Correlation between C-Reactive Protein to Albumin Ratio and Disease Activity in Patients with Axial Spondyloarthritis

Zhong, Zheng; Huang, Yukai; Liu, Yuqi; Chen, Junming; Liu, Meng; Huang, Qidang; Zheng, Shaoling; Guo, Xin; Deng, Weiming; Li, Tianwang

doi:https://doi.org/10.1155/2021/6642486

Disease Markers

On this page

Abstract Introduction Materials and Methods Results Discussion Data Availability Conflicts of Interest Authors’ Contributions Acknowledgments Supplementary Materials References Copyright Related Articles

Research Article | Open Access

Volume 2021 | Article ID 6642486 | https://doi.org/10.1155/2021/6642486

Correlation between C-Reactive Protein to Albumin Ratio and Disease Activity in Patients with Axial Spondyloarthritis

Zheng Zhong,^1,2Yukai Huang,¹Yuqi Liu,¹Junming Chen,^1,2Meng Liu,¹Qidang Huang,¹Shaoling Zheng,¹Xin Guo,¹Weiming Deng,¹and Tianwang Li^1,2

Academic Editor: Peng fei Li

Received12 Dec 2020

Accepted29 May 2021

Published14 Jun 2021

Abstract

Background. The C-reactive protein (CRP) to albumin (ALB) ratio (CAR) has emerged as a novel inflammatory biomarker. This study was designed to investigate the role of CAR in the disease activity of axial spondyloarthritis (axSpA). Methods. A total of 241 patients and 61 healthy controls were retrospectively enrolled in this study. AxSpA patients were further divided into the inactive group () and active group () according to Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) cutoff value of 4. Laboratory data and clinical assessment indices were recorded. Spearman’s correlation analysis, receiver operation characteristic (ROC) curve analysis, and binary logistic regression analysis were performed. Results. In axSpA patients, CAR was significantly higher than the healthy group (). Similarly, axSpA patients in the active group had higher CAR than the inactive group (). Besides, CAR was positively correlated with erythrocyte sedimentation rate (ESR) (, ), CRP (, ), BASDAI (, ), and Bath Ankylosing Spondylitis Functional Index (BASFI) (, ). ROC curve analysis suggested that the area under the curve (AUC) of CAR for axSpA of the active group was 0.701, which was higher than that of CRP and ESR. The optimal cutoff point of CAR for axSpA of the active group was 0.3644, with a sensitivity and specificity of 58.5% and 79.0%. Binary logistic analysis results revealed that CAR was an independent predictive factor for axSpA disease activity (, 95% CI: 1.423-15.348, ). Conclusions. CAR was increased in axSpA and axSpA of the active group. CAR may be a novel and reliable indicator for axSpA disease activity.

1. Introduction

Axial spondyloarthritis (axSpA) is a common inflammatory and autoimmune disease characterized by chronic back pain and morning stiffness, which is divided into nonradiographic and radiographic disease according to radiographic structural damage in the sacroiliac joints [1]. The prevalence of axSpA varies from 0.32% to 1.4% in different countries with a female to male ratio of about 1 : 2-3 [2]. Although the exact etiology of axSpA still remains obscure, inflammation is reported to be involved in the pathogenesis and development of axSpA, contributing to the deformity and disability in the late disease stage [3, 4]. Therefore, it is crucial to monitor the inflammation state and disease activity of axSpA. Erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) are the most widely used inflammatory indicators and increased in active disease status, but the specificity and sensitivity of them are limited and they only reflect the short-term inflammatory activity [5–7].

Many studies have shown that the whole blood components including neutrophils, lymphocytes, monocytes, and platelets were correlated with systemic inflammation [8–10]. Neutrophil to lymphocyte ratio (NLR), monocyte to lymphocyte ratio (MLR), and platelet to lymphocyte ratio (PLR) have been identified as new inflammatory markers and reported to be higher in axSpA patients and related to disease activity [11–13]. However, the results are contradictory with other studies [14, 15]. Albumin (ALB) is a plasma protein and functions in maintaining colloid pressure and transporting free fatty acids, drug metabolites, and bilirubin, whose synthesis is reported to be affected by inflammation [16]. In recent years, CRP to ALB ratio (CAR) has emerged as a novel inflammatory indicator to evaluate inflammation and predict the prognosis of several cancers, such as Ewing sarcoma, esophageal cancer, and colorectal cancer [17, 18]. Some studies have demonstrated the correlations of CAR with disease activity of inflammatory diseases, such as Crohn’s disease and rheumatoid arthritis [19, 20]. However, the role of CAR in axSpA has not been investigated yet. Hence, this study was designed to investigate the level of CAR and the relationship between CAR and disease activity in axSpA patients.

2. Materials and Methods

2.1. Subjects

We performed a retrospective, cross-sectional study in Guangdong Second Provincial General Hospital from December 2015 to August 2019. A total of 241 axSpA patients and 61 healthy controls were enrolled [21]. The diagnosis met the 2009 Assessment of SpondyloArthritis International Society (ASAS) Classification Criteria. Exclusion criteria were as follows: concomitant infection, cardiovascular diseases, diabetes mellitus, renal and hepatic dysfunction, liver cirrhosis, malignancy, tuberculosis, pregnancy, malnutrition, and other inflammatory and autoimmune diseases. This study was approved by the ethics committee office of Guangdong Second Provincial General Hospital (2017-FSMY-009).

2.2. Demographic and Laboratory Data

Demographic and laboratory data including age, gender, CRP, ESR, lymphocyte, monocyte, neutrophil, platelet, ALB, and medications were recorded. CAR, NLR, MLR, and PLR were calculated. All peripheral venous blood samples were drawn from 6 to 8 am after overnight fasting and examined for the routine blood test and biochemical test within 2 hours after sample collection. Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) [22, 23] and Bath Ankylosing Spondylitis Functional Index (BASFI) [24] were also collected. Patients were assigned into two groups based on a cutoff value of 4, with in the inactive group and in the active group.

2.3. Statistical Analysis

SPSS 19.0, GraphPad Prism 6.0, and MedCalc software were used to carry out statistical analyses. Continuous variables were expressed as , and categorical variables were presented as percentages or numbers. Comparisons of continuous variables were performed by Student’s -test or Mann–Whitney test, while categorical variables were analyzed by chi-square test. The associations of CAR with inflammatory indicators and disease activity were assessed by Spearman’s correlation analysis. Receiver operating characteristic (ROC) curves were performed, and the areas under the ROC curves (AUCs) were calculated to assess the utility of variables to discriminate the disease activity. In order to explore the predictive factors of axSpA disease activity, logistic regression analysis was performed. value less than 0.05 was considered statistically significant.

3. Results

3.1. Demographic and Laboratory Data of the Subjects

Demographic and laboratory data of healthy controls and axSpA patients were summarized (Table 1). There were no statistical differences with regard to age () and sex () between healthy controls and axSpA patients. CAR, NLR, PLR, MLR, ESR, and CRP were significantly higher in axSpA patients than those in healthy controls (, respectively, except for PLR, ), while ALB was significantly lower in axSpA patients ().

3.2. Comparisons of Variables between Active Group and Inactive Group of axSpA

According to the BASDAI, the inactive group included 176 patients and the active group included 65 patients. No statistical differences were detected in age () and gender () between the active group and the inactive group. In comparison with the inactive group, CAR, ESR, CRP, BASDAI, and BASFI were significantly higher in the active group, while ALB was lower (, respectively) (Table 2).

3.3. Correlations of Variables with ESR, CRP, BASDAI, and BASFI

CAR, NLR, PLR, and MLR were all positively correlated with ESR level (, ; , ; , ; and , , respectively) and CRP level (, ; , ; , ; and , , respectively) (Figure 1). CAR, CRP, and ESR were positively associated with BASDAI (, ; , ; and , , respectively) and BASFI (, ; , ; and , , respectively), while no correlations were found between NLR, PLR, MLR, BASDAI, and BASFI (Figure 1 and Figure S1).

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

(j)

(k)

(l)

(m)

(n)

(o)

(p)

3.4. Diagnostic Values of Variables for Discriminating axSpA of Active Group from Inactive Group

ROC curves were depicted to assess the values of CAR, NLR, PLR, MLR, ESR, and CRP for discriminating axSpA of active group from inactive group (Figure 2). CAR yielded the highest AUC (0.701, 95% CI: 0.638-0.758) than NLR (0.550, 95% CI: 0.485-0.614), PLR (0.528, 95% CI: 0.463-0.592), MLR (0.532, 95% CI: 0.466-0.596), ESR (0.685, 95% CI: 0.622-0.743), and CRP (0.691, 95% CI: 0.629-0.749) (Table 3). The optimal cutoff point of CAR for axSpA of active group was 0.3644, with a sensitivity and specificity of 58.5% and 79.0%.

3.5. Binary Logistic Regression Analysis of Independent Predictive Factors for axSpA Disease Activity

Besides, in order to analyze the independent predictive factors for axSpA disease activity, binary logistic regression analysis was performed. After univariate regression analysis, CAR, NLR, PLR, MLR, ESR, and CRP were analyzed in multivariate regression analysis. The result indicated that only CAR was an independent predictive factor for axSpA disease activity (, 95% CI: 1.423-15.348, ) (Table 4).

4. Discussion

In this study, CAR was found to be increased in axSpA and axSpA of the active group. CAR showed positive correlations with BASDAI and BASFI. ROC curve analysis revealed that CAR yielded the best value in distinguishing the active group from the inactive group. Besides, logistic regression analysis revealed that CAR was an independent predictive factor for axSpA disease activity.

AxSpA is a common inflammatory and autoimmune disease with inflammation of axial skeleton [25]. Increasing evidence shows that neutrophils, platelets, monocytes, and lymphocytes are involved in the inflammation and thus the combinations of these parameters, NLR, PLR, and MLR, have emerged as novel inflammatory indicators in many inflammatory diseases, including axSpA [13, 26, 27]. Previous studies found that NLR, PLR, and MLR were significantly higher in axSpA patients compared with healthy controls [13]. Consistently, the present study showed increased NLR, PLR, and MLR in axSpA patients. However, the results about the relationship between NLR, PLR, MLR, and inflammation and disease activity of axSpA remain conflicting. Enginar et al. reported that PLR and NLR were associated with ESR, CRP, and disease activity [28], while Bozan et al. found no correlations between PLR, MLR, and BASDAI in axSpA patients [29]. Yang et al. revealed positive correlations of MLR with traditional inflammatory indicators [14], while Liu et al. showed that there was only a positive correlation of MLR with CRP but not with ESR [15]. In this study, correlations were only found between NLR, PLR, MLR, CRP, and ESR but not clinical assessment indices BASDAI or BASFI. Therefore, it is necessary to find more reliable biomarkers evaluating the disease activity of axSpA.

CRP is synthesized by the liver and reflects systemic inflammation, which is widely used as an inflammatory indicator in the clinic [30]. ALB is recognized as an indicator for nutrition status, which is decreased in serious malnutrition, chronic inflammatory, and autoimmune diseases [31]. Therefore, CAR, integrating the effects of CRP and ALB, could be a strong indicator for assessing inflammation. Recently, CAR has emerged as a novel inflammatory biomarker for evaluating disease activity in some inflammatory diseases, such as rheumatoid arthritis [19]. However, the relationship between CAR and axSpA was rarely investigated. To the best of our knowledge, this study is the first to investigate the role of CAR in axSpA. Our results showed that CAR was significantly increased in axSpA patients and strongly associated with ESR and CRP, indicating that CAR could be a reliable inflammatory biomarker in axSpA patients. Besides, significantly higher CAR was detected in the active group compared to the inactive group, and positive correlations were found between CAR and BASDAI and BASFI. The AUC of CAR was higher than other biomarkers (NLR, PLR, MLR, ESR, and CRP) in discriminating axSpA patients of the active group from inactive group, reflecting that CAR may serve as a superior indicator in assessing disease activity in axSpA patients. It was reported that synthesis of CRP was induced by interleukin-6 (IL-6), and synthesis of ALB was inhibited by tumor necrosis factor (TNF) and IL-6 which were proinflammatory [32, 33]. Previous studies have demonstrated that TNF and IL-6 are involved in the initiation and maintenance of inflammation of axSpA [34], which may account for the associations between increased CAR and inflammation and disease activity of axSpA. Furthermore, several studies have revealed that CAR had prognostic value in cancers [35]. Similarly, this study showed that CAR was an independent predictive factor of axSpA disease activity, indicating that increased CAR may predict active disease activity in axSpA.

Some limitations were included in this study. One limitation was that it was a single-center study with a relatively small sample size, so the results may not be extrapolated to the general population. Second, given that our study was a retrospective study, selection bias and recall bias were unavoidable. Third, some data about radiological scores and disease duration were missing, so we could not assess the effect of radiological changes and disease duration on the results. Thus, the findings of our study need further validation by more large-sample, multicenter, and prospective studies.

In conclusion, CAR was increased in axSpA and axSpA of the active group. CAR was an independent predictive factor for axSpA disease activity. CAR may be a novel and reliable biomarker for assessing the disease activity of axSpA patients.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare no conflict of interest in this work.

Authors’ Contributions

Zheng Zhong and Yukai Huang have contributed equally to this work.

Acknowledgments

We sincerely thank the researchers and participants in the study. This study was supported by the Natural Science Foundation of Guangdong Province of China (No. 2017A030313526).

Supplementary Materials

Figure S1: correlations of CAR, CRP, and ESR with BASDAI and BASFI in axSpA patients. (Supplementary Materials)

References

J. Sieper, J. Braun, M. Dougados, and D. Baeten, “Axial spondyloarthritis,” Nature Reviews Disease Primers, vol. 1, no. 1, p. 15013, 2015.
View at: Publisher Site | Google Scholar
J. Sieper and D. Poddubnyy, “Axial spondyloarthritis,” Lancet, vol. 390, no. 10089, pp. 73–84, 2017.
View at: Publisher Site | Google Scholar
J. Zochling and J. Braun, “Mortality in rheumatoid arthritis and ankylosing spondylitis,” Clinical and Experimental Rheumatology, vol. 27, 4 Suppl 55, pp. S127–S130, 2009.
View at: Google Scholar
A. Zink, J. Braun, J. Listing, and J. Wollenhaupt, “Disability and handicap in rheumatoid arthritis and ankylosing spondylitis--results from the German rheumatological database. German Collaborative Arthritis Centers,” The Journal of Rheumatology, vol. 27, no. 3, pp. 613–622, 2000.
View at: Google Scholar
K. F. Hobbs and M. D. Cohen, “Rheumatoid arthritis disease measurement: a new old idea,” Rheumatology, vol. 51, suppl 6, pp. vi21–vi27, 2012.
View at: Publisher Site | Google Scholar
M. Dougados, A. Gueguen, J. P. Nakache et al., “Clinical relevance of C-reactive protein in axial involvement of ankylosing spondylitis,” The Journal of Rheumatology, vol. 26, no. 4, pp. 971–974, 1999.
View at: Google Scholar
A. Spoorenberg, D. van der Heijde, E. de Klerk et al., Relative Value of Erythrocyte Sedimentation Rate and C-Reactive Protein in Assessment of Disease Activity in Ankylosing Spondylitis, Universiteit Maastricht, 1999.
G. Layh-Schmitt, S. Lu, F. Navid et al., “Generation and differentiation of induced pluripotent stem cells reveal ankylosing spondylitis risk gene expression in bone progenitors,” Clinical Rheumatology, vol. 36, no. 1, pp. 143–154, 2017.
View at: Publisher Site | Google Scholar
C. Gabay and I. Kushner, “Acute-phase proteins and other systemic responses to inflammation,” New England Journal of Medicine, vol. 340, no. 6, pp. 448–454, 1999.
View at: Publisher Site | Google Scholar
H. Resorlu, M. Resorlu, F. Gokmen et al., “Association between mean platelet volume and bone mineral density in patients with ankylosing spondylitis and diagnostic value of diffusion-weighted magnetic resonance imaging,” Journal of Physical Therapy Science, vol. 27, no. 4, pp. 1137–1140, 2015.
View at: Publisher Site | Google Scholar
F. Gökmen, A. Akbal, H. Reşorlu et al., “Neutrophil–lymphocyte ratio connected to treatment options and inflammation markers of ankylosing spondylitis,” Journal of Clinical Laboratory Analysis, vol. 29, no. 4, pp. 294–298, 2015.
View at: Publisher Site | Google Scholar
O. Boyraz, B. Koç, A. Boyacı, A. Tutoğlu, H. Sarman, and H. Ozkan, “Ratio of neutrophil/lymphocyte and platelet/lymphocyte in patient with ankylosing spondylitis that are treating with anti-TNF,” International Journal of Clinical and Experimental Medicine, vol. 7, no. 9, pp. 2912–2915, 2014.
View at: Google Scholar
Y. Huang, W. Deng, S. Zheng et al., “Relationship between monocytes to lymphocytes ratio and axial spondyloarthritis,” International Immunopharmacology, vol. 57, pp. 43–46, 2018.
View at: Publisher Site | Google Scholar
Z. Yang, Z. Zhang, F. Lin et al., “Comparisons of neutrophil-, monocyte-, eosinophil-, and basophil- lymphocyte ratios among various systemic autoimmune rheumatic diseases,” APMIS, vol. 125, no. 10, pp. 863–871, 2017.
View at: Publisher Site | Google Scholar
M. Liu, Y. Huang, Z. Huang et al., “The role of fibrinogen to albumin ratio in ankylosing spondylitis: correlation with disease activity,” Clinica Chimica Acta, vol. 505, pp. 136–140, 2020.
View at: Publisher Site | Google Scholar
B. Ruot, F. Béchereau, G. Bayle, D. Breuillé, and C. Obled, “The response of liver albumin synthesis to infection in rats varies with the phase of the inflammatory process,” Clinical Science (London, England), vol. 102, no. 1, pp. 107–114, 2002.
View at: Publisher Site | Google Scholar
T. Tominaga, T. Nonaka, Y. Sumida, S. Hidaka, T. Sawai, and T. Nagayasu, “The C-reactive protein to albumin ratio as a predictor of severe side effects of adjuvant chemotherapy in stage III colorectal cancer patients,” PLoS One, vol. 11, no. 12, article e0167967, 2016.
View at: Publisher Site | Google Scholar
M. Ishizuka, H. Nagata, K. Takagi, Y. Iwasaki, N. Shibuya, and K. Kubota, “Clinical significance of the C-reactive protein to albumin ratio for survival after surgery for colorectal cancer,” Annals of Surgical Oncology, vol. 23, no. 3, pp. 900–907, 2016.
View at: Publisher Site | Google Scholar
W. Yang, W. H. Zhang, H. Q. Ying et al., “Two new inflammatory markers associated with disease activity score-28 in patients with rheumatoid arthritis: albumin to fibrinogen ratio and C-reactive protein to albumin ratio,” International Immunopharmacology, vol. 62, pp. 293–298, 2018.
View at: Publisher Site | Google Scholar
G. Qin, J. Tu, L. Liu et al., “Serum albumin and C-reactive protein/albumin ratio are useful biomarkers of Crohn’s disease activity,” Medical Science Monitor, vol. 22, pp. 4393–4400, 2016.
View at: Publisher Site | Google Scholar
J. Sieper, M. Rudwaleit, X. Baraliakos et al., “The Assessment of SpondyloArthritis international Society (ASAS) handbook: a guide to assess spondyloarthritis,” Annals of the Rheumatic Diseases, vol. 68, Suppl 2, pp. ii1–i44, 2009.
View at: Publisher Site | Google Scholar
S. Garrett, T. Jenkinson, L. G. Kennedy, H. Whitelock, P. Gaisford, and A. Calin, “A new approach to defining disease status in ankylosing spondylitis: the Bath Ankylosing Spondylitis Disease Activity Index,” The Journal of Rheumatology, vol. 21, no. 12, pp. 2286–2291, 1994.
View at: Google Scholar
Y. Akkoc, A. G. Karatepe, S. Akar, Y. Kirazli, and N. Akkoc, “A Turkish version of the bath ankylosing spondylitis disease activity index: reliability and validity,” Rheumatology International, vol. 25, no. 4, pp. 280–284, 2005.
View at: Publisher Site | Google Scholar
A. Calin, S. Garrett, H. Whitelock et al., “A new approach to defining functional ability in ankylosing spondylitis: the development of the Bath Ankylosing Spondylitis Functional Index,” The Journal of Rheumatology, vol. 21, no. 12, pp. 2281–2285, 1994.
View at: Google Scholar
L. Procházková, V. Červeňák, and M. Souček, “Axial spondyloarthritis,” Vnitr̆ní Lékar̆ství, vol. 64, no. 2, pp. 108–116, 2018.
View at: Publisher Site | Google Scholar
C. Brostjan and R. Oehler, “The role of neutrophil death in chronic inflammation and cancer,” Cell Death Discovery, vol. 6, no. 1, p. 26, 2020.
View at: Publisher Site | Google Scholar
E. Boilard, P. A. Nigrovic, K. Larabee et al., “Platelets amplify inflammation in arthritis via collagen-dependent microparticle production,” Science, vol. 327, no. 5965, pp. 580–583, 2010.
View at: Publisher Site | Google Scholar
A. U. Enginar and C. Kacar, “Neutrophil-lymphocyte and platelet-lymphocyte rate and their seasonal differences in ankylosing spondylitis and rheumatoid arthritis patients using anti-TNF medication,” Bratislavské Lekárske Listy, vol. 120, no. 8, pp. 586–592, 2019.
View at: Publisher Site | Google Scholar
N. Bozan, M. Alpaycı, M. Aslan et al., “Mean platelet volume, red cell distribution width, platelet-to-lymphocyte and neutrophil-to-lymphocyte ratios in patients with ankylosing spondylitis and their relationships with high-frequency hearing thresholds,” European Archives of Oto-Rhino-Laryngology, vol. 273, no. 11, pp. 3663–3672, 2016.
View at: Publisher Site | Google Scholar
A. Saxena and B. N. Cronstein, “Acute phase reactants and the concept of inflammation,” in Kelley's Textbook of Rheumatology, Elsevier, 2013.
View at: Google Scholar
J. W. Kim, J. Y. Jung, C. H. Suh, and H. A. Kim, “Systemic immune-inflammation index combined with ferritin can serve as a reliable assessment score for adult-onset Still's disease,” Clinical Rheumatology, vol. 40, no. 2, pp. 661–668, 2021.
View at: Publisher Site | Google Scholar
A. Fleck, “Clinical and nutritional aspects of changes in acute-phase proteins during inflammation,” The Proceedings of the Nutrition Society, vol. 48, no. 3, pp. 347–354, 1989.
View at: Publisher Site | Google Scholar
S. Cabrerizo, D. Cuadras, F. Gomez-Busto, I. Artaza-Artabe, F. Marín-Ciancas, and V. Malafarina, “Serum albumin and health in older people: review and meta analysis,” Maturitas, vol. 81, no. 1, pp. 17–27, 2015.
View at: Publisher Site | Google Scholar
A. Levitova, H. Hulejova, M. Spiritovic, K. Pavelka, L. Senolt, and M. Husakova, “Clinical improvement and reduction in serum calprotectin levels after an intensive exercise programme for patients with ankylosing spondylitis and non-radiographic axial spondyloarthritis,” Arthritis Research & Therapy, vol. 18, no. 1, p. 275, 2016.
View at: Publisher Site | Google Scholar
A. Kinoshita, H. Onoda, N. Imai et al., “The C-reactive protein/albumin ratio, a novel inflammation-based prognostic score, predicts outcomes in patients with hepatocellular carcinoma,” Annals of Surgical Oncology, vol. 22, no. 3, pp. 803–810, 2015.
View at: Publisher Site | Google Scholar

Copyright

Copyright © 2021 Zheng Zhong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation

Download other formats

Order printed copies

Views

559

Downloads

540

Citations