Platelet/lymphocyte, neutrophil/lymphocyte, and lymphocyte/monocyte ratios as biomarkers for rheumatoid arthritis: correlation with disease activity

Abstract

Background

Assessment of disease activity in rheumatoid arthritis (RA) patients is essential for the adjustment of therapy. Inflammatory changes in lymphocytes, neutrophils, monocytes, and platelets supported the use of neutrophil/lymphocyte ratio (NLR), lymphocyte/monocyte ratio (LMR), and platelet/lymphocyte ratio (PLR) as markers of inflammation, we aimed to explore the clinical significance of PLR, NLR, and LMR in RA patients.

Results

The study included 120 RA patients and 50 healthy matched controls. Clinical and laboratory data of the patients were assessed. Disease activity was measured using disease activity score (DAS28). Complete blood count (CBC) with differential count was used for the calculation of NLR, PLR, and LMR. Patients had significantly high NLR, and PLR (p < 0.001) and significantly low LMR (p < 0.001) when compared with the control group. Also, there were significant differences in the three ratios between patients in activity and those in remission (p < 0.001). Similarly, there were significant differences in all three ratios between patients with different degrees of disease activity. DAS28 score was positively correlated with NLR, PLR (r = 0.666, p < 0.001, r = 0.586, p < 0.001) and negatively correlated with LMR (r = 0.761, p < 0.001). Receiver operating characteristic (ROC) curve analysis revealed that NLR had the highest sensitivity (86.9%) for RA disease activity, followed by PLR (85.9%) then LMR (76.2%), and regarding the specificity, NLR had high specificity (81%) followed by LMR (78%) then PLR (67%).

Conclusions

Given that NLR, PLR, and LMR were significantly different in patients when compared with the controls, also on comparing different degrees of disease activity and the three ratios were significantly correlated with DAS28 score, in addition to their good sensitivity and specificity for detection of RA disease activity, all this imply that they may be easy, reliable, cost-effective, and time-saving biomarkers when added to DAS28 score for the assessment of RA disease activity.

Background

Rheumatoid arthritis is an autoimmune disease characterized by systemic inflammation with the synovial joints being the main target, leading to articular damage, disability, and increased mortality [1]. Autoimmune response to citrullinated proteins, activation of immune cells, and production of pro-inflammatory cytokines are the main pathological hallmarks for disease development [1]. Inflammation is the major mechanism behind increased disability and mortality in patients with RA [2]; assessment of inflammation with a reliable marker is essential to predict disease outcome. The markers which are commonly used in daily practice are CRP and ESR [3], but they have some limitations as they reflect short term inflammation and cannot discriminate between different inflammatory conditions [4].

The detection of autoantibodies as rheumatoid factor (RF), anti-citrullinated protein antibodies (ACPA) and anti-carbamylated protein (anti-CarP) antibodies is important for patients’ classification and prognosis [5] and disease activity assessment is essential for the adjustment of therapy in RA patients [6]; it depends mainly on pain, tender and swollen joints, C-reactive proteins (CRP) and visual analogue scale (VAS) [7]. Although CRP and ESR are commonly used to assess disease activity in clinical practice, they may be normal in patients with low disease activity [8] and they are reported to be weekly correlated with disease activity [8].

Blood cells interactions are important in the pathophysiology of immune response, inflammation, hemostasis, and oncogenesis [9]. Systemic inflammation is commonly accompanied with changes in the composition and quantity of circulating blood cells, so the characters of circulating blood cells can be used for the evaluation of inflammatory activity [10]. Elements of the immune system including platelets, neutrophils, and lymphocytes not only have a role in controlling inflammation but also they change in response to inflammation [11]. Neutrophils represent terminally differentiated cells, and when there is no inflammation, they present in the circulation for 24 to 48 h then return to the bone marrow and undergo apoptosis [12]. Cytokines, leukotrienes and bacterial lipopolysaccharides delay apoptosis of neutrophils during inflammation [13]. Neutrophils represent up to 90% of cells in the synovial fluid of RA patients; and they are also abundant at the junction of cartilage and pannus [14]. Dysregulated activation of neutrophils plays an important role in the pathogenesis of RA [15]. Early in RA, there is migration of neutrophils into joint cavity where they contribute to the initiation of inflammatory process [14]. Lymphocytes have an important role in the pathogenesis of RA, lymphopenia occurs in various hematological, autoimmune, and infectious diseases [16]. The NLR is the percentage between neutrophils which is an inflammatory activator, and lymphocytes which is an inflammatory regulator, so increased NLR is a sign of inflammation [17].

Platelets have a marked role in immune modulation and inflammation through the crosstalk between the inflammatory system and markers of coagulation. In RA platelets secrete pro-inflammatory particles that interact with leucocytes leading to inflammation [18]. Several studies reported the shifts in PLR for the evaluation of systemic inflammation, infections, and combined problems in autoimmune rheumatic diseases [19, 20]. High NLR values were recorded in patients with active Behcet’s Disease and those with increased carotid intimal thickness [21], Selim [22] reported the association of each of NLR, MLR and PLR with different manifestations of Behcet’s disease. NLR, LMR, and PLR are considered biomarkers of inflammation with a prognostic value in some types of cancers such as lung, colorectal, esophageal, and pancreatic cancer [20]. Since DAS28 score and other disease activity measures are multifactorial and time-consuming, it is necessary to search for an easy, reliable, and time saving marker for the assessment of RA disease activity, and it is beneficial to have objective data from the blood picture that correlate with RA disease activity. We aimed to evaluate the sensitivity and specificity of PLR, NLR, and LMR for the assessment of RA disease activity and their correlation with DAS28 score.

Methods

One hundred-twenty RA patients were recruited from the rheumatology department. In addition, 50 sex and age-matched healthy subjects were included as a control group. All subjects in the study groups assigned a written informed consent prior to their inclusion in the study. Ethical approval was obtained from the university ethical committee. RA patients were classified according to ACR 2010 criteria [23]. Patients with other connective tissue diseases, systemic diseases, infections, and malignancy were excluded. Disease activity was evaluated using DAS28 score through counting of tender and swollen joints, ESR measurement, and VAS was used by the patients for scoring their pain with a range from 0 (no pain) to 100 (the worst imaginable pain). Patients were classified according to DAS28 score into; remission (DAS28 ≤ 2.6), low disease activity (2.6 < DAS28 ≤ 3.2), moderate disease activity (3.2 < DAS28 ≤ 5.1), and high disease activity (DAS28 > 5.1) [24].

Blood samples were obtained from all patients for the assessment of ESR, CRP, and CBC with differential count, RF, and ACPA. PLR was calculated by dividing the platelet count by the absolute lymphocyte count. NLR was calculated by dividing the absolute neutrophil count by the absolute lymphocyte count. LMR was calculated by dividing the absolute lymphocyte count by the absolute monocyte count.

Statistical analysis

SPSS (version 17) was used for statistical analysis of the data; we used numbers and percentages for presenting demographic and clinical data for the participants. Mean ± SD was used for comparing numerical data. Pearson’s correlation was used to correlate the parameters, and p < 0.05 was considered statistically significant. ROC curve analysis with estimation of the area under the curve (AUC) and 95% confidence interval (CI) was performed to determine the sensitivity and specificity of the studied parameters for RA disease activity.

Results

Our study included 120 RA patients and 50 healthy individuals as a control group. The demographic, clinical, and medication characteristics of the participants are shown in Table 1; 90% of our patients had arthralgia, 47.5% had arthritis, 53.3% had morning stiffness, and 5% had extra-articular manifestations. Our patients had significantly (p < 0.001) very high serum levels of acute phase reactants (ESR, and CRP) significantly (p = 0.003) low serum level of hemoglobin in comparison to the control group. Despite there was no significant difference between patients and controls regarding total WBCs count, the differential count showed very high significant differences between patients and controls with decreased lymphocytes (p < 0.001), increased neutrophils (p = 0.001), and monocytes (p < 0.001) in the patients’ group. Despite the patients having high platelet count compared with the controls, the difference was insignificant. On calculating the PLR, NLR, and LMR, we found that the patients had significantly high PLR (p < 0.001) and NLR (p < 0.001) and significantly low LMR (p < 0.001) in comparison to the control group, as shown in Table 2.

Table 1. Demographic and clinical data of the participants

Item	RA patients (120)	Controls (50)
Age/years (mean ± SD)	42.87 ± 9.77	40.56 ± 10
Gender: (no. (%))
Females	108 (90%)	44 (88%)
Males	12 (10%)	6 (12%)
Disease duration /years (mean ± SD)	5.46 ± 2.93	………….
Arthralgia (no. (%))	108 (90%)	………….
Arthritis (no. (%))	57 (47.5%)	………….
Morning stiffness (no. (%))	64 (53.3%)	………….
Extra-articular manifestations	6 (5%)	………….
DAS28 (mean ± SD)	4.52 ± 1.38	………….

Table 2. Laboratory data of the patients and controls

Item	Patients (n = 120) Mean ± SD	Controls (n = 50) Mean ± SD	P value
ESR	50.73 ± 25.87	12.58 ± 5.32	< 0.001
CRP	25.95 ± 16.40	2.68 ± 1.91	< 0.001
Hemoglobin	11.53 ± 1.46	12.33 ± 1.75	0.003
Platelets	273.85 ± 40.26	263.38 ± 30.48	0.1
WBCs	7.16 ± 2.35	6.95 ± 1.68	0.573
Lymphocytes	1.77 ± 0.36	2.49 ± 0.615	< 0.001
Neutrophils	3.77 ± 0.70	3.35 ± 0.742	0.001
Monocytes	0.49 ± 0.11	0.33 ± 0.06	< 0.001
PLR	157.13 ± 50.03	111.00 ± 28.94	< 0.001
NLR	2.19 ± 0.80	1.55 ± 0.60	< 0.001
LMR	3.87 ± 1.54	7.77 ± 2.30	< 0.001
RF	106.44 ± 103.08	…………	………
ACPA	117.43 ± 98.73	…………	………

Where: PLR is platelet/lymphocyte ratio, NLR is neutrophil/lymphocyte ratio, and LMR is lymphocyte/monocyte ratio

The association of laboratory parameters with disease activity

Our patients were classified according to disease activity into active group (n = 99, DAS28 > 2.6), and remission group (n = 21, DAS28 ≤ 2.6). By comparing different laboratory parameters between the two groups, we found that the active group had significantly high acute phase reactants including ESR, and CRP (p = 0.001). Regarding the CBC parameters, active patients had high platelets (p < 0.001), monocytes (p < 0.001), neutrophils (p < 0.001), and low lymphocytes (p < 0.001) in comparison to the remission group. However, there was no significant difference between the two groups regarding hemoglobin and total WBCs count. The increased monocyte and neutrophil count and decreased lymphocyte count was associated with increased PLR (p < 0.001), NLR (p < 0.001), and decreased LMR (p < 0.001) in active patients compared with those in remission as shown in Table 3.

Table 3. Comparison of laboratory parameters between active and inactive groups

Item	Mean Difference	Std.error difference	P value	95% CI
				Lower	Upper
DAS28	2.34629	0.25	< .001	1.83	2.85
ESR	19.59019	5.97	.001	7.75	31.42
CRP	13.25844	3.76	.001	5.80	20.71
Hemoglobin	-0.58743	0.34	.095	-1.27	0.11
Platelets	42.48	8.9	< .001	24.87	60.09
WBCs	0.03306	0.56	.954	-1.09	1.15
Monocytes	0.17743	0.03	< .001	0.13	0.22
Lymphocytes	-0.55580	0.08	< .001	-0.69	-0.41
Neutrophils	1.09012	0.13	< .001	0.82	1.36
PLR	75.37	9.88	< .001	55.8	94.93
NLR	1.28668	0.15	< .001	0.98	1.58
LMR	-3.06332	0.24	< .001	-3.54	-2.58

Where: PLR is platelet/lymphocyte ratio, NLR is neutrophil/lymphocyte ratio, and LMR is lymphocyte/monocyte ratio

When we compared different disease activity groups, we found that patients with low disease activity had high neutrophils (p = 0.028), PLR (p = 0.048), NLR (p = 0.028), and low lymphocytes (p = 0.041) and LMR (p < 0.001) in comparison to those in remission. Patients with moderate disease activity had high, neutrophils (p = 0.032), monocytes (p = 0.002), PLR (p = 0.045), and NLR (p = 0.009), and low lymphocytes (p = 0.023) and LMR (p < 0.001) in comparison to those with low disease activity. In addition, high disease activity group patients showed increased ESR (p < 0.001), CRP (p < 0.001), neutrophils (p = 0.005), monocytes (p < 0.001), PLR (p = 0.019), and NLR (p < 0.001) and decreased lymphocytes (p = 0.04) and LMR (p < 0.001) in comparison to moderate disease activity group as shown in Table 4.

Table 4. laboratory parameters in different disease activity groups

Item (Mean ± SD)	Remission (21)	Low (13)	Moderate (37)	High (49)	P value
DAS28	2.09 ± 0.5	3.05 ± 0.132	4.4 ± 0.545	5.83 ± 0.5	P1 = 0.111 P2 < 0.001 P3 < 0.001
ESR	34.57 ± 11.83	27.38 ± 11.5	38.4 ± 11.78	73.16 ± 23.9	P1 = 0.66 P2 = 0.22 P3 < 0.001
CRP	15.01 ± 7.23	16.07 ± 8.73	15.62 ± 5.49	41.06 ± 14.48	P1 = 0.99 P2 = 0.1 P3 < 0.001
Hemoglobin	12.01 ± 1.27	11.96 ± 1.37	11.59 ± 1.5	11.16 ± 1.46	P1 = 0.1 P2 = 0.86 P3 = 0.52
Platelets	238.8 ± 18.51	265.53 ± 39.04	281.44 ± 38.54	285.38 ± 40.56	P1 = 0.11 P2 = 0. 66 P3 = 0.97
WBCs	7.13 ± 2.31	5.93 ± 1.42	6.89 ± 1.72	7.7 ± 2.82	P1 = 0.46 P2 = 0. 57 P3 = 0.38
Lymphocytes	2.23 ± 0.26	1.97 ± 0.22	1.72 ± 0.31	1.56 ± 0.24	P1 = 0. 041 P2 = 0.023 P3 = 0.04
Neutrophils	2.87 ± 0.68	3.38 ± 0.29	3.83 ± 0.17	4.21 ± 0.618	P1 = 0. 028 P2 = 0.032 P3 = 0.005
Monocytes	0.034±0.035	0.4 ± 0.03	0.49 ± 0.08	0.58 ± 0.09	P1 = 0. 24 P2 = 0.002 P3 < 0.001
PLR	94.95 ± 17.07	129.52 ± 38.22	162.32 ± 41.56	187.19 ± 39.8	P1 = 0. 048 P2 = 0.045 P3 = 0.019
NLR	1.13 ± 0.26	1.66 ± 0.5	2.21 ± 0.48	2.77 ± 0.64	P1 = 0. 028 P2 = 0.009 P3 < 0.001
LMR	6.39 ± 0.85	4.94 ± 0.718	3.58 ± 0.86	2.71 ± 0.61	P1 < 0.001 P2 < 0.001 P3 < 0.001

Where: PLR is platelet/lymphocyte ratio, NLR is neutrophil/lymphocyte ratio, and LMR is lymphocyte/monocyte ratio. P1 is the significance by comparing the remission with the low disease activity, p2 is the significance by comparing the low disease activity with the moderate disease activity, and p3 is the significance by comparing the moderate disease activity with the high disease activity

Correlation of DAS28 score, PLR, NLR, and LMR with laboratory parameters

We tested the correlations between different laboratory parameters and DAS28 score, we found that DAS28 score was positively correlated with ESR (r = 0.666, p < 0.001), CRP(r = 0.599, p < 0.001), PLT (r = 0.325, p < 0.001), neutrophils (r = 0.588, p < 0.001) and monocytes (r = 0.702, p < 0.001), and negatively correlated with hemoglobin (r = -0.252, p < 0.005), and lymphocytes (r = -0.573, p < 0.001) as shown in Table 5, also we found that DAS28 score was positively correlated with PLR (r = 0.586, p < 0.001), and NLR (r = 0.666, p < 0.001), and negatively correlated with LMR (r = -0.761, p < 0.001) as shown in Fig. 1(a). Both PLR and NLR were positively correlated with ESR, CRP, neutrophils, monocytes and negatively correlated with lymphocytes, while LMR was negatively correlated with ESR, neutrophils and monocytes, and positively correlated with lymphocytes. RF and ACPA were not correlated with any of the studied parameters as shown in Table 5. A graphical summary outlines the main findings of the study is shown in Fig. 2.

Receiver operating characteristic curve for detection of sensitivity and specificity of PLR, NLR, and LMR for disease activity

In the present work, ROC curve analysis revealed that PLR had an area under the curve (AUC = 0.841, p < 0.001) with a sensitivity of 85.9% and a specificity of 67% at cut off 112.39, NLR had an AUC of 0.913, p < 0.001 with a sensitivity of 86.9% and a specificity of 81% at cut-off 1.3, and LMR had an AUC of 0.873, p < 0.001, with 76.2% sensitivity and 78% specificity at cut off 4.67 as shown in Fig. 1(b).

Table 5. Correlation of DAS28 score, PLR, NLR, and LMR with laboratory parameters

	DAS28		PLR		NLR		LMR
	r	p	r	p	r	p	r	p
ESR	0.666**	< 0.001	0.291**	0.001	0.398**	< 0.001	-0.232*	0.011
CRP	0.599**	< 0.001	0.222*	0.015	0.302**	0.001	-0.16	0.081
Hemoglobin	-0.252*	0.005	-0.131	0.154	-0.175	0.056	0.082	0.373
Platelets	0.325*	< 0.001	0.469**	< 0.001	0.069	0.453	-0.088	0.338
WBCs	0.167	0.06	0.124	0.178	0.09	0.331	0.004	0.965
Lymphocytes	-0.573**	< 0.001	-0.836**	< 0.001	-.833**	< 0.001	0.746**	< 0.001
Neutrophils	0.588**	< 0.001	0.347**	< 0.001	0.733**	< 0.001	-0.370**	< 0.001
Monocytes	0.702**	< 0.001	0.190*	0.037	0.225*	0.013	-0.729**	< 0.001
RF	0.161	0.08	0.169	0.066	0.122	0.183	-0.065	0.48
ACPA	0.129	0.161	0.04	0.661	0.115	0.21	-0.139	0.13

Where: PLR is platelet/lymphocyte ratio, NLR is neutrophil/lymphocyte ratio, and LMR is lymphocyte/monocyte ratio

Where * means low correlation, and ** means high correlation

Fig. 1 [Images not available. See PDF.]

(a) Correlation of DAS28 score with PLR, NLR, and LMR, (b) ROC curve analysis of PLR, NLR, and LMR for the active RA patients compared with the remission group

Fig. 2 [Images not available. See PDF.]

A graphical summary outlines the main findings of the study

Discussion

The evaluation of RA disease activity is still challenging [25]. The assessment of inflammatory activity is of great importance to evaluate the efficacy of treatment, despite the current use of DAS28 score, CRP, and ESR to assess RA disease activity, but previous studies reported some limitations [26]. As the symptoms may not be typically presented in some patients, for example, patients may have low disease activity with CRP, ESR, CDAI, and DAS28 score at a cutoff value but still suffering from synovial inflammation with joint damage [27], despite the wide use of DAS28 score in clinical practice, it still has some drawbacks, as it is time-consuming and has subjective components, so it is essential to find an easy, reliable, time saving, objective method for the assessment of disease activity in RA patients.

Lymphocytes, neutrophils, and platelets have marked role in controlling inflammation in patients with RA, and their serum level can be used as a marker of disease activity [23]. Our findings revealed that despite there is no significant difference between patients and controls regarding total WBCs count, the differential count showed very high significant differences. Our patients had significantly decreased lymphocytes and increased neutrophils and monocytes; this agrees with Du and Tsukamoto [24, 25], who reported that the lymphocyte count was lower while the monocyte count was higher in RA patients than in healthy controls [26]. Suggesting that decreased lymphocytic count in RA patients may be due to the accumulation of lymphocytes in the inflamed joints and increased apoptotic elements such as caspase and heat shock protein. While Berezné [28] attributed lymphopenia to decreased production of lymphocytes and increased destruction secondary to immunosuppressive therapy or alterations in lymphocyte distribution. The role of neutrophils in the pathogenesis of RA is suggested by their involvement in the production of lytic enzymes and pro-oxidative mediators in the joints and activation of antigen-presenting cells, and the release of neutrophils extracellular traps which contain huge amounts of citrullinated proteins [29]. Reactive oxygen species and proteases derived from neutrophils are important in cartilage destruction and in post-translational modification of proteins and DNA [15]. In addition, cytokines and chemokines derived from neutrophils regulate the immune response and initiate autoantibodies production, and delay apoptosis of neutrophils in synovial joints leading to chronic inflammation [15].

According to our findings, RA disease activity had an impact on WBCs differential count as active patients revealed increased neutrophils and monocytes and decreased lymphocytes. DAS28 score was positively correlated with ESR, CRP, PLT, neutrophils, and monocytes, and negatively correlated with hemoglobin, and lymphocytes. This agrees with Cascao [30] who reported that patients with active disease had increased neutrophil and platelet count and decreased lymphocyte count, and he attributed the increased neutrophils due to enhanced secretion of anti-apoptotic cytokines and activation of myeloid cells and neutrophils by granulocyte colony-stimulating factor. Kouri [31] suggested that the role of neutrophils in increasing disease activity in RA is achieved through the secretion of prostaglandins, proteases, and reactive oxygen species to the joint cavity and stimulation of other cells through the secretion of B lymphocytes stimulator, IL-17, TNF-α, and other mediators of inflammation. Regarding platelets, active patients had high platelets count in comparison to the remission group and this is in agreement with Tekeoğlu [32] who found that RA patients may present with thrombocytosis during disease activity which decreases with disease remission. Platelets have a controversial role in the pathogenesis of RA [33]; Zamora [34], reported that platelets have an anti-inflammatory role mediated through leukocytes (macrophage, lymphocyte, monocyte) cell to cell interaction through platelet glycoprotein 1bα, p-selectin and CD40L while Biolard [35] suggested a pro-inflammatory role by the recruitment of leukocytes into the vascular synovium. Platelets have abundant pro-inflammatory agents that can release active micro particles, which are involved in the development of autoimmune diseases [36]. The micro particles which are released by activated platelets interact with neutrophils through the expression of lipoxygenase and eicosanoid pathway activation [37].

The PLR is considered a good marker showing shifts in both platelet and lymphocyte count in response to pro-thrombotic and inflammatory states [20]. PLR and NLR are considered new inflammatory markers and they are reported to have a role in cardiovascular diseases and malignancy, but up to date limited studies with small sample size were found showing their role in RA [38]. In addition, the role of PLR and NLR as markers of disease activity in RA is still not properly investigated due to the limited number of studies and variation in cut-off values used [19]. LMR is an inflammatory marker for the development and progression of RA, but its role is still unclear [34]. Our study revealed that RA patients had significantly high PLR, NLR, and low LMR when compared with the control group and this is in agreement with Jin [39] who found that the PLR and NLR were higher in RA patients than healthy controls. Lee [40] had concluded in his meta-analysis study that PLR and NLR were high in RA patients than healthy controls. Several studies supported the importance of PLR and NLR as diagnostic biomarkers in patients with rheumatic diseases especially RA [41, 42]. Du [34] found that LMR was lower in patients with RA than healthy controls.

According to our findings, active patients had high PLR, NLR and low LMR in comparison to those in remission and when we compared different disease activities groups, we found that increased disease activity was associated with increased PLR, NLR, and decreased LMR. In addition, we found that DAS28 score was positively correlated with PLR, and NLR and negatively correlated with LMR. Our findings are in agreement with Abd-Elazeem and Sargin [19, 43] who reported that PLR and NLR were high in RA patients with active disease and positively correlated with the DAS28 score. Lee [40] concluded that PLR and NLR were positively correlated with disease activity. Du [34] found that LMR was low in patients with active disease and negatively correlated with disease activity. In the work of Gaballah [44], NLR was not only significantly correlated with DAS28 but also with the findings of tenosynovitis on ultrasound in RA patients. So future work should focus on these ratios as biomarkers for articular as well as extra-articular inflammatory activity in RA patients.

ROC curve analysis revealed that NLR had the highest sensitivity for RA disease activity, followed by PLR then LMR. Regarding the specificity, NLR had the highest specificity, followed by LMR then PLR. Zhang [45] found that combining NLR with PLR is more accurate for distinguishing patients with active disease from those in remission according to ROC curve analysis. Our findings support that PLR, NLR, and LMR may be useful markers for the assessment of RA disease activity despite that they should not be used in isolation, it is important to consider other factors such as clinical, laboratory, and radiological findings to accurately assess disease activity and guide treatment decisions.

Conclusions

Given that NLR, PLR, and LMR were significantly different in patients when compared with the controls, also on comparing different degrees of disease activity and the three ratios were significantly correlated with DAS28 score, in addition to their good sensitivity and specificity for detection of RA disease activity, all this imply that they may be easy, reliable, cost-effective, and time-saving biomarkers when added to DAS28 score for the assessment of RA disease activity.

Limitation

Our study is a single center, small sample size study, so further studies with larger sample sizes and longer follow-up periods with monitoring patient’s medications are needed to confirm the usefulness of these markers for the assessment of RA disease activity.

Acknowledgements

Not applicable.

Authors’ contributions

Conceptualization and diagnosis: Sahar A. Elsayed and Mohamed A. Esmail; Material preparation, and data collection: Shereen M. Basily; Analysis, investigation, and writing: Sahar A. Elsayed and Ola Mounir; Review, editing and approval: All authors.

Funding

This study had no funding from any resource.

Availability of data and materialS

The data of the current study is available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

This study was carried out in accordance with the ethical standards laid down in the Helsinki Declaration of 1975 and its later amendments in 2000 and approved by the Medical Research Ethics Committee, Faculty of Medicine, Sohag University, all patients included in this study gave written informed consent to participate in this research.

Consent for publication

All patients included in this research gave written informed consent to publish the data contained within this study.

Competing interests

The authors declare that they have no competing interests.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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