A Novel Model for Predicting the Probability of Anemia of Chronic Disease in Patients with Systemic Lupus Erythematosus

doi:10.21203/rs.3.rs-3230069/v1

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A Novel Model for Predicting the Probability of Anemia of Chronic Disease in Patients with Systemic Lupus Erythematosus

https://doi.org/10.21203/rs.3.rs-3230069/v1

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Background: ACD is the most common hematological manifestation of systemic lupus erythematosus (SLE). As well as causing damage to vital organs, ACD suppresses the immune system and causes infections, which ultimately affects the prognosis of SLE. Additionally, anemia may worsen if the recommended treatments for SLE are overused, including hydroxychloroquine and immunosuppressants. We constructed a model based on commonly measured laboratory parameters in order to predict the likelihood of ACD in SLE patients.

Methods: Patients with a confirmed SLE diagnosis and a disease duration of at least one year were eligible for inclusion. Randomly dividing patients into a training set and a test set with an 8:2 ratio. Predictive models were created in the training set using logistic regression with the least absolute shrinkage and selection operator (LASSO)-selected predictors. A nomogram was also constructed based on the prediction model. In addition, the efficacy of the model was evaluated using receiver operating curves (ROC), calibration curves, and decision curve analysis (DCA).

Results: The study included 216 patients, of whom 172 were randomly assigned to the training set and 44 to the test set. The area under the curve (AUC) value of the prediction model was 0.833 (95% CI, 0.773-0.892) in the training set and 0.861 (95% CI, 0.750-0.972) in the test set. Consistency between expected probability and observed probability was shown by the calibration curves. On the DCA curve, the model generated a net profit when the threshold likelihood in the training set is between 20% and 90% and the threshold likelihood in the test set is between 10% and 80%.

Conclusion: We developed a predictive model capable of predicting the risk of ACD in SLE patients. With the aid of this model, clinicians can estimate the likelihood of ACD in SLE patients and make the necessary adjustments to therapy to prevent additional harm.

Systemic lupus erythematosus

anemia

Anemia of chronic disease

Nomogram

Predict model

Systemic lupus erythematosus (SLE) is a multisystem autoimmune disease that predominantly affects middle-aged women. A prevalent symptom of SLE is hematologic involvement, including anemia, leukopenia, and thrombocytopenia. Anemia is the most common hematologic complication of lupus, mainly including anemia of chronic disease (ACD), autoimmune hemolytic anemia (AIHA), aplastic anemia, and iron deficiency anemia(IDA) [1]. According to the 2019 European League Against Rheumatism/American College of Rheumatology (EULAR) SLE classification criteria, AIHA is a diagnostic criterion for SLE༻2༽. Severe and persistent anemia may be the result of AIHA, which can sometimes be the primary or initial symptom of SLE༻3༽. However, the prevalence of AIHA in patients with SLE is relatively low, at approximately 6% ༻4༽.

In comparison to AIHA, ACD is the most prevalent form of anemia among patients with SLE. ACD is characterized by a slight decrease in reticulocytes and a mild-to-moderate decrease in hemoglobin in patients with chronic kidney disease, infections, autoimmune diseases, and malignancy [4,5]. Several theories have been proposed to explain the etiology of ACD, primarily including iron homeostasis, impaired proliferation of erythroid progenitor cells, and a decrease in erythropoietin output༻6༽. It should be noted that ACD is an exclusionary diagnosis since it must be ruled out diseases such as megaloblastic anemia, IDA, AIHA, and hemorrhage in order to establish a diagnosis.

Pallor, fatigue, and diminished activity tolerance are common symptoms of anemia. Throughout the long-term course of ACD, patients can tolerate modest-to-moderate anemia without manifesting evident clinical symptoms. Despite this, anemia causes tissue hypoxia that leads to vasodilation and a drop in blood pressure. In order to maintain normal blood pressure, the body activates the renin-angiotensin-aldosterone system, leading to tachycardia and fluid retention. As a result, cardiac workload increases and glomerular filtration rate decreases, resulting in a decline in cardiac and renal function [7]. Thus, anemia has been identified as a leading contributor to cardiovascular morbidity and mortality༻8༽. Since the kidney is responsible for producing erythropoietin (EPO), impaired renal function can lead to a decrease in EPO secretion, which in turn contributes to anemia༻9༽. Correcting anemia can enhance the prognosis of chronic renal failure patients༻10༽. Besides negatively impacting important organs, persistent anemia can weaken the patient's immunity and cause acute bacterial infections༻11༽. As an autoimmune disease, SLE causes immune disorders in patients, increasing the risk of infection, which is considered to be one of the primary causes of mortality in SLE༻12,13༽. Therefore, treating anemia may improve the prognosis of patients with SLE by reducing infections. To minimize the risk of ACD, it is essential to keep a close eye on the patient's ACD risk and to adjust the treatment regimen as needed.

Currently, iron supplementation and erythropoiesis-stimulating agents are effective in alleviating symptoms of ACD. In cases of hemoglobin levels below 80 g/L, transfusion of red blood cells should also be considered as an emergency treatment [6]. Though focusing on the underlying disease is the most effective treatment for ACD, early diagnosis of ACD is advantageous for clinical decision-making and prompt treatment.

In light of these findings, a predictive model was developed for ACD in patients with SLE.

Patients

The patient data used in this retrospective analysis were gathered from patients hospitalized with SLE between 2021-1-1 and 2023-3-1 (at Wuhan Union Hospital in Wuhan, China). Inclusion criteria for SLE patients include the following: 1. Clinical symptoms and laboratory findings of patients were scored according to the 2019 EULAR SLE classification guidelines with a score of 10 or higher [9]. 2. The SLE condition has been present for more than one year. Moreover, ACD is diagnosed by the following criteria: 1. Patients who have two consecutive blood tests below normal standards within three months (below 110 g/L for women and below 120 g/L for men). 2. Patients did not exhibit bleeding symptoms and had no previous history of hemorrhage. 3. There were no other signs of blood disorders (vitamin B12 deficiency, folic acid deficiency, thalassemia, AIHA, IDA) in these patients. 4. Neither the radiation, nor the chemicals were responsible for the anemia exhibited by the patients. In the end, 216 patients were enrolled in this study.

The retrospective study protocol was approved by the ethics committee of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology.

Statistical analysis

A total of 25 variables were included in this study, including gender, age, platelets(PLT), white blood cells(WBC), erythrocyte sedimentation rate (ESR), complement 3 (C3), complement 4 (C4), anti-double-stranded DNA antibodies (Anti-dsDNA Abs), alkaline phosphatase (ALP), aspartate transaminase(AST), alanine transaminase(ALT), creatinine, glomerular filtration rate (GFR), uric acid, tumor necrosis factor-alpha (TNF-α), interferon-gamma (IFN-γ), interleukin 6 (IL-6), interleukin 4 (IL-4), interleukin 2 (IL-2), interleukin 10 (IL-10), SLE Disease Activity Index (SLEDAI), SLEDAI grade, urine protein level, urine occult blood level, urine leukocyte level. SLEDAI and SLEDAI grades were evaluated based on patient symptoms and laboratory findings using the SLEDAI-2000.

The continuous variables included in this study are presented as means ± standard deviation (SD), while the variables not following a normal distribution are presented as medians and quartiles. The categorical variables were illustrated using percentages, and a differential analysis between the two groups was conducted on each variable. The least absolute shrinkage and selection operator (LASSO) regression was applied to select variables that statistically differed significantly (p < 0.05) between ACD and non-ACD groups. The patients were randomly divided into training and test sets at an 8:2 ratio. A multivariate logistic regression model based on lasso-selected predictors was constructed in the training set. For both training and testing sets, receiver operating curves (ROCs) were used to evaluate the predictability of the model. Bootstrapping was employed for internal validation (resampling = 1000). Using the calibration curves and the Homer-Lemeshow test to estimate the calibration of the model. For the purpose of assessing the clinical utility of the solution, a decision curve analysis (DCA) was conducted. Data analysis was conducted using R software (version 4.3.0) and SPSS Statistics (version 27.0.1).

As shown in Table 1, the baseline patient information is presented. It was found that there were statistically significant differences in PLT, WBC, ESR, C3, C4, creatinine, GFR, IL-6, IL-4, IL-10, SLEDAI, and SLEDAI grade between groups with and without ACD (p < 0.05). As a result of entering these variables into LASSO, six predictor variables (PLT, C3, ESR, IL-4, IL-10, and GFR) were identified (Fig. 1).

Table 1
Characteristic	SLE patient (n = 216)			P-value
Characteristic	Total	Non-ACD (n = 117)	ACD (n = 99)	P-value
Gender, n (%)				0.824
Female	177 (82)	97 (83)	80 (81)
Male	39 (18)	20 (17)	19 (19)
Age, Median (Q1, Q3)	39 (31.75, 50)	36 (31, 49)	40 (32, 51)	0.342
PLT, Mean ± SD	188.05 ± 73.34	205.65 ± 68.25	167.25 ± 74.02	< 0.001
WBC, Median (Q1, Q3)	5.48 (4.01, 7.38)	5.97 (4.71, 7.72)	4.84 (3.3, 6.6)	< 0.001
ESR, Median (Q1, Q3)	11 (7, 21)	8 (5, 14)	16 (7, 33.5)	< 0.001
C3, Mean ± SD	0.66 ± 0.21	0.74 ± 0.2	0.58 ± 0.18	< 0.001
C4, Median (Q1, Q3)	0.15 (0.11, 0.2)	0.15 (0.12, 0.2)	0.14 (0.1, 0.17)	0.002
Anti-dsDNA antibody, Median (Q1, Q3)	2 (0, 15)	2 (0, 15)	2 (0, 15)	0.951
ALT, Median (Q1, Q3)	17.5 (13, 26.5)	20 (13, 28)	16 (12, 25)	0.134
AST, Median (Q1, Q3)	19 (15, 25)	19 (16, 23)	20 (15, 27)	0.106
ALP, Median (Q1, Q3)	51 (38, 63.25)	52 (38, 63)	51 (38, 63.5)	0.752
Creatinine, Median (Q1, Q3)	57.8 (50.38, 70.72)	55.5 (46.9, 63.6)	62.3 (51.7, 86.75)	< 0.001
Uric acid, Median (Q1, Q3)	302.7 (237.02, 368.88)	301.9 (233, 358.8)	303.9 (243.4, 376.1)	0.41
GFR, Median (Q1, Q3)	113.2 (94.4, 122.63)	115.85 (103.1, 123.36)	105.49 (76.1, 120.28)	< 0.001
TNF-α, Median (Q1, Q3)	3.59 (1.82, 16.42)	3.45 (1.77, 16.81)	4.57 (2.14, 15.43)	0.703
IFN-γ, Median (Q1,Q3)	1.83 (1.39, 2.3)	1.71 (1.4, 2.13)	1.88 (1.36, 2.4)	0.144
IL-6, Median (Q1, Q3)	6.02 (3.12, 13.2)	4.46 (2.9, 10.68)	7.52 (3.98, 14.14)	0.012
IL-4, Median (Q1, Q3)	1.69 (1.35, 2.19)	1.67 (1.29, 1.98)	1.74 (1.4, 2.38)	0.03
IL-2, Median (Q1, Q3)	1.77 (1.41, 2.22)	1.7 (1.47, 2)	1.8 (1.4, 2.39)	0.119
IL-10, Median (Q1, Q3)	3.48 (2.7, 4.96)	3.02 (2.5, 3.84)	4.84 (3.18, 6.11)	< 0.001
SLEDAI, Median (Q1, Q3)	8 (4, 12)	7 (3, 11)	10 (5.5, 14)	0.003
SLEDAI grade, n (%)				0.012
Disease inactivity	64 (30)	45 (38)	19 (19)
Mild disease activity	54 (25)	29 (25)	25 (25)
Medium disease activity	62 (29)	28 (24)	34 (34)
High disease activity	36 (17)	15 (13)	21 (21)
Urine protein, n (%)				0.174
-	145 (67)	85 (73)	60 (61)
+	35 (16)	17 (15)	18 (18)
++	33 (15)	13 (11)	20 (20)
+++	3 (1)	2 (2)	1 (1)
Urine leukocytes, n (%)				0.501
-	190 (88)	103 (88)	87 (88)
+	10 (5)	7 (6)	3 (3)
++	4 (2)	1 (1)	3 (3)
+++	12 (6)	6 (5)	6 (6)
Urine occult blood, n (%)				0.148
-	153 (71)	86 (74)	67 (68)
+	25 (12)	14 (12)	11 (11)
++	18 (8)	11 (9)	7 (7)
+++	20 (9)	6 (5)	14 (14)

On the variables selected by LASSO as predictors, univariate logistic regressions were conducted (Fig. 2a, Supplementary Table 1). Furthermore, AUC values were calculated for each univariate with statistically significant differences between the ACD and non-ACD groups (Fig. 2b). On the basis of these six LASSO-selected variables, a multivariate logistic regression model was developed (Fig. 2c, Supplementary Table 2). The nomogram is illustrated in Fig. 2d. For the training set (Fig. 3a), the AUC was 0.83 (95% CI, 0.77–0.897) and for the test set (Fig. 3b), it was 0.849 (95% CI, 0.755–0.944), and the Delong test indicated no statistical significance between the them (Fig. 3c). Compared to a univariate model, this multivariate model performs better. From the calibration curves, it is evident that the calibration of the model is reliable on both the training and test sets (Fig. 4a and b). As indicated by the decision analysis curve, the model is capable of achieving net profits within the high-risk threshold range of 20% and 90% for the training set, and within the high-risk threshold range of 10% and 80% for the test set (Fig. 4c and d).

After lasso screening, IL-10, ESR, C3, IL-4, GFR, and PLT were found to be predictive factors for ACD in patients with SLE. All of these factors appear to be interconnected with the pathogenesis of ACD.

In patients with SLE, IL-10 is primarily produced by B cells through autocrine secretion and by macrophages through paracrine secretion. There is a positive correlation between IL-10 titers and SLEDAI and anti-dsDNA antibody titers in the serum of patients with SLE［14,15］. IL-10 secretion is also associated with tissue injury caused by lupus inflammation［14］. A key role played by IL-10 in humoral immunity is to stimulate the production of antibodies by activated B cells［16］. Meanwhile, IL-10 exerts an anti-inflammatory effect by reducing the production of anti-inflammatory factors and free radicals ［17］. In inflammatory bowel disease, patients who received IL-10 treatment developed anemia, which gradually improved without further clinical intervention following the discontinuation of IL-10 treatment. Ferritin concentrations increased dose-dependently in patients treated with IL-10, suggesting that IL-10 may induce anemia by stimulating ferritin production［18］. In previous studies, it was found that IL-10 promotes heme catabolism by inducing the expression of heme oxygenase-1 (HO-1) via the p38 mitogen-activated protein kinase pathway［19］. Furthermore, IL-10 inhibits erythroid progenitor cells directly［20］. The Stat3-IL-10-IL-6 pathway has been found to promote macrophage cytokinesis and phenotypic transformation in liver damage conditions［21］. ACD patients had higher levels of IL-10 and IL-6 serum titers than their non-ACD counterparts. Although IL-6 was eventually omitted from the model, it is undeniable that it also plays a crucial role in the mechanism of ACD. IL-6 stimulates hepatocytes to produce hepcidin under inflammatory conditions. Specifically, hepcidin acts on phagocytes in the intestinal epithelium, liver, and reticuloendothelial cells, anchoring ferroprotein (FP1) and disassembling it. Iron is transported from macrophages to plasma by FP1. The decomposition of FP1 by hepcidin results in a lower level of plasma iron, which inhibits the production of hemoglobin［22］. Our hypothesis is that, during the inflammatory phase of SLE, IL-10 induces IL-6 production through the stat3-IL-10-IL-6 pathway, thus aggravating ACD. It has been demonstrated that anti-IL-10 therapy is an effective treatment for SLE. In mouse models, anti-IL-10 antibodies have been shown to reduce kidney damage［23］. Patients with SLE treated with anti-IL-10 antibodies showed significant improvements in their skin and joint symptoms［24］.

Similar to IL-10, IL-4 facilitates the proliferation of activated B cells and promotes their transformation into plasma cells, hence being called the B cell stimulating factor［25］. Through its interaction with receptors, IL-4 activates tyrosine kinases. Both myeloid and erythroid cells express IL-4 receptors［26］. However, the function of IL-4 in normal erythropoiesis remains controversial. A study conducted by I GREEN et al.[27] concluded that IL-4 and EPO were synergistic in stimulating the production of erythroid progenitor cells［27］. As reported in another study, IL-4 inhibited IL-3-supported erythroid burst forming units, while having little or no effect on EPO-supported erythrogenic burst units［28］. In this regard, IL-4 may have differential effects on erythropoiesis in different environments in the body. There is an increase in IL-4 production when chronic inflammation is exacerbated in SLE ［29］.Upon analyzing the data, we found that the SLEDAI score was higher in the ACD group than in the non-ACD group, and the IL-4 level was also higher. The increased levels of IL-4 during SLE active periods could have intensified the inhibitory effect on erythroid progenitor cells.

In the body, the complement system is instrumental in eliminating pathogens, apoptotic cells, and cellular debris. A deficiency in complement impairs the body's ability to eliminate antigens, leading to the accumulation of autoantibodies and ultimately contributing to the onset of SLE ［30］. A decrease in C3 and/or C4 was included as an indicator of SLE activity in SLEDAI-2000. As shown in Table 1, C3 and C4 decreased more in the ACD group than in the non-ACD group, but C3 decreased significantly more. During complement activation, C4 is only involved in the classical pathway, whereas C3 additionally participates in the bypass pathway and the lectin-activated pathway. Upon recognizing C3 fragments on the surface of apoptotic cells, phagocytes bind and phagocytose them. In the case of abnormally activated C3, erythrocytes are destroyed and extravascular hemolysis is induced, resulting in paroxysmal hemoglobinuria［31］. Presently, anti-C3 medications are utilized in the treatment of paroxysmal hemoglobinuria［32］.

The ESR is a commonly used indicator of inflammation as well as an effective predictor of disease progression. It is elevated in acute inflammatory disorders, connective tissue diseases, severe anemia, and malignancies while decreasing in erythrocytosis and dehydration. In spite of not being included in the SLEDAI as an indicator of lupus activity, an elevated ESR is still considered as a criterion for lupus activity in the European Consensus Lupus Activity Measure (ECLAM), SLE Index Score (SIS). Plasma fibrinogen concentration and the number and morphology of erythrocytes affect ESR［33］. Even though fibrinogen increases during the active phase of SLE, this elevation is relatively moderate for individuals with SLE, where anemia is a common symptom. Therefore, ESR may represent the erythrocyte state of SLE patients to some extent［34］.

In general, PLT values range between 100 and 300 grams per liter. The PLT levels of the majority of patients in the ACD group were within the normal range, but they were still lower than those in the non-ACD group. As PLT aid in blood coagulation, the relatively low concentration of PLT in the ACD group increases the risk of bleeding, contributing to anemia.

GFR and uric acid can both be utilized to evaluate renal function. Approximately 50% of SLE patients suffer from variable degrees of kidney injury, resulting in impaired renal function［35］. Compared to the non-ACD group, the GFR of the ACD group was lower and the uric acid level was higher, indicating a relatively impaired renal function. It is critical to note that the kidney is the major organ for EPO secretion and that decreased renal function will result in decreased EPO production, which will culminate in a decrease in erythropoiesis［9］. Meanwhile, anti-EPO antibodies are detectable in patients with SLE and serum EPO levels are lower than normal ［36］. SLE patients with ACD are more likely to have anti-EPO antibodies, leading to insensitivity to EPO［37］.

To date, regarding medications for SLE, hydroxychloroquine is advised for people without contraindications, while immune suppressants are recommended for those who are suffering flare-ups［38］. Hydroxychloroquine has been shown to improve dermatological symptoms and immunosuppressants are effective in treating refractory SLE and reducing glucocorticoids ［39］. However, SLE patients are prone to ACD, which may be exacerbated by the overuse of antimalarials and immunosuppressants ［40］. This model is clinically useful since it is able to predict the risk of ACD among patients, thus assisting in the prevention of further hematological harm caused by medication usage.

There are still some limitations. The first limitation of this study is that it only included patients from one hospital. A multicenter, multiethnic, and larger clinical cohort is required in the future in order to improve the accuracy of the model. Furthermore, additional variables such as serum EPO titer, anti-EPO antibody test, and renal pathology should be included as conditions permit.

In conclusion, this study developed an effective model for predicting the risk of ACD in patients with SLE. IL-10, ESR, C3, IL-4, GFR, and PLT have been identified as reliable predictors of ACD in patients with SLE. By paying close attention to these indicators, clinicians may be able to predict the likelihood of ACD, administer timely treatment, and modify the patient's medication regimen to prevent further hematological harm.

SLE: systemic lupus erythematosus; ACD: anemia of chronic disease; AIHA: autoimmune hemolytic anemia; IDA: iron deficiency anemia; EULAR: European League Against Rheumatism/American College of Rheumatology; PLT: platelets; WBC: white blood cells; ESR: erythrocyte sedimentation rate ; C3: complement 3; C4: complement 4; Anti-dsDNA Abs: anti-double-stranded DNA antibodies; ALP: alkaline phosphatase; AST: alkaline phosphatase; ALT: alanine transaminase; GFR: glomerular filtration rate; TNF-α: tumor necrosis factor-alpha; IFN-γ: interferon-gamma; IL-6: interleukin 6; IL-4: interleukin 4; IL-2: interleukin 2; IL-10: interleukin 10; SLEDAI: SLE Disease Activity Index; LASSO: the least absolute shrinkage and selection operator; DCA: decision curve analysis; ECLAM: European Consensus Lupus Activity Measure; SIS: SLE Index Score

Ethical approval: The retrospective study protocol was approved by the ethics committee of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology.

Consent for publication: Not applicable.

Availability of data and materials: The data that support the findings of this study are available from Wuhan union hospital but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Wuhan union hospital.

Competing interests: The authors declare no competing interests.

Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Authors’ contributions: Zhan Jinshan designed the research and drafted the initial article copy. Huang Changzheng and Zhang Jing have refined the research proposal, while Chen Fangqi revised the initial draft of the article. The remaining authors conducted data collection. All authors have read and approved the final manuscript.

Acknowledgments: None.

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A Novel Model for Predicting the Probability of Anemia of Chronic Disease in Patients with Systemic Lupus Erythematosus

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