) 0.015 18 (6) 10 (26) 0.002
Vital signs at day of sampling, n (%)
Heart rate (bpm) 80 (76–87) 80 (76–86) 82 (76–90) 0.248 80 (76–86) 80 (76–90) 0.516
Temperature (◦C) 36.7 (36.5–37.2) 36.7 (36.5–37.2) 36.7 (36.6–37.3) 0.337 36.7 (36.5–37.2) 36.8 (36.6–37.5) 0.128
Respiratory rate (vpm) 19 (18–21) 19 (18–20) 20 (18–22) 0.004 19 (18–20) 22 (19–22) 0.0001
Oxygen saturation, % 95 (93–98) 96 (94–98) 93 (90–96) 0.0001 95 (94–98) 95 (91–98) 0.116
Inltrate on chest radiograph,
%
25 (12–40) 20 (8–32) 56 (45–66) 0.0001 25 (12–40) 40 (20–52) 0.012
Hospital length of stay (days) 10 (9–12) 10 (8–11) 11 (9–15) 0.0001 10 (9–11) 11 (8–15) 0.383
Invasive mechanical
ventilation
20 (6.3) – 20 (29) – 8 (2.9) 12 (30.8) 0.0001
Mortality 39 (12.4) 22 (9) 17 (24.6) 0.0001 NA NA NA
Laboratory ndings
Leukocytes x 10
9
/L 5.2 (3.9–6.5) 5.1 (3.9–6.2) 5.8 (4.2–7.0) 0.027 5.2 (3.9–6.3) 5.9 (4.5–7.2) 0.043
Neutrophils x 10
9
/L 3.29 (2.30–4.49) 3.17 (2.20–4.34) 3.95 (2.81–5.47) 0.001 3.24 (2.24–4.36) 4.07 (2.91–5.58) 0.004
NLR 2.6 (1,6–3.8) 2.4 (1,5–3.5) 3.4 (2.4–5.3) 0.0001 2.4 (1.6–3.7) 3.4 (2.4–8.4) 0.0001
ESR, (mm/h) 15 (10–22) 15 (10–23) 15 (10–20) 0.0001 15 (10–22) 15 (8–20) 0.597
CRP (mg/l) 15.1 (6.1–74.7) 12.0 (6.0–37.3) 35.9 (12.0–145.2) 0.0001 12.0 (6.0–46.4) 47.1 (8.2–104.8) 0.031
Ferritin (
μ
g/l) 238 (148–375) 233 (130–356) 254 (186–440) 0.039 234 (148–361) 244 (163–435) 0.335
D-dimer, (ng/ml) 301 (181–441) 286 (165–423) 378 (205–523) 0.004 286 (169–425) 425 (305–549) 0.0001
sPD-L1 (pg/ml) 232.5
(159.1–344.5)
225.9
(150.7–329.5)
279.1
(183.4–401.3)
0.011 228.6
(154.4–336.2)
295.6 (177.8–390.4) 0.045
Data are presented as median with interquartile ranges or n (%). Patient data were compared using the chi-square test, or Fisher’s exact test for categorical variables
and one-way analysis of variance. Mann–Whitney, nonparametric t-test was used for continuous variables. p <0.05 was considered statistically signicant. *Moderate
versus severe illness. **Survivors versus non-survivors. Abbreviations: Body mass index (BMI), Blood pressure (BP), Intensive care unit (ICU), C-reactive protein (CRP),
Erythrocyte sedimentation rate (ESR).
L. Akhmaltdinova et al.

International Immunopharmacology 129 (2024) 111600
4
Fig. 1. The plasma concentration of sPD-L1 (A) and NLR (B) moderate and severely ill patients, survivor, and non-survivor patients (C and D). Data are presented as
median with interquartile ranges, p-values were calculated with Mann–Whitney U tests.
Fig. 2. Receiver operating characteristic (ROC) curves of sPD-L1 (A) and NLR (B) concentrations to predict mortality among patients with COVID-19. The area under
the curve (AUC) and the p-values for signicant are depicted in the graphic.
L. Akhmaltdinova et al.

International Immunopharmacology 129 (2024) 111600
5
Multiple retrospective studies have shown that uncontrolled immune
responses and hyperinammation are hallmarks of severe COVID-19
[23,24]. The SARS-CoV-2 virus leads to a decrease in the number of
lymphocytes, additionally, it can also cause the shrinking of secondary
lymphoid organs, such as the spleen and lymph nodes [25,26]. Bio-
markers of SARS-CoV-2 infection may include overproduction of cyto-
kines that can cause lung damage, such as interleukin-1β (IL-6β), IL-2,
interferon-γ (IFN-γ), tumor necrosis factor
α
(TNF-
α
) or transforming
growth factor β (TGF-β) [27–30]. CD4 and CD8 T cells in COVID-19
patients express markers of T cell exhaustion, including PD-1 and Tim-
3, which contribute to SARS-CoV-2-induced sepsis and death [13].
The main hypothesis for the immunopathogenesis of long-term COVID-
associated complications is that it may involve persistent virus or viral
antigens and RNA in tissues, leading to chronic inammation. This can
trigger autoimmunity following an acute viral infection, as well as
dysbiosis of the microbiome and irreversible tissue damage. All of these
factors, to some degree or another, involve the immune system [31].
Previous studies indicate elevated PD-L1 levels in COVID-19 patients
with severe symptoms [32]. However, studies on soluble PD-L1 are
limited to L. Chavez-Galan et al demonstrated that increased sPD-L1
levels in COVID-19 patients are associated with requiring mechanical
ventilation [33]. F. Sabbatino et al showed that higher levels of sPD-L1
were found in deceased COVID-19 patients compared to survivors [16].
Our study suggests that the levels of sPD-L1 continue to be linked with
mortality even after one year of hospitalization.
The immune system is damaged during COVID-19 due to a failed
antiviral activity of interferons, which results in systemic manifesta-
tions. Additionally, there is higher degranulation of neutrophils, and
increased levels of cytokines, both in acute and long-term periods
[34–37]. Combining different biomarkers shows promise in assessing
COVID-19 patient outcomes. During the rst wave of the COVID-19
pandemic, lymphopenia and NLR emerged as early prognostic markers
[38,39]. The biomarker NLR is a crucial component of the risk score
used to predict the likelihood of developing critical illness from COVID-
19 [38,40,41]. Its presence is explained by its contribution to the
pathogenesis of inammation and its related complications in a broader
sense.
Our study showed that the combination of two immune-mediated
markers (sPD-L1 and NLR), which reect the systemic inammatory
balance of activation and exhaustion, can complement each other and
improve the assessment of the risk of death in patients with COVID-19.
With a plasma sPD-L1 value of more than 277.13 pg/ml, the risk of
mortality within one year HR was 2.46, and for NLR more than 2.46 HR
was 2.87. Combining sPD-L1 and NLR while creating a model resulted in
a signicant improvement in the predictive accuracy of the Hazard
Ratio, which was found to be 7.6 (95 % CI: 3.02–19.11).
It can be assumed that patients with more severe disease are at
higher risk of death, and therefore there is overlap in groups depending
Table 2
ROC-test characteristics. Signicance of sPD-L1 and NLR at admission in predicting mortality in patients with COVID-19.
Cut-off Youden’s J AUC (95 % CI) Sensitivity, % (95 % CI) Specicity, % (95 % CI) p-value
sPD-L1 >277.13 0.2395 0.609 (0.552–0.663) 57.89 (40.8 – 73.7) 66.06 (60.1 – 71.6) 0.033
NLR >2.46 0.2737 0.684 (0.642–0.723) 77.7 (60.8 –89.9) 49.59 (45.1 –54.1) 0.0001
Fig. 3. Survival analysis of patients with COVID-19 for high versus low con-
centrations of (A) NLR, (B) sPD-L1, (C) integration of sPD-L1 and NLR. Hazard
ratios were calculated with the log-rank (Mantel-Cox) test.
Table 3
Multivariate Regression Model for Mortality in patients with COVID-19.
Variable OR (95 % Condence Interval) p-value
Sex (Male) 1.334 (0.600–2.965) 0.480
Age over 60 years 4.183 (1.513–11.599) 0.006
Charlson Comorbidity Index 2.489 (0.725–8.542) 0.147
Severity illness 1.990 (0.861–4.599) 0.108
High sPDL-1 and High NLR 2.359 (1.025–5.429) 0.044
*OR =Odds ratio.
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International Immunopharmacology 129 (2024) 111600
6
on disease severity and mortality. It should be noted that sPD-L1 and
NLR concentrations (Fig. A1) in the non-survivor group did not differ
according to disease severity. It is suggested that elevated plasma con-
centrations of sPD-L1 and NLR are associated with increased mortality,
regardless of disease severity.
The importance of these ndings relates to the possibility of using
PDL-1 as a therapeutic target. Blockade of the PD-1/PDL-1 pathway is a
known therapeutic target in oncology, and
α
-PDL-1 antibody-based
therapies are currently in ongoing clinical trials to evaluate their ef-
fect on severe sepsis/septic shock [42]. Our study supports the theory
that specic anti-PDL therapy can contribute to both short-term and
long-term mortality reduction in COVID-19. Futrhermore, there is
another way to utilize this biomarker. The manifestations of COVID-19
vary widely and the severity of the disease is not always predictable.
Accordingly, personalizing therapy using PDL-1 inhibitors and cortico-
steroid therapy for patients with acute infections and post-infectious
therapy that induces PD-1/PDL-1 expression can improve patient
outcomes.
Our study found that age was not a signicant factor in disease
severity. However, we did observe a higher mean age in the group of
deceased patients, indicating that age may be a contributing factor to
poor outcomes (p =0.0001). Comorbidity was higher in both the group
of severe patients and the deceased. The differences in CRP, dimer, and
ferritin levels correspond with other studies based on disease severity
and outcome [43,44] A. Mainous et al. showed that increased levels of
CRP, which is one of the indicators of severe COVID-19 in the acute
period, are associated with an increased risk of mortality after 12
months of follow-up [45]. There were signicant differences in the main
inammatory markers CRP and D-dimer between survivors and
deceased, as well as between severe and moderate cases. Additionally,
ESR and ferritin levels also differed.
Inevitably, our study has several limitations. Firstly, we did not
investigate genetic variations in PD-L1 that may affect the expression
and function of this biomolecule [46,47]. Secondly, our study was
limited by the lack of analysis of how increased mortality from various
causes is associated with sPD-L1 and NLR levels. In this study, cardio-
vascular failure and acute vascular events were found to be the most
common causes of death in the post-COVID period. The limited number
of observations in each group was the main limitation to conducting a
detailed analysis of sPD-L1 and NLR concentrations based on mortality
causes. The study of all-cause mortality allowed for the inclusion of
those with conrmed COVID-19 pneumonia who died within one year.
Although another limitation such as examining only mortality as a single
endpoint, its signicant advantage is the relatively extended study
duration in the context of COVID-19 research.
5. Conclusions
Soluble PD-L1 and NLR represent distinct aspects of the inamma-
tory response and are involved in the dysregulated inammatory state
observed in COVID-19 patients. The study results suggest that a com-
bination of these biomarkers may be used to stratify patients with poor
outcome.
Ethics statement.
The study was conducted in accordance with the Declaration of
Helsinki, and approved by the Bioethics Committee of Karaganda
Medical University No. 18, dated 14 April 2021. Written informed
consent was obtained from all patients/participants who took part in
this study.
Funding.
This work was supported by the Ministry of Health of the Republic of
Kazakhstan, Program No. BR11065386.
CRediT authorship contribution statement
Lyudmila Akhmaltdinova: Writing – original draft, Visualization,
Validation, Methodology, Formal analysis, Conceptualization. Irina
Mekhantseva: Writing – original draft, Visualization, Validation, Soft-
ware, Methodology, Formal analysis, Data curation, Conceptualization.
Lyudmila Turgunova: Writing – original draft, Visualization, Valida-
tion, Methodology, Formal analysis, Conceptualization. Mikhail Kos-
tinov: Writing – review & editing, Conceptualization. Zhibek
Zhumadilova: Methodology, Investigation, Data curation. Anar Tur-
mukhambetova: Supervision, Resources, Project administration,
Funding acquisition.
Data availability
Data will be made available on request.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.intimp.2024.111600.
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