Risk factors analysis and risk assessment model construction of systemic lupus erythematosus patients with infection

Abstract

Objective

To analyze the characteristics of peripheral blood lymphocyte subsets in systemic lupus erythematosus (SLE) patients with infection and non-infection group. Explore the risk factors of infection in SLE patients and establish a risk matrix model to predict the occurrence of co-infection.

Methods

total of 333 SLE patients without infection, 163 patients suffering from infection, and 132 healthy controls (HCs) were recruited. General clinical data and disease activity indicators were collected. The levels of total T, B, CD4⁺T, CD8⁺T, NK, Th1, Th2, Th17, and Treg cells in peripheral blood of HCs, SLE patients (including infected and non-infected group) were analyzed by flow cytometry. The risk assessment model was constructed, and the receiver operating characteristic curve was drawn. 39 SLE patients with infection and 20 patients without infection were randomly selected to evaluate the predictive power of the regression model.

Results

The levels of T, B, CD4⁺T, CD8⁺T, and NK cells in the infected patients were significantly decreased when compared with that of both non-infected patients and HCs (p < .05). The non-infected patients had a higher level of Th17 than that of HCs (p < . 05), but the absolute numbers of Th17 in infected patients was the lowest among the three groups (p < .001). The number of Treg cells in SLE patients was significantly lower than that of HCs (p < .01), and the infected patients had the fewest Treg cells among all these groups (p < . 05). A risk assessment model for SLE with infection was established, p = 1/(1⁻e^−y), Y = 1.763-0.004 × Absolute number of CD4 ⁺ T cells-0.005 × Absolute number of NK cells −0.005 × Platelet count(×10¹²/L) + 1.033 × Absolute number of lymphocytes (×10⁹/L) + 0.023 × C-reactive protein (mg/dL), whose predictive sensitivity is 77.5%, and specificity is 78.3%.

Conclusion

The new risk assessment model exhibits good predictive ability to assess co-infection risk in SLE patients. T cells, NK cells, and CD4 ⁺ T cells along with other parameters help in differentiating Lupus with infection from Lupus alone.

Keywords

Systemic lupus erythematosus infection lymphocyte subpopulations risk assessment model

Introduction

Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disorder characterized by a variety of autoantibodies production that affects multi-system in the body with a vast array of clinical manifestations.¹ Its pathogenesis and mechanism are complex, and it can occur at any age, especially in women.² Despite advanced awareness of this disease and multiple autoantibodies, infections are a most common cause of morbidity and mortality in this patient population.^3–5 It is estimated that at least 50% of patients with SLE suffered with infections during the course of their disease⁶ and around 30% of deaths are related to infections.^7,8 The possibility of co-infection in active SLE patients should arouse our vigilance.

The pathogenesis of SLE patients predisposed to infection is complex. Viruses, bacteria, fungi, and protozoa can trigger SLE disease activity through bystander activation, molecular mimicry, and antigenic spreading.⁹ The deregulation of intrinsic and adaptive immune system plays a central role in the susceptibility of patients to infections. Recent studies revealed that SLE patients with infection had immunologic abnormalities, especially those treated by immunosuppressive agents.^10–14 Studies have shown that dysregulation of lymphocyte subpopulations plays an important role in the carcinogenesis and progression of SLE. CD4 ⁺ T cells play an important role in orchestrating immune responses,¹⁵ and CD8⁺ T cells responses are essential for efficient anti-tumor and anti-viral immunity.^16,17 Previous investigators have reported that the inhibitory induction effect of CD4⁺T cells and the function of CD8⁺T lymphocytes in SLE patients are decreased. Immune disorders cause the continuous activated B cells to produce autoantibodies, which are involved in the occurrence and development of SLE disease.^18,19 However, the characteristics of lymphocyte subsets in patients with SLE and their relationship with infection remain unclear, and no further study was performed in elucidating the risk factors for infection in patients with SLE.

The interaction between SLE and infection is complicated. SLE renders its affected patients vulnerable to infection, and infection can induce the aggravation of SLE. However, no study has built an effective risk assessment model for prediction in SLE patients with infection. In this retrospective study, we mainly measured the absolute counts of peripheral blood (PB) lymphocytes and CD4⁺T cell subsets of SLE patients with infection to those without infection and/or those of healthy controls (HCs) to elucidate the immune mechanism of SLE. This study further explored the clinical characteristics of SLE patients with co-infection, and evaluated the risk of infection in SLE patients by establishing a matrix model for early prevention and identification.

Methods

Recruitment of participants

Between July 2014 and December 2016, 495 patients with SLE from the inpatient population of the Second Hospital of Shanxi Medical College were enrolled in the study, including 446 females and 49 males, 333 non-infected groups, and 162 infected groups. All patients fulfilled the 2019 SLE revised classification criteria of the American College of Rheumatology.²⁰ Age- and sex-matched 132 healthy individuals were recruited from the physical examination center of the Second Hospital. Exclusion criteria: (1)Patients with incomplete clinical data; (2) Patients complicated with other autoimmune diseases; (3) Patients during pregnancy and lactation; (4) Patients with other autoimmune diseases, malignant tumors, or serious dysfunction of heart, liver, or kidney; (5) Patients with a recent history of surgery or trauma; (6) age < 18 years old. All subjects recruited for this study provided prior written informed consent and this study was approved by the Ethics Committee of the Second Hospital of Shanxi Medical College (Taiyuan, China).

Infection defined

The history, physical examination, and auxiliary examination were based to determine whether infection is bacterial or viral. The presence of an infection was confirmed by a positive pathogen test from various specimens (blood, sputum, pus, stool, and urine) or clear evidence of infection, such as an abscess on computed tomography. If patients had fever (body temperature over 38.0°C) for at least 3 days caused not by disease activity, and effectively reversed by anti-infective treatment were also considered as infected.^21–23 Infection is not recorded if there is no supporting evidence of infection or doubt about the cause of the current symptoms.

Research methods

The total T, B, CD4 ⁺ T, CD8 ⁺ T, natural killer cell (NK), Th1, Th2, Th17, and Treg lymphocyte subsets in peripheral blood were analyzed by flow cytometry (FCM). The patient’s gender, age, course of disease, infection site, pathogen type, clinical manifestation, systemic lupus erythematosus disease activity index (SLEDAI) score, hormone, immunosuppressant use, and auxiliary laboratory indexes were recorded. We assessed the activity of the disease using the SLEDAI score: no activity (SLEDAI = 0–4), mild activity (SLEDAI = 5–10), moderate activity (SLEDAI = 11–14), and high activity (SLEDAI ≥ 15).²⁴ All patients were on prednisone and/or other immunosuppressive medications to control disease activity.

Flow cytometric analysis

Materials

Phorbol myristate acetate (PMA), Anti-human FoxP3-PE and IL-17-PE, permeabilization buffer 10 × fixation/permeabilization diluent, and fixation/permeabilization concentrate were from Invitrogen, eBioscience, Affymetrix Inc. (by Thermo Fisher Scientific, San Diego, CA 92121, USA). GolgiStop was from BD Biosciences Pharmingen (10975, Torreyana Road, San Diego, USA). Monoclonal antibodies including CD3FITC/CD8PE/CD45PercP/CD4APC, CD3FITC/CD16⁺56-PE/CD45 PercP/CD19APC, CD4-FITC, IL-4PE, IFN-γ-APC, CD25-APC, and Trucount tube (contain a set number of beads) were from Becton-Dickinson (USA).

Absolute lymphocyte count

Briefly, the absolute number of T cell subsets (cell/μL) were established from fresh blood samples using reference beads in the Trucount tube from BD as internal standard for cell concentration. This BD Trucount tube with beads is ready to use and avoids variation produced by adding standard beads one by one.

Analysis of lymphocyte surface and intracellular markers

The total absolute number of CD4⁺T cells was assessed by flow cytometry (FACS Calibur, Becton Dickinson) according to our modified stain-and-then lyse-and-wash protocol in manufacturers’ directions of BD Trucount TM tubes. Briefly, 50 μL of EDTA-anticoagulated venous blood was added into the Trucount tube A and B separately by reverse pipetting and stained by 20 μL of antiCD3-FITC/CD8-PE/CD45-PercP/CD4-APC antibodies in tube A and 20 μL of CD3-FITC/CD16⁺56-PE/CD45-PercP/CD19-APC antibodies in tube B (do not touch the blood). Then cells were mixed with 450 μL of 1X FACS. Fifteen thousand cells were acquired and detected by MultiSET software within 24 h. The absolute number of T, B, CD8⁺T, and NK cells could also be count by the same way.

Analysis of Th1/Th2/Th17 cells

Cells in 80 μL of heparin-anticoagulated venous blood were stimulated by 10 μL PMA, 10 μL ionomycin (final concentration was 750 ng/mL), and 1 μL GolgiStop, respectively, at 37°C for 5 h and then divided into two tubes (tube A and tube B) followed by staining with human anti-CD4-FITC antibodies in room temperature away from light for 30 min. Using 1 mL fresh fixation/permeabilization to fix and permeabilize cells and then stained them by IL-4-PE and IFN-γ-APC in tube A and human anti-IL-17-PE in tube B. The cells were washed with PBS and analyzed using flow cytometry.

Analysis of CD4⁺CD25⁺Foxp3⁺Treg cells

Cells in 80 μL of heparin-anticoagulated vein blood were surface-labeled with anti-CD4-FITC and anti-CD25-APC and fixed and permeabilized by 1 mL fresh fixation/permeabilization followed by staining with human anti-FOXP3-PE.

Statistical analyses

All statistical analyses were performed with SPSS 26.0 software (IBM, USA). The categorical demographic characteristics of the patients were compared with the use of the χ² test. Continuous data that satisfy the homogeneity of normality and variance are presented as mean (±SD). An independent Student’s t-test was used for comparisons between two groups, and one-way analysis of variance (ANOVA) was used for comparisons among three or more groups. Data that does not satisfy the homogeneity of normality or variance are presented as median (range) and compared by Mann–Whitney U test. Spearman correlation test was used for correlation analysis. Statistically significant variables were included in the multi-factor logistic regression analysis, and the variables of measurement data were first converted into classification variables and then analyzed. The risk assessment model building was based on a logistic regression model, the receiver operating characteristic (ROC) curve was drawn, and the samples of clinical SLE patients were randomly selected to determine the predictive efficacy of the model. All statistical tests were conducted by bilateral test, and p < .05 was considered statistically significant.

Results

Demographic and clinical characteristics of SLE patients in the non-infected and infected groups

Of total 495 SLE patients, 302 (60.1%) were female and 162 (32.7%) suffered from infection. As shown in Table 1, there was no significant difference in gender between the patients with and without infection (p > .05). The patients with infection were older than those without infection (p < .05). The average duration of disease in the infected patients was longer than that of the non-infected, but no significant difference was observed (p = .098). All the patients received conventional steroids and immunosuppressive therapies and higher dosage of prednisone was used in infection groups (p < .01). Compared with the non-infected SLE, infected patients had a lower level of hemoglobin and platelet count (p < .001), but a higher proportion of ESR and CRP (p < .001). SLEDAI scores of SLE between two groups were not statistically significant. (Table 1).

Table 1.

Comparison of baseline characteristics between the non-infected group and infected group in patients with SLE.

Characteristics	Non-infected (n = 333)	Infected (n = 162)	Statistic	p Value
Age, year, mean (SD)	35.9 (14.0)	39.0 (15.8)	−2.161	0.032
Female, no. (%)	302 (90.1)	144 (18)	0.629	0.530
Duration of SLE, year, median (range)	3.0 (0.02, 34.0)	4.0 (0.03, 39.9)	−1.538	0.125
WBC, ×10⁹/L, mean (SD)	5.49 (2.98)	5.34 (3.42)	0.485	0.628
Hemoglobin(g/L)	114.207(19.409)	103.834(19.799)	5.543	<0.001
Platelet, ×10¹²/L, mean (SD)	188 (82)	158 (94)	3.614	<0.001
Lymphocyte, ×10⁹/L, mean (SD)	1.35 (0.69)	1.06 (0.66)	4.503	<0.001
Neutrophil, ×10⁹/L, mean (SD)	3.80 (2.84)	3.95 (3.06)	−0.523	< 0.001
ESR(mm/h)	42.202(35.364)	61.213(38.182)	−5.283	<0.001
CRP (mg/dL)	8.168(17.192)	20.524(38.83)	3.753	<0.001
SLEDAI score >9 scores	243(72.97)	122(75.31)	−0.995	0.320
≤9 scores	90	40
Prednisone dose, mg/day, median (range)	30 (0, 60)	40 (0, 60)	−2.666	0.008
Use of concomitant agents (no. of patients)
Hydroxychloroquine	251	112		—
Methotrexate	38	11		—
Leflunomide	73	24		—
Cyclophsphamide	158	67		—
Mycophenolate mofetil	69	33		—

Comparison of peripheral lymphocyte subsets in the SLE infected group, non-infected group, and healthy control group

The absolute numbers of T and CD4⁺T cells in the non-infected group were significantly lower than that of HCs (p < .001), but still dramatically higher than that in infected patients (p < .001). The levels of B, CD8⁺T, and NK cells in the infected patients were significantly decreased when compared with that of both non-infected patients and health controls (p < . 01), while there was no significant difference in these cells between health controls and the non-infected patients (p > .05) (Figure 1(a)). As for CD4⁺T cell subsets, the numbers of Th1 and Th2 cells in the infected group were lower than that of non-infected patients and health controls (p < . 001), though there was no significant difference in these cells between the non-infected patients and HCs (p > .05). The non-infected patients had a higher level of Th17 cells than that of HCs (p < . 05), but the absolute numbers of Th17 cells in infected patients were the lowest among the three groups (p < . 05). The number of Treg cells in SLE patients was significantly lower than that of HCs (p < .01), and the infected patients had the fewest Treg cells among all these groups (p < . 05) (Figure 1(b)).

Figure 1.

Multiple subpopulations of lymphocytes decreased in peripheral blood of SLE patients with infection. Absolute numbers of peripheral lymphocytes subpopulations were analyzed by flow cytometry. Data were presented as mean ± SD and statistical analysis was determined by one-way ANOVA. *p < 0.05, **p < 0.01, ***p < 0.001. (a) shows that SLE patients (n = 495) have lower levels of T, B, NK, CD4+T, and CD8+T cells in PB compared with that of health controls. The numbers of PB lymphocytes in the infected groups (n = 162) was also much lower than those in non-infected patients (n = 333). (b) shows the comparison of numbers of CD4+T cell subsets among different groups. Patients with SLE have lower levels of Th1 cells, Th2 cells, Th17 cells as well as Treg cells compared with those in health donors (n = 132), especially the infected groups.

Correlation analysis between peripheral lymphocyte subsets count and disease activity in SLE-infected patients

The absolute count of peripheral lymphocyte subsets in SLE patients is closely related to disease activity. NK cells were negatively correlated with SLEDAI (r = −0.161, p = .040) and ESR (r = −0.230, p = .003), and had little correlation with CRP. Th1 cells were negatively correlated with ESR (r = −0.278, p = .014) and CRP (r = −0.258, p = .024). Th17 cells were negatively correlated with ESR (r = −0.452, p < .001) and CRP (r = −0.324, p = .004). Treg cells were negatively correlated with ESR (r = −0.351, p = .002), as shown in Table 2

Table 2.

Correlation analysis between peripheral lymphocyte subsets count and disease activity in SLE-infected patients.

	SLEDAI		ESR		CRP
	r	p value	r	p value	r	p value
T	−0.052	0.507	-.232**	0.003	−0.136	0.096
B	0.097	0.218	−0.124	0.117	−0.251**	0.002
CD4+T	−0.090	0.252	−0.273**	< 0.001	−0.140	0.085
CD8+T	−0.048	0.545	−0.124	0.117	−0.095	0.242
NK	−0.161*	0.040	−0.230**	0.003	−0.032	0.692
Th1	0.029	0.803	−0.278*	0.014	−0.258*	0.024
Th2	0.008	0.946	−0.285*	0.012	−0.234*	0.041
Th17	−0.002	0.983	−0.452**	< 0.001	−0.324**	0.004
Treg	−0.142	0.218	−0.351**	0.002	−0.168	0.145

*p < 0.05, **p < 0.01, ***p < 0.001.

Prediction of peripheral lymphocytes in patients with SLE infection

Analysis of clinical characteristics of SLE patients with infection showed that CD4⁺T, CD8⁺T, and NK cells were statistically different from those of the uninfected group (p < .001). The predicted infection value of single peripheral lymphocyte was analyzed by drawing ROC curve. The area under the ROC curve (AUC) of T cell count was the largest, with 809.5 cells/μ L as the critical value, and its sensitivity and specificity for predicting SLE infection are 74.7% and 66.4%, followed by CD4⁺T, CD8⁺T, and NK cell counts. The critical values of 387/μ L, 406/μ L, and 44.5/μ L, respectively, showed low sensitivity and specificity in predicting SLE infection (Figure 2(a) and Table 3). New prediction models should be further explored.

Figure 2.

The receiver operating characteristic (ROC) curve (a) ROC curve of lymphocytes for predicting SLE co-infection. (b) ROC curve of SLE co-infection.

Table 3.

Predictive value of lymphocytes in patients with SLE infection.

	AUC	p Value	Jordan	Cut-off (cells/μL)	Sensitivity	Specificity
	AUC	p Value	Index		(%)	(%)
T	0.741	<0.001	0.410	809.5	74.7	66.4
B	0.627	<0.001	0.241	96.8	53.7	70.4
CD4+	0.738	<0.001	0.386	387.0	77.2	61.4
CD8+	0.697	<0.001	0.333	406.0	73.5	59.9
NK	0.659	<0.001	0.235	44.5	45.7	76.5

AUC, area under the curve.

Multivariate logistic regression analysis of SLE patients with infection

In the comparison of clinical data and baseline characteristics of peripheral lymphocyte subsets of patients with SLE patients with infection, the index of p < .05 was selected as the variable, and whether SLE patients with infection was the dependent variable, so as to further construct the multivariate logistic regression equation. Platelet count was statistically significant for co-infection (OR = 0.995, 95% CI 0.991–0.999, p < .01). The absolute count of lymphocytes was statistically significant for SLE complicated with infection (OR = 2.809, 95% CI 1.471–5.365, p < .01). The effect of C-reactive protein on SLE co-infection was statistically significant (OR = 1.023, 95% CI 1.009–1.037, p = .001). The effect of CD4 ⁺ T lymphocyte absolute count on SLE co-infection was statistically significant (OR = 0.996, 95% CI 0.993–0.999, p < .05). The effect of NK cell absolute count on SLE co-infection was statistically significant (OR = 0.995, 95% CI 0.991–1, p < .05), as shown in Table 4.

Table 4.

Multivariate logistic regression analysis of SLE patients with infection.

Characteristics	B value	Standard error of B value	Wald	p value	OR value	OR value 95% CI
Age, year	0.019	0.010	3.727	0.054	1.020	1–1.04
Prednisone dose, mg/day	−0.006	0.008	0.680	0.409	0.994	0.979-1.009
Hemoglobin(g/L)	−0.011	0.008	1.677	0.195	0.989	0.973-1.006
Platelet, ×1012/L	−0.005	0.002	6.991	0.008**	0.995	0.991-0.999
Lymphocyte, ×109/L	1.033	0.330	9.791	0.002**	2.809	1.471-5.365
ESR(mm/h)	−0.001	0.005	0.036	0.850	0.999	0.99-1.008
CRP (mg/dL)	0.023	0.007	10.368	0.001**	1.023	1.009-1.037
T	−0.001	0.002	0.169	0.681	0.999	0.996-1.002
B	0.000	0.001	0.206	0.650	1.000	0.999-1.002
CD4 + T	−0.004	0.002	5.042	0.025*	0.996	0.993-0.999
CD8 + T	−0.001	0.002	0.188	0.664	0.999	0.996-1.003
NK	−0.005	0.002	4.679	0.031*	0.995	0.991–1

CI, confidence interval. ESR, erythrocyte sedimentation rate. CRP, C-reactive protein *p < 0.05, **p < 0.01, ***p < 0.001.

Build a risk assessment model

The risk assessment model of SLE with infection was constructed by logistic coefficient and constant terms, p = 1/(1−e−^y), Y = 1.763-0.004 × absolute number of CD4⁺T cells −0.005 × absolute number of NK cells −0.005 × platelet count (×10¹²/L) + 1.033 × absolute number of lymphocytes (×10⁹/L) + 0.023 × C-reactive protein (mg/dL), where p is the probability of SLE patients with co-infection, and each indicator value is the actual detected value. ROC curve was drawn according to the constructed regression equation (Figure 2(b)), and AUC value of the area under the curve was 0.838 (95% CI: 0.793, 0.883). The point closest to the upper left corner of the ROC curve was selected. According to each coordinate point, the value at Jorden index (sensitivity + specificity −1) is 0.558, and cut-off = 0.282 is the best diagnostic threshold value. The predicted sensitivity is 77.5% and the specificity is 78.3%. This regression equation has better goodness of fit.

Evaluation of predictive value of regression model

59 SLE patients with matched age and gender hospitalized in the second hospital of Shanxi Medical University from July 2014 to December 2016 were selected, including 39 SLE patients with infection and 20 SLE patients without infection. The constructed regression model was used to calculate the risk index of SLE patients and predict the combined infection. The risk index ≥0.282 was predicted as SLE patients with infection, compared with the actual situation. The sensitivity, specificity, and accuracy of regression model prediction were 86.5%, 68.2%, and 79.7%, respectively, as shown in Table 5

Table 5.

Actual situation and predicted results of 59 validation samples.

Predicted results	n	Actual situation
Predicted results	n	SLE with infection	SLE without infection
SLE with infection	37	32	5
SLE without infection	22	7	15
Total		39	20

Discussion

SLE is a chronic autoimmune disease with multiple organ involvement. Infection is the main cause of death in SLE patients under the immune disorder, disease activity, and the use of hormones and immunosuppressants.⁷ Epidemiological studies have found that the hospitalization rate of SLE patients with infection is 11%–23%,²⁵ which is 12 times higher than that without infection.²⁶ Our results show that patients with older age or longer course of disease are more susceptible to infection. There is no significant difference between the total infection rate of SLE (32.7%) and previous reports (11%–45%).^27,28 We should be highly vigilant about the possibility of co-infection in SLE patients with active disease. Viruses, bacteria, and protozoa lead to immune dysfunction through epitope diffusion, molecular simulation, and bypass activation.⁹ Our data confirm that SLE co-infection occurs in a wide range of sites, in which respiratory tract infection is the most common site, mainly bacteria, followed by fungi, viruses and other infections, which is consistent with previous studies.^25,29 Streptococcus pneumoniae is the most common pathogenic microorganism of respiratory tract infection. Staphylococcus aureus can cause bacteremia, skin, and joint infections. Escherichia coli mainly causes urinary tract infection. Epstein–Barr virus, cytomegalovirus, and respiratory syncytial virus are common viral infections.

The possible cause of infection is lymphocyte disorder and immune system abnormality.^30,31Our results show that the absolute number of T, B, NK, CD4 ⁺ T, CD8 ⁺ T, Th1, Th2, Th17, or Treg cells in peripheral blood of SLE patients is in low level. Moreover, the decrease of these cells was more obvious in SLE patients with infection, suggesting that both innate and adaptive immune systems in SLE patients are disturbed.³² Studies have shown that the levels of CD4⁺T, CD8⁺T, and NK cells in SLE patients with co-infection are lower than those in SLE patients without co-infection, indicating that the immune disorders in SLE patients with co-infection are more significant. The low absolute number of all these lymphocytes in infected SLE patients may serve as an indicator of infection in SLE patients, and monitoring the lymphocyte subsets in SLE patients may provide a reference for the prevention and treatment of infection.

In our study, the number of T cells has interesting sensitivity and specificity of 74.7% and 66.4%, with apparent clinical utility. It is well-known that T cells play a central role in modulating host immune responses to pathogens. CD4⁺ T cells and CD8⁺ T cells make up the majority of T cells.³³ CD4⁺T cells mainly play an auxiliary role in coordinating the interaction between immune cells.¹⁵ CD8⁺T lymphocytes are mainly involved in anti-virus, anti-tumor immunity, and suppression of immune response.^16,17 According to previous reports,^18,19 the inhibition and induction of CD4⁺T cells and the function of CD8 ⁺ T lymphocytes in patients with SLE are reduced. Immune imbalance makes continuously activated B cells produce autoantibodies, which is involved in the occurrence and development of SLE disease. Infections are prone to develop in SLE patients with severe lymphocytopenia, especially CD4⁺ T cell depletion.^34,35

SLE patients themselves have a high risk of infection, and infection can induce the exacerbation of SLE. Clinically, it is difficult to distinguish disease activity and co-infection in SLE patients.³⁶ Therefore, the absolute number of peripheral blood lymphocytes in SLE patients showed that CD4⁺, CD8⁺, and NK cells with co-infection were significantly lower than those without co-infection, which could be an important indicator for the clinical differentiation or diagnosis of SLE co-infection. Monitoring of related indicators during the diagnosis and treatment of SLE patients can be used to assess the degree of disease activity and as a potential standard for predicting SLE co-infection. To clarify the risk factors of SLE patients with infection and establish a preliminary risk assessment model based on logistic regression analysis may provide help for the early diagnosis of SLE patients with co-infection, so as to carry out active intervention and effective treatment. Therefore, in order to control the disease of SLE patients and reduce the hospital length of stay, it is necessary to keep an eye out for the clinical and laboratory indicators of patients, rationally use glucocorticoids and immunosuppressants, improve the immunity of patients, and control the condition of SLE.

In conclusion, autoimmune disorders are mainly manifested by reduced number of various peripheral lymphocyte subsets in SLE patients. The absolute number of peripheral lymphocyte subsets in infected SLE patients was significantly reduced compared with that in uninfected patients, and the low absolute number status of these cells can be used as an indicator of a higher risk of infection in SLE patients. A risk assessment model for SLE with infection was established, p = 1/(1−e−^y), Y = 1.763-0.004 × absolute number of CD4⁺T cells −0.005 × absolute number of NK cells −0.005 × platelet count (×10¹²/L) + 1.033 × absolute number of lymphocytes (×10⁹/L) + 0.023 × C-reactive protein (mg/dL), this may be a new method to identify SLE patients with co-infection.

Footnotes

Acknowledgements

We acknowledge and appreciate our colleagues for valuable efforts and comments on this paper.

Author contributions

Study design and manuscript writing: JZ and SZ. Data extraction, quality assessment, analysis, and interpretation of data: JQ and MQ. All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be published. Xiao-Feng Li had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project was supported by the National Natural Science Foundation of China (No. 82001740) and Graduate Students Outstanding Innovation Project Foundation of Shanxi Province (2C592020079). The funding body had role in the design of the study and collection, analysis, and interpretation of data.

Data Availability

All data generated or analyzed during this study are included in this published article.

Ethics approval

This study was approved by the Ethics Committee of the Second Hospital of Shanxi Medical University (2016 KY-007).

ORCID iD

Jiaqian Zhang

References

Kaul

Gordon

Crow

, et al. Systemic lupus erythematosus. Nat Rev Dis Primers 2016; 2: 16039.

Esposito

Bosis

Semino

, et al. Infections and systemic lupus erythematosus. Eur J Clin Microbiol Infect Dis 2014; 33(9): 1467–1475.

Fors Nieves

Izmirly

. Mortality in systemic lupus erythematosus: an updated review. Curr Rheumatol Rep 2016; 18(4): 21.

Pan

Liu

Liao

, et al. Current mechanistic insights into the role of infection in systemic lupus erythematosus. Biomed Pharmacother 2019; 117: 109122.

Kedves

Kosa

Kunovszki

, et al. Large-scale mortality gap between SLE and control population is associated with increased infection-related mortality in lupus. Rheumatology (Oxford) 2020; 59(11): 3443–3451.

Bouza

Moya

Munoz

. Infections in systemic lupus erythematosus and rheumatoid arthritis. Infect Dis Clin North Am 2001; 15(2): pp. 335–361.

Rua-Figueroa

Lopez-Longo

Galindo-Izquierdo

, et al. Incidence, associated factors and clinical impact of severe infections in a large, multicentric cohort of patients with systemic lupus erythematosus. Semin Arthritis Rheum 2017; 47(1): 38–45.

Wang

Niu

Jiang

, et al. The diagnostic values of C-reactive protein and procalcitonin in identifying systemic lupus erythematosus infection and disease activity. Medicine (Baltimore) 2019; 98(33): e16798.

Jung

Suh

. Infection in systemic lupus erythematosus, similarities, and differences with lupus flare. Korean J Intern Med 2017; 32(3): 429–438.

10.

Dorner

Jacobi

Lee

, et al. Abnormalities of B cell subsets in patients with systemic lupus erythematosus. J Immunol Methods 2011; 363(2): 187–197.

11.

Becker

Dao

Han

, et al. SLE peripheral blood B cell, T cell and myeloid cell transcriptomes display unique profiles and each subset contributes to the interferon signature. PLoS One 2013; 8(6): e67003.

12.

Abud-Mendoza

Cuevas-Orta

Santillan-Guerrero

, et al. Decreased blood levels of B lymphocytes and NK cells in patients with systemic lupus erythematosus (SLE) infected with papillomavirus (HPV). Arch Dermatol Res 2013; 305(2): 117–123.

13.

Ferreira Vda

Mesquita

, et al. The effects of freeze/thawing on the function and phenotype of CD4(+) lymphocyte subsets in normal individuals and patients with systemic lupus erythematosus. Cryobiology 2015; 71(3): 507–510.

14.

Doaty

Agrawal

Bauer

, et al. Infection and lupus: which causes which. Curr Rheumatol Rep 2016; 18(3): 13.

15.

Mao

Hou

, et al. TIGIT signalling pathway negatively regulates CD4(+) T-cell responses in systemic lupus erythematosus. Immunology 2017; 151(3): 280–290.

16.

Comte

Karampetsou

Yoshida

, et al. Signaling lymphocytic activation molecule family member 7 engagement restores defective effector CD8+ T cell function in systemic lupus erythematosus. Arthritis Rheumatol 2017; 69(5): 1035–1044.

17.

Miao

Qiu

, et al. Circulating angiogenic T cells and their subpopulations in patients with systemic lupus erythematosus. Mediators Inflamm 2016; 2016: 2842143.

18.

Liu

Wang

Song

, et al. Increased circulating CD4(+)CXCR5(+)FoxP3(+) follicular regulatory T cells correlated with severity of systemic lupus erythematosus patients. Int Immunopharmacol 2018; 56: 261–268.

19.

Ichinose

Umeda

Koga

, et al. The potential role of CD4(+)CD52(lo) T-cell populations in systemic lupus erythematosus. Clin Immunol 2019; 200: 35–36.

20.

Aringer

Costenbader

Daikh

, et al. European league against rheumatism/American college of rheumatology classification criteria for systemic lupus erythematosus. Arthritis Rheumatol 2019; 71(9): 1400–1412.

21.

Alur

Mary Thabah

Sistla

, et al. Occurrence, predictors and outcome of infections at three months in hospitalized patients with SLE: a prospective study from Southern India. Lupus 2020; 29(6): 649–658.

22.

, et al. Mortality prediction in systemic lupus erythematosus patients with pulmonary infection. Int J Rheum Dis 2019; 22(6): 1077–1083.

23.

Chen

Xie

Chen

, et al. Infection in southern Chinese patients with systemic lupus erythematosus: spectrum, drug resistance, outcomes, and risk factors. J Rheumatol 2016; 43(9): 1650–1656.

24.

Fanouriakis

Kostopoulou

Alunno

, et al. Update of the EULAR recommendations for the management of systemic lupus erythematosus. Ann Rheum Dis 2019; 78(6): 736–745.

25.

Goldblatt

Chambers

Rahman

, et al. Serious infections in British patients with systemic lupus erythematosus: hospitalisations and mortality. Lupus 2009; 18(8): 682–689.

26.

Tektonidou

Wang

Dasgupta

, et al. Burden of serious infections in adults with systemic lupus erythematosus: a national population-based study, 1996–2011. Arthritis Care Res (Hoboken) 2015; 67(8): 1078–1085.

27.

Hernández-Cruz

Tapia

Villa-Romero

, et al. Risk factors associated with mortality in systemic lupus erythematosus. A case-control study in a tertiary care center in Mexico City. Clin Exp Rheumatol 2001; 19(4): 395–401.

28.

Noël

Lortholary

Casassus

, et al. Risk factors and prognostic influence of infection in a single cohort of 87 adults with systemic lupus erythematosus. Ann Rheum Dis 2001; 60(12): 1141–1144.

29.

Feldman

Hiraki

Winkelmayer

, et al. Serious infections among adult medicaid beneficiaries with systemic lupus erythematosus and lupus nephritis. Arthritis Rheumatol 2015; 67(6): 1577–1585.

30.

Kim

Jung

Suh

. Usefulness of neutrophil-to-lymphocyte ratio as a biomarker for diagnosing infections in patients with systemic lupus erythematosus. Clin Rheumatol 2017; 36(11): 2479–2485.

31.

Xiao

Zhang

. Application of procalcitonin, white blood cell count and neutrophil-to-lymphocyte ratio in the diagnosis of systemic lupus erythematosus with a bacterial infection. Ann Palliat Med 2020; 9(6): 3870–3876.

32.

Makiyama

Chiba

Noto

, et al. Expanded circulating peripheral helper T cells in systemic lupus erythematosus: association with disease activity and B cell differentiation. Rheumatology (Oxford) 2019; 58(10): 1861–1869.

33.

Kitagishi

Kobayashi

Yamashina

, et al. Elucidating the regulation of T cell subsets (review). Int J Mol Med 2012; 30(6): 1255–1260.

34.

Qin

Qiu

Xie

, et al. [Opportunistic infection in systemic lupus erythematosus patients: the disease spectrum and the characteristics of peripheral lymphocyte subsets]. Zhonghua Nei Ke Za Zhi 2018; 57(1): 32–36.

35.

Qin

Qiu

Hsieh

, et al. Association between lymphocyte subsets and cytomegalovirus infection status among patients with systemic lupus erythematosus: a pilot study. Medicine (Baltimore) 2019; 98(39): e16997.

36.

Pego-Reigosa

Nicholson

Pooley

, et al. The risk of infections in adult patients with systemic lupus erythematosus: systematic review and meta-analysis. Rheumatology (Oxford) 2021; 60(1): 60–72.

Risk factors analysis and risk assessment model construction of systemic lupus erythematosus patients with infection

Abstract

Objective

Methods

Results

Conclusion

Keywords

Introduction

Methods

Recruitment of participants

Infection defined

Research methods

Flow cytometric analysis

Materials

Absolute lymphocyte count

Analysis of lymphocyte surface and intracellular markers

Analysis of Th1/Th2/Th17 cells

Analysis of CD4+CD25+Foxp3+Treg cells

Statistical analyses

Results

Demographic and clinical characteristics of SLE patients in the non-infected and infected groups

Comparison of peripheral lymphocyte subsets in the SLE infected group, non-infected group, and healthy control group

Correlation analysis between peripheral lymphocyte subsets count and disease activity in SLE-infected patients

Prediction of peripheral lymphocytes in patients with SLE infection

Multivariate logistic regression analysis of SLE patients with infection

Build a risk assessment model

Evaluation of predictive value of regression model

Discussion

Footnotes

Acknowledgements

Author contributions

Declaration of conflicting interests

Funding

Data Availability

Ethics approval

ORCID iD

References

Analysis of CD4⁺CD25⁺Foxp3⁺Treg cells