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Evaluation of hematological markers as prognostic tools in rheumatoid arthritis

BMC Rheumatology volume 8, Article number: 75 (2024) Cite this article

Abstract

Background

Reducing inflammation is central to the management of RA. However, commonly used markers such as CRP and ESR, along with the DAS-28 score, have shown limitations. Hematologic indices, such as platelet-lymphocyte ratio (PLR), lymphocyte-monocyte ratio (LMR), and neutrophil-lymphocyte ratio (NLR), show potential as reliable indicators of inflammation in RA. This study evaluates these markers across different RA activity levels to identify effective biomarkers for distinguishing active RA from remission.

Materials and methods

305 RA patients were enrolled in our study, diagnosed by ACR/EULAR 2010 criteria, and divided into four groups according to the DAS28-ESR score. 8 ml of blood were taken for a CBC test and serological tests such as rheumatoid factor (RF), anticyclic citrullinated peptide antibodies (anti-CCP), anti-nuclear antibodies (ANA), and C-reactive protein (CRP). Platelet-lymphocyte ratio (PLR), lymphocyte-monocyte ratio (LMR), and neutrophil-lymphocyte ratio (NLR) were assessed as potential markers of inflammation.

Results

The mean age of RA patients was 51.7 years and a disease duration of 56.7 months. Significant differences in tender and swollen joints were observed between RA groups. Laboratory findings revealed higher CRP and ESR in active RA patients, while hemoglobin, hematocrit, and lymphocyte counts were higher in remission group. ROC analysis showed ESR, CRP, NLR, and PLR as potential markers for distinguishing active from remission RA, with ESR demonstrating the highest diagnostic accuracy. LMR could not differentiate between active and inactive forms of RA disease.

Conclusion

The NLR and PLR markers were significantly correlated with traditional inflammatory markers like CRP and ESR. These novel markers could be useful tools for evaluating RA activity, offering a cost-effective alternative to imaging techniques. Further research is needed to confirm their clinical utility.

Peer Review reports

Introduction

Rheumatoid arthritis (RA) is an autoimmune disease marked by chronic, and progressive inflammation, mainly affecting the joints and resulting in bone and cartilage destruction. This deterioration leads to a decreased quality of life and reduced life expectancy [1, 2]. Although the exact cause of RA is not fully understood, it involves complex interactions among genetic predispositions and environmental factors [3,4,5]. Recent researches have highlighted that several metabolic pathways are dysregulated in RA patients, contributing to chronic inflammation [6,7,8]. Given that inflammation is the primary issue for these patients, the main goal of RA treatment is to reduce disease activity or achieve remission. Decreasing inflammation could prevent further bone and cartilage destruction, improve quality of life, and reduce mortality and morbidity rates [9].

Inflammation is the main player of an increased morbidity and mortality rate in RA patients [10, 11]. Therefore, monitoring of inflammation in RA is critical [12]. Commonly used markers such as C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) markers, along with the disease activity score 28 (DAS-28), have notable limitations in accurately assessing disease activity [13,14,15]. These markers may not always reliable to indicate inflammation [9, 16]. It has been reported that while magnetic resonance and ultrasound imaging showed synovial inflammation, the inflammatory markers such as CRP, ESR, and DAS-28 were at the lowest level or at the normal level [17, 18]. Indeed, synovial inflammation detected by MRI or ultrasound may not be reflected in CRP, ESR, or DAS-28 scores. However, while the MRI and ultrasound imaging techniques are effective, they are time-consuming and costly, highlighting the need for simpler and reliable biomarkers to assess inflammation in RA patients [19].

The chronic inflammation and immune dysregulation in RA also affect the hematopoietic system through factors such as a lack of growth factors, immune complex formation, antibody production, drug toxicities, and production of inflammatory mediators. Immune cells, including lymphocytes, monocytes, neutrophils, and platelets, play significant roles in inflammation [16]. Systemic inflammation can alter both the quantity and composition of these immune cells, and the number and ratio of circulating blood immune cells may provide insight into inflammatory status of RA patients [20]. Ration such as the platelet-lymphocyte ratio (PLR), lymphocyte-monocyte ratio (LMR), and neutrophil-lymphocyte ratio (NLR) have been identifies as sensitive biomarkers of inflammation in conditions such as diabetes [21], cardiovascular disease [22], cancers [23,24,25,26], and infectious diseases [27].

In RA, studies have shown that PLR and NLR are significantly higher in RA patients than in healthy control subjects. Furthermore, these ratios, PLR and NLR, were correlated with inflammatory indices, disease activity score, and treatment responses in RA patients [16, 28,29,30,31,32]. LMR was also been associated with disease activity score in RA patients [33]. Our study aims to evaluate hematological and serological markers across four stages of RA (high activity, moderate activity, low activity, and remission) to identify reliable biomarkers that can differentiate patients with high disease activity from those in remission.

Materials and methods

Study design and participants

Our study was conducted at Shahid Beheshti hospital, Qom, Iran, from May to December 2022. All RA patients were examined by an experienced rheumatologist and diagnosed with RA based on the American College of Rheumatology (ACR)/European League Against Rheumatism (EULAR) 2010 classification criteria [34]. The sample size was determined based on previous studies [35, 36], ensure significant power to detect significant associations between hematological markers and disease severity in RA patients. A total of 305 RA patients were enrolled in our study, with no clinical evidence or family history of any type of autoimmune disorders other than RA. RA patients were divided into four groups according to the DAS-28 score. The mean age ± standard deviation (SD) for each group was as follow: highly active RA patients = 52.1 ± 1.6 years, moderate active RA patients = 53.1 ± 1.2 years, low activity RA patients = 53.5 ± 1.9 years, and RA patients in remission = 49.2 ± 1.2 years. Patients with systemic diseases, including cancer, acute or chronic infections, hematologic abnormalities, chronic renal failure, chronic obstructive pulmonary disease, hypertension, coronary artery disease, and diabetes mellitus, were excluded from the study. This study adhered to the ethical guidelines outlined in the Declaration of Helsinki for humans, and was approved by the Human Research Ethics Committee of Khomein University of Medical Sciences (IR.KHOMEIN.REC.1401.002). All participants were informed of the study design, and written informed consent was obtained from all those who participated in the study.

Data and sample collection

A physical examination was performed by an expert rheumatologist, and parameters such as the swollen joint count (SJC), tender joint count (TJC), and patient global assessment (PGA) using a visual analogue scale (VAS) were recorded. The disease activity score for each RA patient was calculated using the DAS28-ESR, which incorporates TJC, SJC, ESR, and global health scores [37] as follow:

DAS28-ESR= (0.56*Sqrt (Tender Joint Count) + 0.28*Sqrt (Swollen Joint Count) + 0.7*ln (ESR) + 0.014*(global health)).

Based on this score, disease activity was categorized into high activity (DAS28 ≥ 5.1), moderate activity (3.2 < DAS28 < 5.1), low activity (2.6 < DAS28 < 3.2) and remission (DAS28 < 2.6). We divided the patients into four groups according to the DAS28-ESR score [19].

Blood samples (8 ml, with and without anticoagulant) were collected from each RA patient, with 2 ml for the ESR test, 2 ml for hematological parameter analysis, and 4 ml for serological tests.

Laboratory tests

ESR was measured using Westergren method [38]. Hematological markers, including white blood cells (WBC) count, red blood cell (RBC) count, platelets, neutrophils, lymphocytes, and monocytes, hemoglobin (Hb) concentration, hematocrit (Hct), mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), mean platelet volume (MPV), and red cell distribution width (RDW) assessed via a complete blood count (CBC) test. Ratios such as NLR (neutrophil-to-lymphocyte), LMR (lymphocyte-to-monocyte), and PLR (platelet-to-lymphocyte) were calculated. MPV, the marker of platelet activation, was derived from the platelet histogram. Hematological parameters were measured using a Cell-Dyn 3700 SL hematology analyzer (Abbott, Chicago, IL, USA). The remaining 4 ml of blood (without anticoagulant) was centrifuged, separated, and stored at -20 °C until use. Rheumatoid factor (RF) levels were measured via enzyme-linked immunosorbent assay (ELISA), with values above 20 arbitrary units considered positive. Anti-cyclic citrullinated peptide antibodies (anti-CCP) were also measured by ELISA, with levels above 5 arbitrary unit considered positive. Anti-nuclear antibody (ANA) positivity was obtained from medical records. CRP was measured by ELISA, with results reported as mean ± SD.

Statistical analysis

Data were analyzed using the Statistical Package for the Social Sciences (SPSS) software for Windows, version 26.0, IBM (SPSS Inc., IL, USA), and graphs were designed using GraphPad Prism 8.0 (GraphPad Prism Software Inc., San Diego, CA, USA). Data are presented as mean ± SD, range (minimum and maximum values), or frequencies (number of cases) and percentages when appropriate. Our data was evaluated for normality by Kolmogorov–Smirnov’s test. One-way analysis of variance (ANOVA) for comparisons between more than two groups. To determine specifically which groups differed from one another, a post-hoc Bonferroni test was performed. Correlations between laboratory variables were evaluated using Spearman’s or Pearson’s correlation test. The discriminative ability for RA disease activity was assessed using receiver operating characteristic (ROC) curves, with sensitivity, specificity, area under the curve (AUC), and optimal cutoff values calculated. P-values less than 0.05 were considered statistically significant.

Results

Patient characteristics

The demographics, clinical, serologic, and hematologic parameters of the patients are summarized in Table 1. The mean ± SD DAS28-ESR score was 3.50 ± 1.61. According to the DAS28 score, 19% (n = 59) of the patients were classified as having high disease activity (DAS28 > 5.2), 31% (n = 96) had moderate disease activity (3.2 < DAS28 < 5.1), 15% (n = 45) exhibited low disease activity (2.6 < DAS28 < 3.2), and 35% (n = 105) were in remission (DAS28 < 2.6). The mean ± SD age of patients was 51.7 ± 12.5 years, with nearly 90% of the patients were female. Disease duration ranged from 1 to 640 months, with a mean of 56.7 months. No significant differences were observed among the groups in terms of age, gender, and disease duration (P = 0.71, P = 0.72, and P = 0.66 respectively). However, there were significant differences in the number of tender and swollen joints across the groups (P < 0.001). Further analysis revealed that patients with high activity had significantly more tender and swollen joints compared to those in other groups (all P < 0.001). Similar results were obtained when comparing moderate activity group with the low activity and remission groups (all P < 0.001) (Table 2).

Table 1 Clinical characteristics, demographic information, and laboratory data of patients with rheumatoid arthritis (n = 305)
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Table 2 Post-hoc Bonferroni test was performed to determine specifically which groups differed from one another
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Laboratory findings

Serologic parameters, including ESR, CRP, ANA, RF, and anti-CCP, were compared among the groups. The RF, ANA, and anti-CCP did not show significant differences among the RA groups (P = 0.07, P = 0.09, and P = 0.32, respectively), while CRP and ESR showed significant differences (P = 0.01 and P < 0.001, respectively). Post-Hoc analysis revealed that CRP levels were significantly higher in the high activity RA group compared to the remission group (16.1 ± 2.5 vs. 7.8 ± 1.2, P = 0.02). Additionally, both the high activity and moderate activity RA groups had higher ESR values compared to the remission group (37.2 ± 3.4 vs. 15.2 ± 0.9 and 30.9, ± 2.3 vs. 15.2 ± 0.9, respectively; all P < 0.001). No significant differences were found in the WBC count (P = 0.62), RBC count (P = 0.06), MCV (P = 0.95), MCH (P = 0.74), MCHC (P = 0.64), and monocyte count (P = 0.06) across the groups. However, CBC parameters such as hemoglobin (P = 0.01), hematocrit (P = 0.02), RDW (P = 0.006), MPV (P = 0.003), platelet count (P = 0.009), neutrophil count (P = 0.000), and lymphocyte count (P = 0.02) showed significant differences across the groups. Hemoglobin, hematocrit, and lymphocyte counts were significantly higher in the remission group compared to the highly active group (12.4 ± 0.2 vs. 13.3 ± 0.1, P = 0.007; 37.6 ± 0.6 vs. 39.7 ± 0.5, P = 0.04, 25 ± 1.5 vs. 30 ± 1.1, P = 0.03, respectively). RDW and MPV were significantly higher in the high activity group compared to the remission group (15.7 ± 0.4 vs. 14.2 ± 0.2, P = 0.004; 12.3 ± 1.6 vs. 8.9 ± 0.2, P = 0.002, respectively). Platelet counts were higher in the in the active, moderate, and low activity RA groups compared to the remission group, with the difference being significant only when comparing the remission group compared with the low activity group (299 ± 16.8 vs. 259 ± 8.4, P = 0.01). Neutrophil counts were significantly higher in the high activity group compared to the low activity and remission groups (66 ± 1.5 vs. 59 ± 1.5, P = 0.02; 66 ± 1.5 vs. 58 ± 1.2, P < 0.001, respectively). No significant differences in the mean LMR, PLR, and NLR were found among the RA groups (P = 0.52, P = 0.68, and P = 0.93, respectively). However, the NLR and PLR were higher in the high activity group compared to the remission group (3.6 ± 0.3 vs. 2.8 ± 0.4; 14.8 ± 1.3 vs. 12.1 ± 1.6), though these differences were not statistically significant.

Correlation of LMR, PLR, and NLR levels with ESR, CRP, and DAS28-ESR

Correlations between NLR, MLR, and PLR with CRP, ESR, and DAS28-ESR, the three most widely used indices for disease-activity assessment, were analyzed. NLR was positively correlated with DAS28-ESR (P = 0.0003, R = 0.21, Fig. 1C), but showed no significant correlation with CRP (P = 0.12, R = 0.08, Fig. 1A) and ESR (P = 0.78, R = 0.01, Fig. 1B). PLR was positively correlated with CRP (P = 0.0001, R = 0.22, Fig. 1D), ESR (P = 0.01, R = 0.14, Fig. 1E), and DAS28-ESR (P = 0.0001, R = 0.23, Fig. 1F) scores. LMR showed a significant negative correlation with CRP (P = 0.03, R=-0.12, Fig. 1G), but no significant correlation with ESR (P = 0.58, R = 0.03, Fig. 1H) and DAS28-ESR (P = 0.13, R = 0.08, Fig. 1I).

Fig. 1
figure 1

Spearman’s correlation analysis was applied to estimate relationships in RA patients. The first row shows scatter plot correlations of NLR with CRP (A), ESR (B), and DAS28-ESR scores (C). The second row displays scatter plot correlations of PLR with CRP (D), ESR (E), and DAS28-ESR scores (F). The last row shows scatter plot correlations of LMR with CRP (G), ESR (H), and DAS28-ESR scores (I). Correlation coefficients and P-values are presented in every scatter plot

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ROC analysis

ROC analysis was performed to evaluate the potential of NLR, PLR, LMR, ESR, and CRP as markers for distinguishing active RA from remission. The AUC for ESR was 0.80, with 70% specificity and 75% sensitivity, and a cutoff value of > 19.5 (P < 0.001). The AUC for CRP was 0.63, with 72% specificity and 52% sensitivity, and a cutoff value of > 5.4 (P = 0.004). The AUC for NLR was 0.66, with 81% sensitivity and 49% specificity, and a cutoff value of > 1.85 (P < 0.001). The AUC for PLR was 0.64, with 68% specificity and 61% sensitivity, and a cutoff value of > 10.9 (P = 0.001). However, LMR did not show significant difference between active and inactive RA groups (P = 0.63). These parameters are depicted in Fig. 2 and presented in Table 3.

Fig. 2
figure 2

Receiver operating characteristic (ROC) curves comparing the discriminatory ability of NLR, PLR, LMR, ESR, and CRP indices to distinguish active RA form remission. Sensitivity and 1-specificity values are displayed

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Table 3 ROC curve analysis of NLR, PLR, LMR, ESR and CRP as key factors in differentiating active RA from remission
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Discussion

RA is a chronic autoimmune disease marked by inflammation and joint damage, which leads to reduced quality of life and elevated morbidity and mortality. The primary goal in managing RA is to reduce disease activity and achieve remission, thereby preventing further joint damage and improving patient outcomes [7, 8]. In this study, we aimed to evaluate hematological markers as prognostic tools for assessing disease activity in RA patients.

Currently, markers such as CRP and ESR are used to evaluate inflammation and disease activity in RA patients. However, these markers have limitations and may not accurately reflect disease activity. Studies have reported discrepancies between these markers and imaging techniques, such as magnetic resonance and ultrasound, which can reveal synovial inflammation even when inflammatory markers are normal or low. However, imaging techniques are time-consuming and costly, necessitating the need for reliable and easily measurable biomarkers [14, 39, 40].

This study focused on hematological markers and their association with disease activity in RA patients. Ratios such as the LMR, PLR, and NLR have emerged as potential inflammation biomarkers in various diseases [41,42,43,44]. We investigated these ratios across different RA stages, including high disease activity, moderate activity, low activity, and remission, with the aim of distinguishing highly active RA patients from those in remission.

Our findings showed significant differences in various hematological parameters among the RA groups. Hemoglobin, hematocrit, and lymphocyte counts were significantly higher in the remission group compared to the highly active RA group, indicating a potential association between these parameters and disease activity. Additionally, RDW, MPV, and neutrophil counts were higher in the highly active group compared to the remission group, suggesting alterations in reticulocyte count, platelet activation, and neutrophil production during active disease stages.

We also examined correlations of NLR, PLR, and LMR with commonly used inflammatory marker, including CRP, ESR, and DAS28-ESR score. We showed a significant positive correlations between NLR and DAS28-ESR, which was in line with the study of Remalante et al. [45]. They reported a positive correlation between NLR and DAS-28 and ESR. They concluded that the NLR could be a helpful marker of disease activity and inflammation in RA. In our study, PLR showed significant positive correlations with CRP, ESR, and DAS28-ESR. LMR was significantly negatively correlated with CRP. These findings suggest that NLR and PLR may be valuable in assessing disease activity in RA, as they show significant correlations with established inflammation markers and disease activity scores.

To determine the diagnostic accuracy of these markers, we performed ROC curve analysis. The results showed that ESR had the highest AUC, followed by CRP, NLR, and PLR. ESR demonstrated a good ability to differentiate active from inactive RA, with a sensitivity of 75% and specificity of 70%. While NLR and PLR had lower AUC values, they still demonstrated fair diagnostic performance. These findings indicate that NLR, PLR, and even CRP may potentially serve as supplementary markers for evaluating disease activity in RA patients.

Overall, our study highlights the potential of hematological markers, particularly NLR and PLR as prognostic tools for evaluating disease activity in RA patients. Our result were in line with those of Fawzy et al. [16], who reported a significant correlation between NLR and disease activity indices in RA patients. They also concluded that NLR could serve as a marker for evaluating disease activity. Abd-Elazeem et al. [12], reported that both the NLR and PLR could be used to evaluate disease activity in active RA patients, which was in line with our results. Furthermore, a study by Targońska-Stępniak et al. [35] reported that both NLR and PLR are associated with disease activity parameters and may serve as reliable, inexpensive markers, with prognostic significance in RA. These markers offer a convenient and cost-effective alternative to imaging techniques and can be easily incorporated into routine clinical practice. However, further studies with larger sample sizes and longitudinal follow-up are necessary to validate these findings and establish their clinical utility for guiding treatment decisions and monitoring disease progression in RA.

It is important to note that this study has some limitations. First, the sample size was relatively small, so the results should be interpreted with caution. Second, the cross-sectional design limits our ability to establish causality and assess the predictive value of the markers over time. Longitudinal studies are necessary to evaluate the dynamic changes in these markers and their association with disease progression. Additionally, the study was conducted at a single center, which may limit the generalizability of the findings. Multi-center studies with diverse populations provide more robust evidence.

In conclusion, our study suggests that hematological markers, including NLR and PLR, may serve as valuable supplementary tools for assessing disease activity in RA patients, which was in line with various studies [12, 29, 35, 46]. These markers provide insights into the inflammatory status and can aid in differentiating highly active RA from remission. Incorporating these markers into routine clinical practice may support optimized treatment decisions and improved patient outcomes. Further research is warranted to validate these findings and explore their potential clinical applications in larger cohorts and longitudinal settings.

Data availability

The data that support the findings of this study are available from the corresponding author, Jafar Karami, upon request.

References

  1. Kugyelka R, Kohl Z, Olasz K, Mikecz K, Rauch TA, Glant TT, Boldizsar F. Enigma of IL-17 and Th17 cells in rheumatoid arthritis and in autoimmune animal models of arthritis. Mediators of inflammation. 2016;2016.

  2. Karami J, Masoumi M, Khorramdelazad H, Bashiri H, Darvishi P, Sereshki HA et al. Role of autophagy in the pathogenesis of rheumatoid arthritis: Latest evidence and therapeutic approaches. Life Sci. 2020;254.

  3. Karami J, Aslani S, Jamshidi A, Garshasbi M, Mahmoudi M. Genetic implications in the pathogenesis of rheumatoid arthritis; an updated review. Gene. 2019;702.

  4. Alizadeh Z, Farazmand A, Akhlaghi M, Jamshidi AR, Shahlaee A, Karami J et al. STAT4 rs7574865 polymorphism in Iranian patients with rheumatoid arthritis. Indian J Rheumatol. 2016;11(2).

  5. Karami J, Aslani S, Tahmasebi MN, Mousavi MJ, Sharafat Vaziri A, Jamshidi A et al. Epigenetics in rheumatoid arthritis; fibroblast-like synoviocytes as an emerging paradigm in the pathogenesis of the disease. Immunol Cell Biol. 2020;98(3).

  6. Masoumi M, Mehrabzadeh M, Mahmoudzehi S, Mousavi MJ, Jamalzehi S, Sahebkar A, Karami J. Role of glucose metabolism in aggressive phenotype of fibroblast-like synoviocytes: Latest evidence and therapeutic approaches in rheumatoid arthritis. Int Immunopharmacol. 2020;89.

  7. Masoumi M, Alesaeidi S, Khorramdelazad H, Behzadi M, Baharlou R, Alizadeh-Fanalou S, Karami J. Role of T cells in the pathogenesis of rheumatoid arthritis: focus on immunometabolism dysfunctions. Inflammation. 2023;46(1).

  8. Masoumi M, Hashemi N, Moadab F, Didehdar M, Farahani R, Khorramdelazad H et al. Immunometabolism Dysfunction in the Pathophysiology and Treatment of Rheumatoid Arthritis. Curr Med Chem. 2023;30(27).

  9. Smolen JS, Landewé R, Breedveld FC, Dougados M, Emery P, Gaujoux-Viala C et al. EULAR recommendations for the management of rheumatoid arthritis with synthetic and biological disease-modifying antirheumatic drugs. Ann Rheum Dis. 2010;69(6).

  10. Zochling J, Braun J. Mortality in rheumatoid arthritis and ankylosing spondylitis. Clin Experimental Rheumatol. 2009;27(4).

  11. Masoumi M, Bashiri H, Khorramdelazad H, Barzaman K, Hashemi N, Sereshki HA et al. Destructive roles of fibroblast-like synoviocytes in chronic inflammation and joint damage in rheumatoid arthritis. Inflammation. 2021;44.

  12. Abd-Elazeem MI, Mohamed RA. Neutrophil-lymphocyte and platelet-lymphocyte ratios in rheumatoid arthritis patients: Relation to disease activity. Egypt Rheumatologist. 2018;40(4).

  13. Chandrashekara S, Sachin S. Measures in rheumatoid arthritis: are we measuring too many parameters. Int J Rheum Dis. 2012;15(3).

  14. Orr CK, Najm A, Young F, McGarry T, Biniecka M, Fearon U, Veale DJ. The utility and limitations of CRP, ESR and DAS28-CRP in appraising disease activity in rheumatoid arthritis. Front Med. 2018;5.

  15. Smolen JS, Breedveld FC, Burmester GR, Bykerk V, Dougados M, Emery P et al. Treating rheumatoid arthritis to target: 2014 update of the recommendations of an international task force. Ann Rheum Dis. 2016;75(1).

  16. Fawzy RM, Said EA, Mansour AI. Association of neutrophil to lymphocyte ratio with disease activity indices and musculoskeletal ultrasound findings in recent onset rheumatoid arthritis patients. Egypt Rheumatologist. 2017;39(4).

  17. Ranganath VK, Hammer HB, McQueen FM. Contemporary imaging of rheumatoid arthritis: clinical role of ultrasound and MRI. Best Pract Res Clin Rheumatol. 2020;34(6).

  18. D’Agostino M-A, Terslev L, Aegerter P, Backhaus M, Balint P, Bruyn GA et al. Scoring ultrasound synovitis in rheumatoid arthritis: a EULAR-OMERACT ultrasound taskforce—Part 1: definition and development of a standardised, consensus-based scoring system. RMD open. 2017;3(1).

  19. Lijuan W, Yuting Z, Chaoyang L, Ju Y. Neutrophil-lymphocyte, platelet-lymphocyte and lymphocyte-monocyte ratios may not be useful markers to assess disease activity in rheumatoid arthritis: A STROBE-compliant article. Medicine. 2021;100(45).

  20. Kisacik B, Tufan A, Kalyoncu U, Karadag O, Akdogan A, Ozturk MA et al. Mean platelet volume (MPV) as an inflammatory marker in ankylosing spondylitis and rheumatoid arthritis. Joint Bone Spine. 2008;75(3).

  21. Afsar B. The relationship between neutrophil lymphocyte ratio with urinary protein albumin excretion in newly diagnosed patients with type 2 diabetes. Am J Med Sci. 2014;347(3).

  22. Chen J, Chen M-H, Li S, Guo Y-L, Zhu C-G, Xu R-X et al. Usefulness of the neutrophil-to-lymphocyte ratio in predicting the severity of coronary artery disease: a Gensini score assessment. J Atheroscler Thromb. 2014;21(12).

  23. Wang X, Lou Z, Zhang L, Liu Z, Zhang J, Gao J, Ji Y. Evaluation of the prognostic value of derived neutrophil/lymphocyte ratio in early stage non-small cell lung cancer patients treated with stereotactic ablative radiotherapy. Medicine. 2020;99(42).

  24. Kumano Y, Hasegawa Y, Kawahara T, Yasui M, Miyoshi Y, Matsubara N, Uemura H. Pretreatment neutrophil to lymphocyte ratio (NLR) predicts prognosis for castration resistant prostate cancer patients underwent enzalutamide. BioMed Research International. 2019;2019.

  25. Ren K, Yin Y, He F, Shao Y, Wang S. Prognostic role of derived neutrophil-to-lymphocyte ratio in surgical triple-negative breast cancer. Cancer Manage Res. 2018.

  26. Xiang Y, Zhang N, Lei H, Wu J, Wang W, Zhang H, Zeng X. Neutrophil-to-lymphocyte ratio is a negative prognostic biomarker for luminal A breast cancer. Gland Surg. 2023;12(3).

  27. Bekdas M, Goksugur SB, Sarac EG, Erkocoglu M, Demircioglu F. Neutrophil/lymphocyte and C-reactive protein/mean platelet volume ratios in differentiating between viral and bacterial pneumonias and diagnosing early complications in children. Saudi Med J. 2014;35(5).

  28. Uslu AU, Küçük A, Şahin A, Ugan Y, Yılmaz R, Güngör T et al. Two new inflammatory markers associated with Disease Activity Score-28 in patients with rheumatoid arthritis: neutrophil‐lymphocyte ratio and platelet‐lymphocyte ratio. International journal of rheumatic diseases. 2015;18(7).

  29. Chandrashekara S, Mukhtar Ahmad M, Renuka P, Anupama K, Renuka K. Characterization of neutrophil-to‐lymphocyte ratio as a measure of inflammation in rheumatoid arthritis. Int J Rheum Dis. 2017;20(10).

  30. Yang Z, Zhang Z, Lin F, Ren Y, Liu D, Zhong R, Liang Y. Comparisons of neutrophil-, monocyte‐, eosinophil‐, and basophil‐lymphocyte ratios among various systemic autoimmune rheumatic diseases. Apmis. 2017;125(10).

  31. Erre GL, Paliogiannis P, Castagna F, Mangoni AA, Carru C, Passiu G, Zinellu A. Meta-analysis of neutrophil‐to‐lymphocyte and platelet‐to‐lymphocyte ratio in rheumatoid arthritis. Eur J Clin Invest. 2019;49(1).

  32. Lee H-N, Kim Y-K, Kim G-T, Ahn E, So MW, Sohn DH, Lee S-G. Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratio as predictors of 12-week treatment response and drug persistence of anti-tumor necrosis factor-α agents in patients with rheumatoid arthritis: A retrospective chart review analysis. Rheumatol Int. 2019;39.

  33. Du J, Chen S, Shi J, Zhu X, Ying H, Zhang Y et al. The association between the lymphocyte-monocyte ratio and disease activity in rheumatoid arthritis. Clin Rheumatol. 2017;36.

  34. Arnett FC, Edworthy SM, Bloch DA, Mcshane DJ, Fries JF, Cooper NS et al. The American Rheumatism Association 1987 revised criteria for the classification of rheumatoid arthritis. Arthr Rhuem. 1988;31(3).

  35. Targońska-Stępniak B, Zwolak R, Piotrowski M, Grzechnik K, Majdan M. The relationship between hematological markers of systemic inflammation (neutrophil-to-lymphocyte, platelet-to-lymphocyte, lymphocyte-to-monocyte ratios) and ultrasound disease activity parameters in patients with rheumatoid arthritis. J Clin Med. 2020;9(9).

  36. Tekeoğlu İ, Gürol G, Harman H, Karakeçe E, Çiftçi İH. Overlooked hematological markers of disease activity in rheumatoid arthritis. Int J Rheum Dis. 2016;19(11).

  37. Prevoo M, Van’ Hof T, Kuper MA, Van Leeuwen H, Van De Putte M, Van Riel L. P. Modified disease activity scores that include twenty-eight‐joint counts development and validation in a prospective longitudinal study of patients with rheumatoid arthritis. Arthritis Rheumatism: Official J Am Coll Rheumatol. 1995;38(1).

  38. Ali ET, Jabbar AS, Mohammed AN. A comparative study of interleukin 6, inflammatory markers, ferritin, and hematological profile in rheumatoid arthritis patients with anemia of chronic disease and iron deficiency anemia. Anemia. 2019;2019.

  39. Keenan R, Swearingen C, Yazici Y. Erythrocyte sedimentation rate and C-reactive protein levels are poorly correlated with clinical measures of disease activity in rheumatoid arthritis, systemic lupus erythematosus and osteoarthritis patients. Clin Experimental Rheumatol. 2008;26(5).

  40. Farheen K, Agarwal SK. Assessment of disease activity and treatment outcomes in rheumatoid arthritis. J Managed Care Pharm. 2011;17(9 Supp B).

  41. Li S, Qiu Y, Yu J, Shao M, Li Y, Cao W, Sun X. Association of systemic inflammation indices with visual field loss progression in patients with primary angle-closure glaucoma: potential biomarkers for 3P medical approaches. EPMA J. 2021;12.

  42. Yang A-P, Liu J-p, Tao W-q, Li H-m. The diagnostic and predictive role of NLR, d-NLR and PLR in COVID-19 patients. Int Immunopharmacol. 2020;84.

  43. Mandaliya H, Jones M, Oldmeadow C, Nordman II. Prognostic biomarkers in stage IV non-small cell lung cancer (NSCLC): neutrophil to lymphocyte ratio (NLR), lymphocyte to monocyte ratio (LMR), platelet to lymphocyte ratio (PLR) and advanced lung cancer inflammation index (ALI). Translational lung cancer Res. 2019;8(6).

  44. Gasparyan AY, Ayvazyan L, Mukanova U, Yessirkepov M, Kitas GD. The platelet-to-lymphocyte ratio as an inflammatory marker in rheumatic diseases. Annals Lab Med. 2019;39(4).

  45. Remalante PPM, Salido EO, Penserga EG, Gauiran DTV. Red cell distribution width and neutrophil–lymphocyte ratio in rheumatoid arthritis. Rheumatol Int. 2020;40(10).

  46. Fu H, Qin B, Hu Z, Ma N, Yang M, Wei T, et al. Neutrophil-and platelet-to-lymphocyte ratios are correlated with disease activity in rheumatoid arthritis. Clin Lab. 2015;61:3–4.

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Acknowledgements

We acknowledge support from the Khomein University of Medical Sciences.

Funding

This research project was financially supported by Khomein University of Medical Science (Grant Number: 401000007).

Author information

Authors and Affiliations

  1. Clinical Research and Development Unit, Shahid Beheshti Hospital, Qom University of Medical Sciences, Qom, Iran

    Maryam Masoumi & Maryam Bozorgi

  2. Student Research committee, Faculty of Medicine, Qom University of Medical Sciences, Qom, Iran

    Zahra Nourmohammadi

  3. Department of Hematology, School of Para-Medicine, Bushehr University of Medical Sciences, Bushehr, Iran

    Mohammad Javad Mousavi

  4. Infectious Diseases Research Center (IDRC), Arak University of Medical Sciences, Arak, Iran

    Aref Shariati

  5. Molecular Medicine Research Center, Khomein University of Medical Sciences, Khomein, Iran

    Jafar Karami

  6. Department of Laboratory Sciences, Khomein University of Medical Sciences, Khomein, Iran

    Jafar Karami

Authors
  1. Maryam Masoumi
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  2. Maryam Bozorgi
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  3. Zahra Nourmohammadi
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  4. Mohammad Javad Mousavi
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  5. Aref Shariati
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  6. Jafar Karami
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Contributions

J-K participated in designing the study, analyzing results, and editing the manuscript. M-M contributed to visiting the patients and manuscript editing. M-B, and Z-N were involved in sample collection and performing laboratory tests. A-Sh and MJ-M participated in drafting and editing of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Jafar Karami.

Ethics declarations

Ethics approval and consent to participate

This study was reviewed and approved by Khomein University of Medical Science (approval number: IR.KHOMEIN.REC.1401.002, dated 24 August 2022). All participants were informed of the study design, and written informed consent was obtained from all those who participated in the study.

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Not applicable.

Generative AI and AI-assisted technologies in the writing process

During the preparation of this work the author(s) used ChatGPT to improve discussion section. The authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

Competing interests

The authors declare no competing interests.

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Masoumi, M., Bozorgi, M., Nourmohammadi, Z. et al. Evaluation of hematological markers as prognostic tools in rheumatoid arthritis. BMC Rheumatol 8, 75 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s41927-024-00444-0

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s41927-024-00444-0

Keywords

BMC Rheumatology

ISSN: 2520-1026

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