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Abstract

Objective

To investigate the role of albumin-to-globulin ratio (AGR) in systemic lupus erythematosus (SLE) and its relationship with disease activity.

Methods

This retrospective study consecutively selected patients with SLE and healthy controls. Patients were divided into three groups according to the SLE Disease Activity Index 2000 (SLEDAI-2K): group 1 (mild disease activity, SLEDAI-2K ≤ 6), group 2 (moderate disease activity, SLEDAI-2K 7–12) and group 3 (severe disease activity, SLEDAI-2K > 12). Predictors of SLE disease activity were analysed by ordinal logistical regression.

Results

A total of 101 Chinese patients with SLE and 75 healthy Chinese controls were included. Patients with SLE had lower AGR values than healthy individuals, and group 3 patients with SLE displayed lower AGR values than those in group 1, but similar values to group 2. AGR was inversely correlated with SLEDAI-2K (r = −0.543). Ordinal logistic regression analysis showed that lower AGR (β = −1.319) and lower complement C4 (β = −1.073) were independent risk factors for SLE disease activity.

Conclusions

AGR was decreased in patients with SLE and may be utilized as a useful inflammatory biomarker for monitoring SLE disease activity.

Keywords

Systemic lupus erythematosus albumin to globulin ratio disease activity complement C4 independent risk factors inflammatory biomarker

Introduction

Systemic lupus erythematosus (SLE) is a systemic inflammatory autoimmune disease that involves multiple organs and displays variegated clinical manifestations. The global incidence of SLE has been reported to range from 1.5 to 11 per 100 000 person-years, and the global prevalence ranges between 13 and 7713.5 per 100 000 individuals.¹ The remarkable variation in SLE burden is partly due to the diverse population structure, whereas mortality among patients with SLE is consistently high, and has been verified to be 2–3 times higher than in healthy individuals.¹ Early diagnosis and persistent stable state are crucial for the prognosis of patients with SLE. In the new European League Against Rheumatism (EULAR)/American College of Rheumatology (ACR) 2019 classification criteria, autoantibodies and complement proteins are included, which emphasizes the importance of serological markers in the diagnosis of SLE.² However, frequently testing for autoantibodies is impractical and costly, and physicians, including rheumatologists, always require more evidence, beside complement proteins, to evaluate SLE disease activity. Inexpensive, readily available and highly specific inflammatory indicators need to be discovered for monitoring disease activity of SLE.

Studies have been conducted to explore new inflammatory markers for SLE, such as neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR).^3–6 NLR may also be used to monitor renal injury in patients with SLE.^7,8 Similar to routine blood analysis, albumin and globulin are two indexes that can be easily obtained during routine tests. As a major protein in human serum, albumin is generally used as an objective reflection of nutritional status and systemic inflammatory condition. Previous studies have shown that hypoalbuminemia is correlated with active SLE and can predict the risk of developing lupus nephritis.^9,10 Globulin is another crucial serum protein, which can bind with cortisol and is involved in immunity and inflammation.^11,12 The albumin-to-globulin ratio (AGR) has recently been verified as an independent adverse prognostic biomarker in diverse human cancers, such as gastric cancer,¹³ diffuse large B cell lymphoma,¹⁴ and non-small cell lung cancer.¹⁵ Low pretreatment AGR is correlated with poorer overall survival in these tumours.^13–15 In addition, AGR at diagnosis was reported to be inversely related to the prognosis of patients with microscopic polyangiitis.¹⁶ Two retrospective studies, conducted in Korea and China, found that newly diagnosed SLE with a low AGR was a strong risk factor for future development of lupus nephritis.^17,18 As lupus nephritis is a major complication of SLE, the role of AGR in SLE and its correlation with disease severity in patients with SLE warrants further exploration. Thus, the aim of the present study was to investigate the role of AGR in SLE and its relationship with disease activity.

Patients and methods

Study population

This retrospective study included consecutive patients with SLE who were hospitalized in the Department of Rheumatology and Immunology at Guangdong Second Provincial General Hospital between January 2017 and November 2021. All patients with SLE met the 1997 updated revised ACR criteria.¹⁹ Patients with any of the following features were excluded from the study: age <18 years, pregnancy, active infection, malignancy, haematologic disease, other autoimmune disease, abnormal hepatic function, diabetes mellitus. Age- and sex-matched healthy individuals, who attended the Department of Physical Examination Centre, Guangdong Second Provincial General Hospital for a routine check-up, were included as healthy controls. The study was reviewed and approved by the research ethics committee of Guangdong Second Provincial General Hospital (2022-KY-KZ-001-01). All patient details were deidentified prior to analysis and written informed consent was obtained from all study participants. The study was designed in compliance with the Declaration of Helsinki as revised in 2013, and the reporting of this study conforms to STROBE guidelines.²⁰

Disease activity index

Every patient with suspected SLE was evaluated by the SLE Disease Activity Index 2000 (SLEDAI-2K) system once they were admitted to hospital for the first 1–2 days.²¹ For the present analysis, patients with SLE were divided into 3 groups according to the 2020 Chinese Guidelines for the Diagnosis and Treatment of Systemic Lupus Erythematosus:²² group 1, mild activity (SLEDAI-2K ≤ 6); group 2, moderate activity (SLEDAI-2K 7–12); and group 3, severe activity (SLEDAI-2K > 12).

Clinical assessment and laboratory data

Clinical characteristics and laboratory examinations were recorded for each patient. Age, sex, disease duration, smoking and alcohol consumption habits, number of white blood cells (WBCs), lymphocytes, neutrophils, monocytes, platelets, red blood cell distribution width (RDW), C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), albumin, globulin, serum total protein, urea, serum creatinine, complement C3 and C4, antinuclear antibody, anti-dsDNA antibody (Ab), anti-Sm Ab, anti-Sjogren syndrome A antigen (anti-SSA) Ab, anti-Ro52 Ab, anti-Sjogren syndrome B antigen (anti-SSB) Ab, anti-nuclear ribonucleoprotein (anti-nRNP) Ab, and anti-ribosomal P protein Ab, were included. In addition, NLR, PLR, monocyte-to-lymphocyte ratio (MLR) and AGR were calculated. Lupus nephritis was defined as clinical and laboratory manifestations that satisfy the ACR criteria published in 2012.²³ The affected systems, or complications, such as neuropsychiatric SLE, secondary antiphospholipid syndrome and haematological involvement, were also recorded.

Statistical analyses

Continuous variables are presented as mean ± SD and categorical variables are presented as n (%) prevalence. Independent-samples t-test or Mann–Whitney U-test was conducted to assess differences between continuous variables, in addition to analysis of variance (ANOVA) or Kruskal–Wallis test. Differences between categorical variables were assessed with χ²-test or Fisher’s exact test. Associations between variables were evaluated by Spearman’s rank correlation coefficient. Laboratory parameters that displayed statistically significant differences between the SLEDAI-2K subgroups were further analysed using a generalized ordered logistic model to verify factors associated with SLE disease activity. All statistical analyses were performed using SPSS software, version 20.0 (IBM Corp., Armonk, NY, USA) and a P value < 0.05 was considered statistically significant.

Results

Basic study population characteristics

A total of 101 Chinese patients with SLE and 75 age- and sex-matched healthy Chinese controls were included in the study. Analyses of the demographic and laboratory indexes between patients with SLE and healthy controls (Table 1) revealed no statistically significant differences in age, sex, neutrophil count, or monocyte count (all P > 0.05). Compared with healthy controls, patients with SLE had lower levels of WBCs, lymphocytes, platelets, total protein, albumin and AGR, but higher levels of RDW, globulin, NLR, PLR, MLR and CRP (all P < 0.05).

Table 1.

Comparison of demographic and laboratory data between healthy controls and patients with SLE.

	Study group
Characteristic	Controln = 75	SLEn = 101	Statistical significance^a
Age (years)	34.75 ± 6.74	34.01 ± 11.60	NS
Sex (male/female)	17/58	13/88	NS
WBCs (×10⁹/L)	6.06 ± 1.59	5.77 ± 2.87	P = 0.042
Neutrophils (×10⁹/L)	3.60 ± 1.13	4.16 ± 2.38	NS
Lymphocytes (×10⁹/L)	1.92 ± 0.58	1.19 ± 0.78	P < 0.001
Monocytes (×10⁹/L)	0.35 ± 0.14	0.37 ± 0.24	NS
Platelets (×10⁹/L)	244.43 ± 47.95	202.05 ± 97.44	P = 0.003
RDW (%)	13.00 ± 1.00	14.40 ± 3.09	P < 0.001
Total protein (g/L)	74.72 ± 4.48	69.33 ± 12.34	P = 0.002
Albumin (g/L)	44.53 ± 3.84	35.53 ± 8.65	P < 0.001
Globulin (g/L)	30.18 ± 5.06	33.81 ± 8.11	P = 0.004
AGR	1.53 ± 0.36	1.10 ± 0.36	P < 0.001
NLR	1.98 ± 0.73	5.01 ± 6.75	P < 0.001
PLR	136.21 ± 40.87	235.62 ± 195.90	P < 0.001
MLR	0.19 ± 0.08	0.41 ± 0.72	P < 0.001
CRP (mg/L)	5.17 ± 3.27	8.25 ± 17.10	P < 0.001

Data presented as mean ± SD or n prevalence.

SLE, systemic lupus erythematosus; WBCs, white blood cells; RDW, red cell distribution width; AGR, albumin-to-globulin ratio; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; MLR, monocyte-to-lymphocyte ratio; CRP, C-reactive protein.

Independent-samples t-test or Mann–Whitney U-test for continuous variables, and χ²-test or Fisher’s exact test for categorical variables.

NS, no statistically significant between-group difference (P > 0.05).

Comparison among SLE subgroups

The characteristics of patients with SLE, grouped into different disease activity levels according to SLEDAI-2K, are shown in Table 2. Similar levels of age, sex, smoking, drinking, disease duration, WBCs, neutrophils, monocytes, platelets, RDW, urea, serum creatinine, globulin, CRP, ESR were noted among the three SLEDAI-2K groups (all P > 0.05). In addition, the SLE subgroups displayed no statistically significant differences in the frequency of secondary antiphospholipid syndrome, hematologic involved, positive antinuclear antibody, positive anti-Sm Ab, positive anti-SSA Ab, positive anti-Ro52 Ab, positive anti-nRNP Ab and positive anti-ribosomal P protein Ab (all P > 0.05). Lymphocytes, total protein, albumin, AGR, C3, C4 and the presence of anti-SSB Ab were significantly decreased in group 3 compared with group 1 (all P < 0.05), but similar to group 2 (all P > 0.05). Moreover, NLR, PLR, MLR, the frequency of SLE with lupus nephritis and neuropsychiatric SLE, and positivity for anti-dsDNA Ab were significantly increased in group 3 compared with group 1 (all P < 0.05), while similar to group 2 (all P > 0.05). In supplementary analyses of patients with SLE with lupus nephritis versus those with SLE without lupus nephritis (Supplementary Tables 1 and 2), AGR was found to be lower in patients with lupus nephritis versus those without lupus nephritis (P < 0.05). Further multivariate analysis showed that total protein, serum creatinine, AGR and anti-Ro52 Ab were all significantly correlated with the presence of both SLE and lupus nephritis (all P < 0.05).

Table 2.

Comparison of demographic and clinical characteristics between patients with SLE grouped according to SLEDAI-2K scores.

	Study subgroup
Characteristic	Group 1SLEDAI-2K ≤ 6n = 46	Group 2SLEDAI-2K 7–12n = 33	Group 3SLEDAI-2K > 12n = 22	Statistical significance^a
Age (years)	32.80 ± 12.55	34.88 ± 10.11	35.23 ± 11.88	NS
Sex (male/female)	4/42	4/29	5/17	NS
Smoking (yes)	1 (2.17)	1 (3.03)	0	NS
Drinking (yes)	0	0	0	–
Disease duration (years)	5.06 ± 7.57	4.25 ± 4.54	3.23 ± 3.11	NS
WBCs (×10⁹/L)	6.22 ± 2.83	5.41 ± 2.71	5.34 ± 3.15	NS
Neutrophils (×10⁹/L)	4.23 ± 2.22	4.04 ± 2.40	4.19 ± 2.75	NS
Lymphocytes (×10⁹/L)	1.52 ± 0.93	1.02 ± 0.50	0.74 ± 0.39	P < 0.001
Monocytes (×10⁹/L)	0.41 ± 0.22	0.29 ± 0.19	0.39 ± 0.33	NS
Platelets (×10⁹/L)	207.39 ± 98.33	204.06 ± 94.89	197.05 ± 103.41	NS
RDW (%)	13.90 ± 3.01	15.18 ± 3.39	14.27 ± 2.62	NS
Urea (mmol/L)	4.70 ± 1.63	6.02 ± 5.90	5.75 ± 3.41	NS
Serum creatinine (µmol/L)	80.07 ± 26.39	90.36 ± 73.56	81.18 ± 35.57	NS
Total protein (g/L)	73.78 ± 10.24	69.46 ± 10.92	59.84 ± 13.47	P < 0.001
Albumin (g/L)	40.28 ± 6.27	33.54 ± 7.75	28.57 ± 8.63	P < 0.001
Globulin (g/L)	33.50 ± 7.82	35.92 ± 8.27	21.27 ± 8.00	NS
AGR	1.27 ± 0.35	0.98 ± 0.29	0.92 ± 0.33	P < 0.001
NLR	3.55 ± 2.32	4.71 ± 3.02	8.50 ± 12.18	P = 0.009
PLR	192.76 ± 187.26	236.85 ± 146.28	323.40 ± 250.79	P = 0.008
MLR	0.21 ± 0.17	0.31 ± 0.20	0.76 ± 1.49	P = 0.025
CRP (mg/L)	6.10 ± 11.20	11.49 ± 24.62	7.90 ± 12.47	NS
ESR (mm/h)	38.55 ± 38.05	45.07 ± 34.80	48.93 ± 40.85	NS
C3 (g/L)	0.82 ± 0.26	0.64 ± 0.28	0.54 ± 0.30	P < 0.001
C4 (g/L)	0.15 ± 0.06	0.12 ± 0.07	0.11 ± 0.06	P = 0.002
SLE with LN	12 (26.09)	16 (48.48)	14 (63.64)	P = 0.008
NPSLE	0	2 (6.06)	5 (22.73)	P = 0.002
Secondary APS	3 (6.52)	0	0	NS
Haematologic involvement	1 (2.17)	2 (6.06)	1 (4.55)	NS
Positive ANA				NS
<1:320	8 (17.39)	4 (12.12)	2 (9.09)
≥1:320	38 (82.61)	29 (87.88)	20 (90.91)
Positive Anti-dsDNA Ab	9 (19.57)	14 (42.42)	11 (50.00)	P = 0.020
Positive Anti-Sm Ab	14 (30.43)	13 (39.39)	5 (22.73)	NS
Positive Anti-SSA Ab	34 (73.91)	25 (75.76)	14 (63.64)	NS
Positive Anti-SSB Ab	9 (19.57)	6 (18.18)	0	P = 0.017
Positive Anti-Ro52 Ab	24 (52.17)	21 (63.64)	9 (40.91)	NS
Positive Anti-nRNP Ab	20 (43.48)	18 (54.55)	14 (63.64)	NS
Positive ARPA	15 (32.61)	17 (51.52)	5 (22.73)	NS

Data presented as mean ± SD or n (%) prevalence.

SLE, systemic lupus erythematosus; SLEDAI-2K, Systemic Lupus Erythematosus Disease Activity Index 2000; WBCs, white blood cells; RDW, red cell distribution width; AGR, albumin-to-globulin ratio; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; MLR, monocyte-to-lymphocyte ratio; CRP, C-reactive protein; ESR, erythrocyte sedimentation rate; LN, lupus nephritis; NPSLE, neuropsychiatric SLE; APS, antiphospholipid syndrome.

The three groups were compared using analysis of variance or Kruskal–Wallis test for continuous variables, and χ²-test or Fisher’s exact test for categorical variables (P < 0.05, comparison among these 3 subgroups).

NS, no statistically significant between-group differences (P > 0.05).

Correlations between clinical characteristics and AGR, NLR, PLR and MLR in patients with SLE

Analyses of the relationships between ratio indexes (AGR, NLR, PLR and MLR) and disease activity indexes (SLEDAI-2K, ESR, C3 and C4) in patients with SLE are summarised in Table 3. As CRP has been previously identified as more likely to indicate severe infections in SLE,²⁴ the associations of AGR, NLR, PLR and MLR with CRP were not explored in the present study. AGR was found to be inversely associated with SLEDAI-2K (r = −0.543, P < 0.001) and ESR (r = −0.549, P < 0.001), and positively correlated with C3 (r = 0.507, P < 0.001) and C4 (r = 0.321, P = 0.001). Both NLR and MLR were positively associated with SLEDAI-2K (P < 0.05), but no statistically significant correlation was found with ESR, C3 and C4 (all P > 0.05). PLR was positively correlated with SLEDAI-2K (r = 0.338, P = 0.001) and ESR (r = 0.203, P = 0.045), but displayed no statistically significant association with C3 and C4 (both P > 0.05). The correlations between AGR and SLEDAI-2K, ESR, C3 and C4 were the strongest.

Table 3.

Associations of AGR, NLR, PLR and MLR with disease activity indexes in patients with SLE.

	Disease activity index
Characteristic	SLEDAI-2K		ESR (mm/h)		C3		C4
Characteristic	r	Statistical significance	r	Statistical significance	r	Statistical significance	r	Statistical significance
AGR	−0.543	P < 0.001	−0.549	<0.001	0.507	P < 0.001	0.321	P = 0.001
NLR	0.327	P = 0.001	0.109	NS	−0.049	NS	0.051	NS
PLR	0.338	P = 0.001	0.203	P = 0.045	−0.174	NS	−0.08	NS
MLR	0.218	P = 0.029	−0.131	NS	−0.143	NS	0.013	NS

SLE, systemic lupus erythematosus; SLEDAI-2K, Systemic Lupus Erythematosus Disease Activity Index 2000; AGR, albumin-to-globulin ratio; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; MLR, monocyte-to-lymphocyte ratio; ESR, erythrocyte sedimentation rate.

NS, no statistically significant correlation (P > 0.05; Spearman’s rank correlation coefficient).

Ordinal logistic regression analysis of factors associated with SLE disease activity

The results of generalized ordered logistic regression analysis in determining factors associated with disease activity in patients with SLE are shown in Table 4. The variables used in this step were laboratory results that exhibited significant differences between SLEDAI-2K subgroups in Table 2 (P < 0.05). As male or female sex is known to be a critical factor in patients with SLE, this variable was also included. In the ordinal logistic regression analysis, AGR (β = −1.319, 95% confidence interval [CI] –2.595, –0.042; P = 0.043) and lower C4 (β = −1.073, 95% CI −2.057, −0.089; P = 0.032) were found to be statistically significant adverse prognostic factors for SLE disease activity.

Table 4.

Ordered logistic regression analyses of the predictors of disease activity in patients with SLE.

Predictor	Regression coefficient β	Standard error	Wald test	Statistical significance	95% confidence interval
Sex	0.647	0.667	0.941	NS	−0.660	1.954
Lymphocytes	−0.047	0.585	0.006	NS	−1.194	1.101
Total protein	−0.643	0.539	1.426	NS	−1.699	0.412
Albumin	−0.743	0.683	1.181	NS	−2.082	0.597
AGR	−1.319	0.651	4.098	P = 0.043	−2.595	−0.042
NLR	−0.879	0.520	2.852	NS	−1.899	0.141
PLR	−0.943	0.533	3.125	NS	−1.989	0.103
MLR	−0.030	0.477	0.004	NS	−0.966	0.905
C3	−0.602	0.552	1.189	NS	−1.685	0.480
C4	−1.073	0.502	4.572	P = 0.032	−2.057	−0.089
dsDNA	0.182	0.520	0.122	NS	−0.838	1.201
Anti-SSB Ab	−0.302	0.689	0.192	NS	−1.653	1.049

SLE, systemic lupus erythematosus; AGR, albumin to globulin ratio; NLR, neutrophil to lymphocyte ratio; PLR, platelet to lymphocyte ratio; MLR, monocyte to lymphocytes ratio.

NS, no statistically significant correlation (P > 0.05).

Discussion

The current study showed that NLR and PLR were significantly higher, and AGR was significantly lower, in the patients with SLE compared with healthy controls. Patients with SLE with severe disease activity also showed higher levels of NLR and PLR, but lower levels of AGR than those with mild disease activity. AGR was inversely correlated with SLEDAI-2K, whereas NLR and PLR were positively related to SLEDAI-2K. Of note, the AGR exhibited the strongest correlation with SLEDAI-2K compared with other inflammatory parameters. Ordinal logistic regression analyses verified that low AGR and low C4 were adverse predictors of disease activity in patients with SLE.

In general, complement proteins, including C3 and C4, are very useful biomarkers for the diagnosis, assessment of disease activity, treatment response and prognosis of patients with SLE.²⁵ C4A and C4B are two isoforms of C4, and congenital deficiency, or a low copy number, of the C4A gene has been correlated with the risk of SLE, but not low C4B gene copy number.^26,27 Higher levels of C4B gene copy number are associated with hypertension and effective response to statins treatment in paediatric patients with SLE.²⁸ As the C4 protein level is dependent on the C4 gene copy number, a patient with SLE exhibiting persistently low C4 should be evaluated for C4 gene copy number.²⁵ In the present study, C3 and C4 were significantly decreased with increased SLE disease activity, and ordered logistic regression analysis showed that C4 was an adverse factor for SLE. In patients with SLE, complement proteins must be tested regularly in routine clinical practice. Of interest, complement levels are not always linked to SLE disease activity, as normal complement levels in the active phase and consistent hypocomplementemia in remission often occurs. Complement levels should be analysed together with other evidence, such as symptoms, physical examination and other laboratory tests.

The NLR and PLR have been extensively researched as new inflammatory indicators in many diseases, including SLE. In the present study, NLR and PLR were found to be increased in patients with SLE when compared with healthy controls. Patients with SLEDAI-2K scores > 12 showed increased levels of NLR and PLR than those with SLEDAI-2K scores ≤6, but similar NLR and PLR levels to those with SLEDAI-2K scores of 7–12. In addition, Spearman’s rank correlation coefficient showed that NLR and PLR were both positively associated with SLEDAI-2K, and the above results were consistent with those of previously published reports.^3–6,29,30 However, studies concerning the association of NLR and PLR with ESR, C3 and C4 are contradictory. For example, the present study identified no relationship between NLR and C3 and C4, which was consistent with some previous studies,^3,5,30 but contradicted other findings.⁴ PLR was found to be significantly positively related to ESR in the present study, comparable to previous findings.^4,31 However, ordered logistic analysis showed that NLR and PLR could not independently predict SLE disease activity, which contrasted previous studies reporting that NLR is independently associated with SLE disease activity.^3,29,30 These inconsistent findings make it difficult to conclude whether NLR and PLR are indicators of SLE disease activity.

The AGR comprises the relationship between albumin and globulin, two crucial components of serum total protein. In general, serum albumin is decreased in poor nutritional condition and inflammatory status. Albumin has been shown to be inversely correlated with inflammatory response,³² and antitumor and antioxidant effects,^33,34 that is, low albumin predicts enhanced inflammation and worse oncologic prognosis. Serum globulin (total protein – albumin) has also been reported as a positive predictor of inflammation and cancer. Overall, both albumin and globulin are involved in the nutrition, development and prognosis of inflammation and tumours. The inverse change of albumin and globulin in such diseases has been found to make the novel prognostic indicator AGR steadier and more reliable.^35,36 In the present study, albumin was decreased and globulin was increased in SLE compared with healthy individuals, which conformed with previous research, and similar results were also found among the three subgroups of patients with SLE. AGR was decreased in SLE compared with healthy controls, and AGR was lower in patients with SLE with severe disease activity versus those with mild disease activity. In addition, Spearman’s rank correlation coefficient showed that AGR was inversely related with SLEDAI-2K, ESR, C3 and C4, and yielded higher associations than NLR, PLR and MLR. Using generalized ordinal logistic regression analysis, the present study verified that AGR was an independent risk factor for SLE disease activity, while NLR and PLR were not significant influencing factors. These findings imply that AGR may have an advantage over NLR and PLR in terms of monitoring disease activity.

The prognostic role of AGR in SLE with lupus nephritis has been verified by two previously published studies,^17,18 both of which enrolled newly diagnosed patients with SLE without renal impairment. Furthermore, these two studies reported that, compared with other biomarkers, low AGR showed the greatest hazard for risk of future lupus nephritis development. In a supplementary multivariate analysis, patients with SLE in the present study were also divided into those with or without lupus nephritis. AGR was found to be inversely, but relatively weakly, associated with SLE with lupus nephritis (Supplementary Table 2). Differences between the present and previous study results may be due to different inclusion and exclusion criteria, as patients with SLE enrolled in the present study were combined with existing lupus nephritis, which was very different from the previous two studies.^17,18 The role of AGR in SLE and SLE with lupus nephritis needs to be further verified in a polycentric, large-sample, prospective study.

To the best of our knowledge, this is the first study to determine the association between AGR and SLEDAI-2K in patients with SLE, however, the present results may be limited by several factors. First, the sample size was relatively small, the study was conducted at a single centre, and all of the participants were Chinese. Secondly, this was a retrospective study, which cannot prove causality between AGR and SLEDAI-2K. Thirdly, other inflammatory biomarkers, such as haematological interferon, interleukin (IL)-6, IL-8 and tumour necrosis factor-α, were not investigated. Thus, prospective, large sample, multi-ethnic and multi-centre studies should be conducted to validate the role of AGR in SLE and its relation to SLEDAI-2K.

Conclusion

In the present study, AGR was shown to be decreased in patients with SLE, and significantly inversely associated with SLEDAI-2K. AGR was also found to be an inverse factor for disease activity in patients with SLE. As the determination of AGR is relatively simple, economical and practical, it might be a potential and novel marker for monitoring disease activity in patients with SLE.

Supplemental Material

sj-pdf-1-imr-10.1177_03000605241244761 - Supplemental material for Albumin to globulin ratio (AGR) in systemic lupus erythematosus: correlation with disease activity

Supplemental material, sj-pdf-1-imr-10.1177_03000605241244761 for Albumin to globulin ratio (AGR) in systemic lupus erythematosus: correlation with disease activity by Meng Liu, Xingjian Li, Yukai Huang, Zhengping Huang and Qidang Huang in Journal of International Medical Research

Footnotes

Author contributions

Conception and design: Meng Liu and Xingjian Li. Data acquisition and analysis: Meng Liu, Xingjian Li, Yukai Huang, Zhengping Huang and Qidang Huang. Manuscript writing: Meng Liu. All authors read and approved the final manuscript.

Data availability statement

All data generated or analysed during this study are included in this published article and its supplementary files. Further inquiries may be directed to the corresponding author.

Declaration of conflicting interests

The authors declare that there is no conflict of interest.

Funding

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

Supplementary material

Supplemental material for this article is available online.

ORCID iD

Meng Liu

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