Skip to main content

Association of simple hematological parameters with disease manifestations, activity, and severity in patients with systemic sclerosis

Abstract

Introduction/objectives

Neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), mean platelet volume (MPV), and red cell distribution width (RDW) may potentially reflect inflammatory status in systemic autoimmune diseases. The aim of this study is to investigate the association between these proposed markers and disease manifestations, activity, and severity in systemic sclerosis (SSc).

Method

We conducted a cross-sectional study of 69 systemic sclerosis (SSc) patients and 50 healthy volunteers in a single center. Adult patients with SSc and healthy controls were compared in terms of NLR, MLR, MPV, RDW, erythrocyte sedimentation rate (ESR), and C-reactive protein (CRP). Venous blood samples were drawn after at least 8 h of fasting in the morning. Extension of skin fibrosis was evaluated by using modified Rodnan skin score (mRSS). Disease severity and activity were assessed by Medsger disease severity and European Scleroderma Trials and Research Group (EUSTAR) disease activity scores, respectively. Associations of disease manifestations, clinical, laboratory, and capillaroscopic findings, mRSS, and the disease activity and severity scores with the proposed hematological markers were evaluated. Multiple regression models were generated for significant associations.

Results

The neutrophil number was higher (p = 0.004) and lymphocyte number was lower (p < 0.001) in SSc group compared to controls. SSc group also had higher NLR, MLR, and RDW. In multiple logistic regression, only the NLR (regression coefficient = 3.49, p = 0.031) and CRP (regression coefficient = 0.17, p = 0.037) remained significantly different between SSc and healthy control groups (Cox and Snell R2 = 0.243, Nagelkerke R2 = 0.337, p < 0.001). NLR and MLR positively correlated with mRSS, EUSTAR score, and CRP. MLR also positively correlated with Medsger score. Higher monocyte counts independently predicted higher EUSTAR and Medsger scores in multiple linear regressions. Patients with digital ulcers had higher NLR and MLR. We did not find any difference in MPV values between SSc and healthy control groups.

Conclusions

Globally available and inexpensive hematological tests, particularly the NLR and MLR, may be associated with vascular and cutaneous manifestations as well as disease activity and severity in SSc.

Key Points
• Monocyte count itself independently predicted higher activity and severity scores in SSc.
• Globally available and inexpensive hematological markers, particularly the NLR and MLR, may have an association with vascular and cutaneous manifestations as well as disease activity and severity in patients with SSc.

This is a preview of subscription content, access via your institution.

References

  1. 1.

    Mayes MD, Lacey JV, Beebe-Dimmer J, Gillespie BW, Cooper B, Laing TJ, Schottenfeld D (2003) Prevalence, incidence, survival, and disease characteristics of systemic sclerosis in a large US population. Arthritis Rheumatol 48:2246–2255

    Google Scholar 

  2. 2.

    Bryan C, Knight C, Black C, Silman A (1999) Prediction of five-year survival following presentation with scleroderma: development of a simple model using three disease factors at first visit. Arthritis Rheumatol 42:2660–2665

    CAS  Google Scholar 

  3. 3.

    Czirják L, Kumánovics G, Varjú C, Nagy Z, Pákozdi A, Szekanecz Z et al (2008) Survival and causes of death in 366 Hungarian patients with systemic sclerosis. Ann Rheum Dis 67:59–63

    PubMed  Google Scholar 

  4. 4.

    Joven BE, Almodovar R, Carmona L, Carreira PE (2009) Survival, causes of death, and risk factors associated with mortality in Spanish systemic sclerosis patients: results from a single university hospital. Semin Arthritis Rheum 39:285–293

    PubMed  Google Scholar 

  5. 5.

    Yamane K, Ihn H, Asano Y, Yazawa N, Kubo M, Kikuchi K, Soma Y, Tamaki K (2000) Clinical and laboratory features of scleroderma patients with pulmonary hypertension. Rheumatology (Oxford) 39:1269–1271

    CAS  Google Scholar 

  6. 6.

    Qin B, Ma N, Tang Q, Wei T, Yang M, Fu H, Hu Z, Liang Y, Yang Z, Zhong R (2016) Neutrophil to lymphocyte ratio (NLR) and platelet to lymphocyte ratio (PLR) were useful markers in assessment of inflammatory response and disease activity in SLE patients. Mod Rheumatol 26:372–376

    PubMed  Google Scholar 

  7. 7.

    Wu Y, Chen Y, Yang X, Chen L, Yang Y (2016) Neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) were associated with disease activity in patients with systemic lupus erythematosus. Int Immunopharmacol 36:94–99

    CAS  PubMed  Google Scholar 

  8. 8.

    Hu Z-D, Sun Y, Guo J, Huang Y-L, Qin B-D, Gao Q, Qin Q, Deng AM, Zhong RQ (2014) Red blood cell distribution width and neutrophil/lymphocyte ratio are positively correlated with disease activity in primary Sjögren's syndrome. Clin Biochem 47:287–290

    CAS  PubMed  Google Scholar 

  9. 9.

    Mercan R, Bitik B, Tufan A, Bozbulut UB, Atas N, Ozturk MA, Haznedaroglu S, Goker B (2016) The association between neutrophil/lymphocyte ratio and disease activity in rheumatoid arthritis and ankylosing spondylitis. J Clin Lab Anal 30:597–601

    CAS  PubMed  Google Scholar 

  10. 10.

    Fu H, Qin B, Hu Z, Ma N, Yang M, Wei T, Tang Q, Huang Y, Huang F, Liang Y, Yang Z, Zhong R (2015) Neutrophil-and platelet-to-lymphocyte ratios are correlated with disease activity in rheumatoid arthritis. Clin Lab 61:269–273

    PubMed  Google Scholar 

  11. 11.

    Moodley D, Mody GM, Chuturgoon AA (2011) Initiation but no execution-modulation of peripheral blood lymphocyte apoptosis in rheumatoid arthritis-a potential role for heat shock protein 70. J Inflamm 8:30

    CAS  Google Scholar 

  12. 12.

    Lescoat A, Lecureur V, Roussel M, Sunnaram BL, Ballerie A, Coiffier G, Jouneau S, Fardel O, Fest T, Jégo P (2017) CD16-positive circulating monocytes and fibrotic manifestations of systemic sclerosis. Clin Rheumatol 36:1649–1654

    PubMed  Google Scholar 

  13. 13.

    Martin J, Shaw T, Heggie J, Penington D (1983) Measurement of the density of human platelets and its relationship to volume. Br J Haematol 54:337–352

    CAS  PubMed  Google Scholar 

  14. 14.

    Martin J, Bath P, Burr M (1991) Influence of platelet size on outcome after myocardial infarction. Lancet 338:1409–1411

    CAS  PubMed  Google Scholar 

  15. 15.

    Berger JS, Eraso LH, Xie D, Sha D, Mohler ER (2010) Mean platelet volume and prevalence of peripheral artery disease, the National Health and Nutrition Examination Survey, 1999–2004. Atherosclerosis 213:586–591

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Soydinc S, Turkbeyler IH, Pehlivan Y, Soylu G, Goktepe MF, Bilici M, Zengin O, Kisacik B, Onat AM (2014) Mean platelet volume seems to be a valuable marker in patients with systemic sclerosis. Inflammation 37:100–106

    PubMed  Google Scholar 

  17. 17.

    Forhecz Z, Gombos T, Borgulya G et al (2009) Red cell distribution width in heart failure: prediction of clinical events and relationship with markers of ineffective erythropoiesis, inflammation, renal function, and nutritional state. Am Heart J 158:659–666

    PubMed  Google Scholar 

  18. 18.

    Lippi G, Targher G, Montagnana M, Salvagno GL, Zoppini G, Guidi GC (2009) Relation between red blood cell distribution width and inflammatory biomarkers in a large cohort of unselected outpatients. Arch Pathol Lab Med 133:628–632

    CAS  PubMed  Google Scholar 

  19. 19.

    Ujszaszi A, Molnar MZ, Czira ME, Novak M, Mucsi I (2013) Renal function is independently associated with red cell distribution width in kidney transplant recipients: a potential new auxiliary parameter for the clinical evaluation of patients with chronic kidney disease. Br J Haematol 161:715–725

    CAS  PubMed  Google Scholar 

  20. 20.

    Ozcan F, Turak O, Durak A et al (2013) Red cell distribution width and inflammation in patients with non-dipper hypertension. Blood Press 22:80–85

    PubMed  Google Scholar 

  21. 21.

    Felker GM, Adams KF Jr, Gattis WA, O’Connor CM (2004) Anemia as a risk factor and therapeutic target in heart failure. J Am Coll Cardiol 44:959–966

    PubMed  Google Scholar 

  22. 22.

    Montagnana M, Cervellin G, Meschi T, Lippi G (2011) The role of red blood cell distribution width in cardiovascular and thrombotic disorders. Clin Chem Lab Med 50:635–641

    PubMed  Google Scholar 

  23. 23.

    Zhao J, Mo H, Guo X, Wang Q, Xu D, Hou Y, Tian Z, Liu Y, Wang H, Lai J, Li M, Zeng X (2018) Red blood cell distribution width as a related factor of pulmonary arterial hypertension in patients with systemic sclerosis. Clin Rheumatol 37:979–985

    PubMed  Google Scholar 

  24. 24.

    Farkas N, Szabó A, Lóránd V, Sarlós DP, Minier T, Prohászka Z et al (2014) Clinical usefulness of measuring red blood cell distribution width in patients with systemic sclerosis. Rheumatology (Oxford) 53:1439–1445

    CAS  Google Scholar 

  25. 25.

    Hoogen F, Khanna D, Fransen J, Johnson SR, Baron M, Tyndall A et al (2013) 2013 classification criteria for systemic sclerosis: an American College of Rheumatology/European League against rheumatism collaborative initiative. Arthritis Rheumatol 65:2737–2747

    Google Scholar 

  26. 26.

    LeRoy EC (1988) Scleroderma (systemic sclerosis): classification, subsets and pathogenesis. J Rheumatol 15:202–206

    CAS  PubMed  Google Scholar 

  27. 27.

    Clements P, Lachenbruch P, Siebold J, White B, Weiner S, Martin R, Weinstein A, Weisman M, Mayes M, Collier D (1995) Inter and intraobserver variability of total skin thickness score (modified Rodnan TSS) in systemic sclerosis. J Rheumatol 22:1281–1285

    CAS  PubMed  Google Scholar 

  28. 28.

    Cutolo M, Sulli A, Pizzorni C, Accardo S (2000) Nailfold videocapillaroscopy assessment of microvascular damage in systemic sclerosis. J Rheumatol 27:155–160

    CAS  PubMed  Google Scholar 

  29. 29.

    Valentini G, Iudici M, Walker UA, Jaeger VK, Baron M, Carreira P, Czirják L, Denton CP, Distler O, Hachulla E, Herrick AL, Kowal-Bielecka O, Pope J, Müller-Ladner U, Riemekasten G, Avouac J, Frerix M, Jordan S, Minier T, Siegert E, Ong VH, Vettori S, Allanore Y (2017) The European Scleroderma Trials and Research Group (EUSTAR) task force for the development of revised activity criteria for systemic sclerosis: derivation and validation of a preliminarily revised EUSTAR activity index. Ann Rheum Dis 76:270–276

    CAS  PubMed  Google Scholar 

  30. 30.

    Medsger T, Bombardieri S, Czirjak L, Scorza R, Rossa A, Bencivelli W (2003) Assessment of disease severity and prognosis. Clin Exp Rheumatol 21(3 SUPP/29):S42–S46

    PubMed  Google Scholar 

  31. 31.

    Uslu AU, Küçük A, Şahin A, Ugan Y, Yılmaz R, Güngör T, Bağcacı S, Küçükşen S (2015) Two new inflammatory markers associated with disease activity score-28 in patients with rheumatoid arthritis: neutrophil-lymphocyte ratio and platelet-lymphocyte ratio. Int J Rheum Dis 18:731–735

    PubMed  Google Scholar 

  32. 32.

    Barnes TC, Spiller DG, Anderson ME, Edwards SW, Moots RJ (2010) Endothelial activation and apoptosis mediated by neutrophil-dependent interleukin 6 trans-signalling: a novel target for systemic sclerosis? Ann Rheum Dis 70:366–372

    PubMed  PubMed Central  Google Scholar 

  33. 33.

    Ziegler-Heitbrock L, Ancuta P, Crowe S, Dalod M, Grau V, Hart DN, Leenen PJM, Liu YJ, MacPherson G, Randolph GJ, Scherberich J, Schmitz J, Shortman K, Sozzani S, Strobl H, Zembala M, Austyn JM, Lutz MB (2010) Nomenclature of monocytes and dendritic cells in blood. Blood 116:e74–e80

    CAS  PubMed  Google Scholar 

  34. 34.

    Xiang F, Chen R, Cao X, Shen B, Liu Z, Tan X, Ding X, Zou J (2018) Monocyte/lymphocyte ratio as a better predictor of cardiovascular and all-cause mortality in hemodialysis patients: a prospective cohort study. Hemodial Int 22:82–92

    PubMed  Google Scholar 

  35. 35.

    Yang Z, Zhang Z, Lin F, Ren Y, Liu D, Zhong R, Liang Y (2017) Comparisons of neutrophil-, monocyte-, eosinophil-, and basophil-lymphocyte ratios among various systemic autoimmune rheumatic diseases. APMIS 125:863–871

    CAS  PubMed  Google Scholar 

  36. 36.

    Muangchan C, Harding S, Khimdas S, Bonner A, Canadian Scleroderma Research Group, Baron M, Pope J (2012) Association of C-reactive protein with high disease activity in systemic sclerosis: results from the Canadian Scleroderma Research Group. Arthritis Care Res (Hoboken) 64:1405–1414

    CAS  Google Scholar 

  37. 37.

    Liu X, Mayes MD, Pedroza C, Draeger HT, Gonzalez EB, Harper BE, Reveille JD, Assassi S (2013) Does C-reactive protein predict the long-term progression of interstitial lung disease and survival in patients with early systemic sclerosis? Arthritis Care Res (Hoboken) 65:1375–1380

    Google Scholar 

  38. 38.

    Coppinger JA, Cagney G, Toomey S, Kislinger T, Belton O, McRedmond JP et al (2004) Characterization of the proteins released from activated platelets leads to localization of novel platelet proteins in human atherosclerotic lesions. Blood 103:2096–2104

    CAS  PubMed  Google Scholar 

  39. 39.

    Gawaz M, Langer H, May AE (2005) Platelets in inflammation and atherogenesis. J Clin Invest 115:3378–3384

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Greisenegger S, Endler G, Hsieh K, Tentschert S, Mannhalter C, Lalouschek W (2004) Is elevated mean platelet volume associated with a worse outcome in patients with acute ischemic cerebrovascular events? Stroke 35:1688–1691

    CAS  PubMed  Google Scholar 

  41. 41.

    Kiliçli-Çamur N, Kiliçli-Camur N, Konuralp C, Eskiser A (2005) Could mean platelet volume be a predictive marker for acute myocardial infarction? Med Sci Monit 11:CR387–CR392

    PubMed  Google Scholar 

  42. 42.

    Şahin A, Yetişgin A, Şahin M, Durmaz Y, Cengiz A (2015) Can mean platelet volume be a surrogate marker of inflammation in rheumatic diseases? West Indian Med J 65:165–169

    PubMed  Google Scholar 

  43. 43.

    Yolbas S, Yildirim A, Gozel N, Uz B, Koca SS (2016) Hematological indices may be useful in the diagnosis of systemic lupus erythematosus and in determining disease activity in Behcet's disease. Med Princ Pract 25:510–516

    PubMed  PubMed Central  Google Scholar 

  44. 44.

    Rezende SM, Lijfering WM, Rosendaal FR, Cannegieter SC (2014) Hematologic variables and venous thrombosis: red cell distribution width and blood monocyte count are associated with an increased risk. Haematologica 99:194–200

    PubMed  PubMed Central  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Müçteba Enes Yayla.

Ethics declarations

Disclosures

None.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Yayla, M.E., İlgen, U., Okatan, İ.E. et al. Association of simple hematological parameters with disease manifestations, activity, and severity in patients with systemic sclerosis. Clin Rheumatol 39, 77–83 (2020). https://doi.org/10.1007/s10067-019-04685-0

Download citation

Keywords

  • Complete blood count
  • MLR
  • Monocyte-to-lymphocyte ratio
  • Neutrophil-to-lymphocyte ratio
  • NLR
  • Scleroderma
  • Systemic sclerosis