| Literature DB >> 30809980 |
Armen Yuri Gasparyan1, Lilit Ayvazyan2, Ulzhan Mukanova3, Marlen Yessirkepov4, George D Kitas5,6.
Abstract
The platelet-to-lymphocyte ratio (PLR) has emerged as an informative marker revealing shifts in platelet and lymphocyte counts due to acute inflammatory and prothrombotic states. PLR has been extensively examined in neoplastic diseases accompanied by immune suppression and thrombosis, which can be predicted by combined blood cell counts and their ratios. Several large observational studies have demonstrated the value of shifts in PLR in evaluating the severity of systemic inflammation and predicting infections and other comorbidities, in inflammatory rheumatic diseases. The value of PLR as an inflammatory marker increases when its fluctuations are interpreted along with other complementary hematologic indices, particularly the neutrophil-to-lymphocyte ratio (NLR), which provides additional information about the disease activity, presence of neutrophilic inflammation, infectious complications, and severe organ damage in systemic lupus erythematosus. PLR and NLR have high predictive value in rheumatic diseases with predominantly neutrophilic inflammation (e.g., Behçet disease and familial Mediterranean fever). High PLR, along with elevated platelet count, is potentially useful in diagnosing some systemic vasculitides, particularly giant-cell arteritis. A few longitudinal studies on rheumatic diseases have demonstrated a decrease in PLR in response to anti-inflammatory therapies. The main limitations of PLR studies are preanalytical faults, inadequate standardization of laboratory measurements, and inappropriate subject selection. Nonetheless, accumulating evidence suggests that PLR can provide valuable information to clinicians who encounter multisystem manifestations of rheumatic diseases, which are reflected in shifts in platelet, lymphocyte, neutrophil, or monocyte counts. Interpretation of PLR combined with complementary hematologic indices is advisable to more accurately diagnose inflammatory rheumatic diseases and predict related comorbidities. © The Korean Society for Laboratory Medicine.Entities:
Keywords: Inflammation; Lymphocyte count; Markers; Neutrophil count; Platelet count; Platelet-to-lymphocyte ratio; Rheumatic diseases
Mesh:
Substances:
Year: 2019 PMID: 30809980 PMCID: PMC6400713 DOI: 10.3343/alm.2019.39.4.345
Source DB: PubMed Journal: Ann Lab Med ISSN: 2234-3806 Impact factor: 3.464
Fig. 1Number of Scopus-indexed articles tagged with the term “platelet lymphocyte ratio” in 2008–2018 (as of October 30, 2018).
Studies on PLR and other laboratory indices in adults with inflammatory rheumatic diseases
| Patients | N | Design | Indices | Results | Notes | Ref. |
|---|---|---|---|---|---|---|
| Newly diagnosed, drug-naïve RA patients without comorbidities | 104 | Case-control | Plt, Neutr, Lymph counts, PLR, NLR, CRP, RF | Plt and Neutr counts, PLR, and NLR were significantly ↑, and Lymph count was significantly ↓ in patients with RA compared with healthy controls. In an adjusted stepwise logistic regression analysis PLR was associated with RA (OR, 1.026, 95% CI, 1.007–1.045). ROC curve analyses revealed predictive value of PLR: AUC, 0.847, sensitivity, 82.5%, sensitivity, 74.8%, 95% CI, 0.794–0.901. | PLR, but not NLR, is associated with RA. | Peng et al. [ |
| Patients with active RA without comorbidities | 128 | Case-control | Plt, Neutr, Lymph counts, PLR, NLR, CRP, ESR | Plt, Neutr counts, PLR, and NLR were significantly ↑ in RA. Lymph count was significantly ↓. PLR and NLR were positively correlated with CRP and DAS28. | Both PLR and NLR reflect activity of RA. | Fu et al. [ |
| Patients with RA with low (<2.6) and high DAS28 (>2.6) without comorbidities and corticosteroid-naive | 104 | Case-control | Plt, Neutr, Lymph counts, PLR, NLR, CRP, ESR | Neutr count, PLR, and NLR were significantly ↑ in RA. PLR and NLR were positively correlated with DAS28. | Both PLR and NLR are markers of RA activity. | Uslu et al. [ |
| Patients with active and non-active RA | 125 | Case-control | Plt, Neutr, Lymph counts, PLR, NLR, CRP, ESR | Plt, Lymph, Neutr counts were not different between RA and healthy control groups. PLR and NLR were significantly ↑ in RA. PLR and NLR were positively correlated with ESR. ROC curve analyses revealed better predictive value of the combined NLR-PLR curves for distinguishing patients with active RA from controls: AUC for NLR, 0.813, 95% CI, 0.739–0.888, for PLR, 0.762, 95% CI 0.673–0.85, and for NLR-PLR, 0.88, 95% CI, 0.819–0.94. | Combined analyses of PLR and NLR are valuable for detecting activity of RA. | Zhang et al. [ |
| Drug-naïve patients without comorbidities with mild (SLEDAI − 2K<9) and severe SLE (SLEDAI − 2K >9) | 116 | Case-control | Plt, Neutr, Lymph counts, PLR, NLR, CRP, ESR | Plt, Neutr, Lymph counts were significantly ↓, whereas PLR and NLR were significantly ↑ in patients with SLE compared with healthy controls. Both PLR and NLR were positively correlated with SLEDAI-2K (r=0.298, P<0.001 and r=0.312, P<0.001, respectively). NLR, but not PLR, correlated with C3 (r=-0.218, P<0.05) and C4 (r=-0.211, P<0.05). Patients with nephritis had a significantly higher NLR (P=0.027). The ROC curve analyses revealed that PLR had 42% sensitivity and 84% specificity to predict high lupus activity. | Both PLR and NLR reflect activity of SLE. High NLR reflects nephritis. | Wu et al. [ |
| Drug-naive patients with SLE without comorbidities | 154 | Case-control | Plt, Neutr, Lymph counts, PLR, NLR, MPV, CRP, ESR | Neutr count was ↑, whereas Lymph and Plt counts were ↓ in patients with SLE compared with healthy controls. PLR, NLR, and MPV were significantly ↑ in SLE. Both PLR and NLR positively correlated with SLEDAI and were significantly higher in patients with lupus nephritis. ROC curve analysis revealed that NLR had the best sensitivity (71%) and specificity (64%), with a cut-off value of 2.664 (AUC, 0.715, 95% CI, 0.616–0.787) for predicting lupus nephritis. | Both PLR and NLR reflect activity of SLE. NLR is more valuable than PLR for predicting nephritis. | Qin et al. [ |
| Patients with SLE with flares and concurrent infections | 120 | Cohort (flares vs infections) | Plt, Neutr, Mon, Lymph counts, PLR, NLR, MLR, CRP, ESR | Plt counts were not different between SLE groups. Neutr count, NLR, and PLR were significantly ↑ in patients with infections. ROC curve analyses showed that NLR (cut-off value of 5.7) had 75% sensitivity and 90% specificity for predicting infections. | NLR is a tool for distinguishing infections from flares in SLE. | Kim et al. [ |
| Patients with AS without comorbidities | 148 | Case-control | Plt, Neutr, Mon, Lymph, Erythr counts, PLR, NLR, MLR, RDW, CRP, ESR | Plt, Neutr, Mon counts, PLR, NLR, MLR were ↑, whereas Lymph count was ↓ in AS. Only MLR and RDW were significantly different between patients with AS and nonradiographic axial spondyloarthritis. ROC curve analyses revealed the highest AUC of 0.768 for MLR in AS (cut-off value of 0.22, 95% CI 0.696–0.839, 71% sensitivity, 68% specificity). | MLR is a marker of severe axial AS. | Huang et al. [ |
| Drug-naïve patients with Ps and PsA without comorbidities | 136 | Case-control | Plt, Neutr, Lymph, Mon, Eos, Bas counts, PLR, NLR, ESR, CRP | Plt, Neutr, Mon counts, and NLR were significantly ↑ in PsA compared with Ps and healthy controls. Multivariate binary logistic regression analysis showed that NLR is the strongest predictor of PsA: OR, 3.351, 95% CI, 1.785–6.292 and 1.012, 1.003–1.021 (NLR vs. PLR). Based on ROC curve analysis, the optimal cut-off value of NLR for predicting arthritis was 2.274 (79% sensitivity and 71% specificity). | NLR is the strongest predictor of PsA. | Kim et al. [ |
| Drug-naïve patients with BD without comorbidities | 254 | Case-control | Plt, Neutr, Lymph counts, PLR, NLR, MPV | Plt, Neutr counts, PLR, NLR were significantly ↑ in BD compared with healthy controls. PLR and MPV were significantly different among patients with mild, moderate, and severe activity (P<0.037 and P<0.016, respectively). However, PLR, NLR and MPV were not different in patients with and without organ involvement and venous thrombosis. Binary regression analyses revealed that NLR is independently associated with BD (OR, 2.5, 95% CI, 1.9–3.3). | NLR is associated with BD. | Alan et al. [ |
| Drug-naive patients with BD without comorbidities | 140 | Case-control | PLR, NLR, LMR, RDW, MPV, ESR, CRP | PLR, NLR, PDW were ↑, whereas LMR, MPV were ↓ in BD patients compared with healthy controls. PLR and NLR were significantly higher in active BD than in inactive BD. ROC curve analysis distinguished PLR as the best predictor of BD (AUC 0.753, 64% sensitivity, 78% specificity, cut-off value of 124.63). | PLR is a marker of BD activity. | Jiang et al. [ |
| Patients with inactive BD with and without anterior uveitis | 140 | Case-control | Plt, Neutr, Lymph counts, PLR, NLR, MPV | Plt counts were not different between BD patients and healthy controls. Neutr count was significantly ↑, whereas Lymph count and MPV were significantly ↓ in patients with uveitis. Patients with uveitis had the highest values of PLR (P=0.04) and NLR (P<0.001) compared with healthy controls and patients without uveitis. Based on ROC curve analyses, NLR predicted uveitis (AUC, 0.725 95% CI, 0.653–0.797) better than other markers | . NLR is a marker of anterior uveitis in BD. | Avci et al. [ |
| Patients undergoing temporal artery biopsy for suspected GCA | 537 | Cohort | Plt, Neutr, Lymph, Mon counts, PLR, NLR, MLR, ESR, CRP | Biopsy suggestive of GCA was recorded in 126 patients (23.5%). High Plt count (>400×109/L) was observed in 49% of patients with GCA and in 18% of patients without GCA (P<0.0001). PLR and NLR were significantly ↑ in patients with GCA. Multivariate analysis revealed that thrombocytosis predicts biopsy suggestive of GCA (OR, 3.187, 95% CI, 1.721–5.902, P<0.001). | High Plt count, PLR, NLR, ESR, CRP aid in the diagnostic work-up of GCA. | Oh et al. [ |
| Drug-naïve or newly- diagnosed patients with TA without comorbidities | 88 | Case-control | Plt, Neutr, Lymph counts, PLR, NLR, RDW, MPV, ESR, CRP | Neutr counts, PLR, NLR, ESR, and CRP were significantly ↑ in TA patients compared with healthy controls. Both PLR and NLR positively correlated with ESR and CRP. ROC curve analyses showed that PLR predicted TA with 34% sensitivity and 93% specificity (cut-off of 183.39, AUC 0.691, 95% CI, 0.58–0.802). For NLR, a cut-off threshold of 2.417 was predictive for TA, with 76% sensitivity and 56% specificity (AUC, 0.697, 95% CI, 0.588–0.806). | PLR and NLR reflect vasculitis activity. | Pan et al. [ |
| Drug-naive newly diagnosed patients with ANCA-associated vasculitis | 163 | Cohort | Plt, Lymph counts, PLR, ESRCRP | Patients with severe vasculitis (BVAS >16) had significantly ↑ Plt count and PLR compared to those with low vasculitis activity. Patients with pulmonary and renal manifestations frequently presented with high PLR (>272). Only PLR ≥272 was independently associated with severe vasculitis in multivariate binary logistic regression analysis (OR 2.734, 95% CI, 1.247–5.993). | PLR is a marker of vasculitis activity. | Park et al. [ |
Abbreviations: PLR, platelet-to-lymphocyte ratio; RA, rheumatoid arthritis; Plt; platelet; Neutr, neutrophil; Lymph, lymphocyte; NLR, neutrophil-to-lymphocyte ratio; ↑, increased; ↓, decreased; CRP, C-reactive protein; RF, rheumatoid factor; OR, odds ratio; AUC, area under the curve; CI, confidence interval; ESR, erythrocyte sedimentation rate; DAS28, disease activity score in 28 joints; SLE, systemic lupus erythematosus; SLEDAI-2K, SLE disease activity index 2000; Mon, monocyte; MLR, monocyte-to-lymphocyte ratio; AS, ankylosing spondylitis; Erythr, erythrocyte; RDW, red cell distribution width; Ps, psoriasis; PsA, psoriatic arthritis; Eos, eosinophil; Bas, basophil; BD, Behçet disease; MPV, mean platelet volume; LMR, lymphocyte-to-monocyte ratio; PDW, platelet distribution width; GCA, giant-cell arteritis; TA, Takayasu arteritis; ANCA, anti-neutrophil cytoplasmic antibodies; BVAS, Birmingham vasculitis activity score.
Main limitations of studies on the platelet-to-lymphocyte ratio
| • Confounding effects of corticosteroids and other anti-rheumatic drugs that affect blood cell counts are sometimes overlooked. |
| • Results of retrospective studies tell nothing about causal relationships of shifts in PLR. |
| • Single measurements of laboratory parameters do not reflect their dynamics. |
| • Long duration of subjects' enrollment (more than three to six months) requires correction for seasonal variability of hemogram indexes. |
| • Specificity of the shifts in PLR is often overlooked in patients with long-standing diseases who may suffer from occult progressive vascular, metabolic, autoimmune, and neoplastic comorbidities. |
| • Molecular markers of activation of platelets and other blood cells are not measured to reveal associations with cellular markers of inflammation. |
| • Preanalytical faults with inadequate anticoagulation of blood samples and use of ethylenediaminetetraacetic acid may result in |
| • There are no race-, age-, and sex-specific recommendations for the use of hemograms to date. |