Low serum C3 level, high neutrophil-lymphocyte-ratio, and high platelet-lymphocyte-ratio all predicted poor long-term renal survivals in biopsy-confirmed idiopathic membranous nephropathy

Article metrics

  • 503 Accesses

Abstract

Idiopathic membranous nephropathy (iMN) is the major cause of end-stage renal disease (ESRD). Recent guidelines suggest limiting immunosuppressants only to high risk patients for ESRD. The present study is aimed at identifying new predictors for the renal outcome of iMN patients. We conducted a retrospective cohort study covering a period from January 2003 to December 2013. We enrolled participants who had received their first renal biopsy at our medical center in Taiwan with the diagnosis of iMN. Clinical, pathological and laboratory data were collected from medical records. Analyses with Mann–Whitney U test was used for continuous variables and Chi-square test for categorical variables. The Kaplan-Meier curve was used for the analyses of patient survival and renal survival. Youden index was used for evaluating the performance of a dichotomous diagnostic test for renal and patient outcomes. Cox proportional hazard regression was used to determine factors affecting renal survival.A total of 99 patients with renal biopsy-confirmed idiopathic iMNs were enrolled. C3 level ≤114 mg/dl predicted patient outcome (p < 0.001) with good predictive power (AUC = 0.736). The univariate analysis showed that risk factors for poor renal outcome were older age (HR = 1.04, p = 0.002), high BUN (HR = 1.03, p < 0.001), poor baseline renal function (HR = 1.30 and p < 0.001 for higher serum creatinine; HR = 0.97 and p < 0.001 for higher eGFR; HR = 1.06 and p < 0.001 for urine PCR), C3 ≤ 93.4 mg/dl (HR = 2.15, p = 0.017), NLR > 3.34 (HR = 3.30, p < 0.001) and PLR > 14.48 (HR = 2.54, p = 0.003). Stage of iMN did not fully account for the risk of ESRD. This is the first evidence that serum levels of C3 ≤ 93.4 mg/dl predicted poor renal outcomes with good predictive power. Easily obtained markers, NLR > 3.34 also predicted poor renal outcomes.

Introduction

Idiopathic membranous nephropathy (iMN) is the major cause of nephrotic syndrome in adults1. Its common pathological finding is the presence of subepithelial deposition over glomerular basement membrane (GBM). The outcome is heterogeneous, spreading from a self-limited status to an end-stage renal disease (ESRD). In addition, the mainstream treatment for iMN is immunosuppressants. However, there is no consensus among medical professionals regarding the timing, types and doses of immunosuppressants. Recently, published in Kidney Disease Improving Global Outcomes (KDIGO) have suggested alkylating agents to be used only in patients with the highest risk for kidney failure in order to avoid unnecessary complications, like infections2. Therefore, the identification of the risk factors for renal outcome of iMN is needed.

Toronto Risk Score was proposed in 1997 to predict the renal outcome in patients of iMN3. It is based on three parameters: (a) time-averaged proteinuria (highest sustained 6-month period of proteinuria), (b) creatinine clearance (CCr) at diagnosis, and (c) the slope of CCr over 6 months. There is however no long-term follow-up study published for this model. In addition, no validation of this model has been made to include other other non-western races. Increasingly more markers (like including urinaryβ2-microglobulin orα1-microglobulin4,5, and anti- phospholipase A2 receptor antibody (anti-PLA2R)6 have been developed to strengthen the predictive power. Furthermore, the knowledge of molecular pathogenesis of iMN is actually rare. Recently, differentially expressed microRNAs and their downstream network may be involved in iMN pathogenesis and could be considered as potential diagnostic biomarkers of MGN7. Until now, none of the above approaches is perfect, and the outcome of iMN remains poorly predicted. Therefore, other factors responsible for predicting the outcome of iMN needed to be discovered. Here, we conducted a long-term retrospective study on such patients and analyzed the risk factors for their renal outcomes.

Materials and Methods

Study population

We conducted a retrospective cohort study covering the period from January 2003 to December 2013. We enrolled participants of ages >20 years and who had received a first renal biopsy for their diagnoses of iMN in our medical center. Graft renal biopsies were excluded. In this medical center, we had the largest population of renal biopsies (>8000 times over 30 years) in Taiwan. We collected patients with the diagnosis of iMN, instead of secondary MN. All recruited patients also did not have other autoimmune disease and infection disease. This study was approved by Ethics Committee of Taichung Veterans General Hospital, IRB number: CE15125B. All methods were carried out in accordance with relevant guidelines and regulations and informed consent was obtained from all subjects.

Data Collection and outcome assessment

All data were obtained through our reviewing of medical records. At the time of renal biopsy, baseline data were collected. They included the following: gender, age, body height (cm), body weight (kg), and systolic or diastolic blood pressure (SBP and DBP). From blood samples the following data were also collected: blood urea nitrogen (BUN) (mg/dl), serum creatinine (mg/dl), estimated glomerular filtration rate (eGFR from MDRD equation8) (ml/min.1.732 m2), bloodwhite blood cell (WBC) (/cumm), blood red blood cell (RBC) (/cumm), hemoglobin (g/dl), neutral and lymphocyte ratio (%), platelet count (/cumm), uric acid (mg/dl), sodium (meq/L), potassium (meq/l), calcium (mg/dl), phosphaste (mg/dl), magnesium (mg/dl), albumin (g/dl), total protein (g/dl), glutamate oxaloacetate transaminase (GOT) (U/L), glutamate-pyruvate transaminase (GPT) (U/L), total cholesterol (mg/dl), triglyceride (mg/dl), low-density lipoprotein (LDL) (mg/dl), high-density lipoprotein (HDL) (mg/dl), fasting and postprandial blood sugar (mg/dl), glycated hemoglobin (%). We calculated NLR as the ratio of neutrophil to lymphocyte counts, and PLT as the platelet to lymphocele counts. Markers for chronic infection or inflammation are as follows: hepatitis B status, hepatitis C status, Anti-Nuclear Ab (ANA), anti-double stranded DNA (anti-dsDNA), Anti-neutrophil cytoplasmic antibodies (ANCAs), proteinase 3 (PR3) and myeloperoxidase (MPO). Urinary samples were tested for spot urine protein (mg/dl), spot urine creatinine (mg/dl), spot urine albumin (mg/dl) and 24-hour proteinuria (g/day).

All pathological samples were reviewed by the same pathologist. Participants had the diagnosis of iMN based on stages of iMN (Ehrenreich- Churg)9. The study endpoints were patient death and renal death (ESRD). In the latter case, dialysis or kidney transplantation was performed according to our management guidelines.

Statistical methods

For continuous variables, data were expressed as mean ± SD, and for categorical variables, as numbers (percentages). Mann–Whitney U test was used to analyze the continuous variables and Chi-square test for the categorical variables. Kaplan-Meier curve was used to represent patient survival and renal survival. Youden index was used to evaluate the performance of a dichotomous diagnostic test for renal and patient outcomes. Cox proportional hazard regression was used to determine factors affecting renal survivals (univariate and multivariate cox models). Values of p < 0.05 was considered statistically significant. All statistical procedures were performed using the SPSS statistical software package, version 17.0 (Chicago, IL).

Results

Demographic data segregated according to stages of iMN

Initially, 187 times of iMN confirmed by renal biopsy patients were screened. Three of them were excluded due to graft kidney biopsy. We further excluded within the remaining 184 native kidney biopsies, the 2nd or 3rd renal biopsies, as well as secondary iMN (excluding autoimmune diseases, infections, medications, and malignancies). Finally, 99 patients with renal biopsy-confirmed idiopathic iMNs were analyzed in this study. The whole duration of follow-up was 11 years (January 2003 to December 2013). The mean duration of follow-up was 6.2 ± 3.2 years. Totally, 79 patients reached endpoint (20 loss of followed or dead). Based on stages of iMN (Ehrenreich- Churg)9, we separated 99 participants into two subgroups: subgroup 1 (stage 1–2)10 and subgroup 2 (stage 3–4)10, as shown in Table 1. As for baseline clinicopathological and laboratory data, recorded at the time of iMN diagnosis, participants were aged around 57.7 year old, and more patients were in the stage 2-chronic kidney disease (eGFR = 75.01 ml/min.1.732 m2). Metabolic syndrome was less frequent: i.e. 132.0 mmHg of SBP, 171.18 mg/dl of LDL, and 5.90% of HbA1c. Their proteinuria was severe, 7.99 g/day of daily urine protein. The participants in the more severe stages of iMN had lower percentages of neutrophils (64.62 ± 11.41 vs. 60.24 ± 8.96, p = 0.031) and lower levels of IgM (136.83 ± 91.57 vs. 98.68 ± 55.37, p = 0.011). Patients in stage 3–4 iMN had higher serum creatinine levels (1.10 ± 0.73 vs. 2.05 ± 2.30) but the difference with stage 1–2 was not significant (p = 0.086).

Table 1 Demographic characteristics of membranous nephropathy

Patient outcomes

As shown in Fig. 1A, we found according to the pathological stages of iMN, the 1-year, 3-year and 5- year patient survivals as follow: 97.5%, 91.6%, and 91.6% for stage 1–2; 95.6%, 92.6 and 88.5% for stage 3–4. Five-year after the diagnosis of iMN, patients of stage 3–4 appeared to have worse survivals, but no statistical significant difference was found with those of stage 1–2 (p = 0.818). No statistical significant difference was found in patient survivals across the stages of iMN. The predictive powers of C3, IgG, neutral-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) were weak (AUC = 0.568, 0.648, 0.684, and 0.673, respectively) (Fig. 2). The C3 ≤ 114 mg/dl also predicted poor patient outcomes (p < 0.001) (Fig. 1B) with good predictive power (AUC = 0.736, sensitivity = 100%, and specificity = 50%) (Fig. 1C). IgG, NLR, and PLR did not predict patient outcomes (Fig. 1D to F).

Figure 1
figure1

Predictions of patient survival of membranous nephropathy. (A) Stages of membranous nephropathy. (B) Serum C3 ≤ 114 mg/dl predicts poor patient outcoms (p < 0.001). (C) Serum C3 ≤ 114 mg/dl predicts poor patient outcoms with good diagnostic power (AUC = 0.736, sensitivity = 100%, specificity = 50%). (D) IgG predicted patient survival with poor diagnostic power (AUC = 0.501, sensitivity = 93.33%, specificity = 28.48%). (E) Neutrophil-to-lymphocyte ratio predicts patient survival with poor diagnostic power (AUC = 0.612, sensitivity = 47.06%, specificity = 78.66%). (F) Platelet-to-lymphocyte ratio predicts patient survival with poor diagnostic power (AUC = 0.570, sensitivity = 52.94%, specificity = 79.52%).

Figure 2
figure2

Predicting renal survival of membranous nephropathy. (A) Serum C3 ≤ 93.4 mg/dl predicts poor renal survival (p = 0.015). (B) Serum C3 ≤ 93.4 mg/dl predicts poor renal survival with good diagnostic power. (AUC = 0.643, sensitivity = 45.24%, specificity = 82.96%). (C) Serum IgG > 730 mg/dl predicts renal survival with poor diagnostic power (AUC = 0.526, sensitivity = 55%, specificity = 56.35%). (D) Stages of membranous nephropahy do not predict renal survival. (2E) Neutrophil-to-lymphocyte ratio >3.34 predicts poor renal survival (p < 0.001). (F) Neutrophil-to-lymphocyte ratio >3.34 predicts poor renal survival with good diagnostic power (AUC = 0.684, sensitivity = 55.81%, specificity = 79.71%). (G) Platelet-to-lymphocyte ratio >14.48 predicts poor renal survival (p = 0.002). (H) Platelet-to-lymphocyte ratio >14.48 predicts poor renal survival with fair diagnostic power. (AUC = 0.60, sensitivity = 46.51%, specificity = 80.71%).

Renal outcomes

Table 2 shows the results of univariate analysis: risk factors for poor renal outcomes were old ages (HR = 1.04, p = 0.002), high BUN (HR = 1.03, p < 0.001), poor baseline renal function (HR = 1.30 and p < 0.001 for higher serum creatinine; HR = 0.97 and p < 0.001 for higher eGFR; HR = 1.06 and p < 0.001 for urine PCR), C3 ≤ 93.4 mg/dl (HR = 2.15, p = 0.017), NLR > 3.34 (HR = 3.30, p < 0.001) and PLR > 14.48 (HR = 2.54, p = 0.003). The multivariate analysis revealed risk factors for poor renal functions being poor baseline renal function (HR = 0.97, p = 0.011 for higher eGFR), and NLR > 3.34 (HR = 2.25, p = 0.042). Youden index for C3 level was ≤93.4 mg/dl with good diagnostic power (AUC = 0.643) (Fig. 2B) in predicting poor renal outcomes (p = 0.015) (Fig. 2A). In addition, Youden index for NLR level was >3.34 with good diagnostic power (AUC = 0.684) (Fig. 2F) in predicting poor renal outcomes (p < 0.001) (Fig. 2E). Youden index for PLR level was >14.48 with good diagnostic power (AUC = 0.600) (Fig. 2H) in predicting poor renal outcomes (p < 0.002) (Fig. 2G). Whereas, IgG level (Fig. 2C) and pathological stages of iMN (Fig. 2D) showed no differences.

Table 2 Cox regression models for renal survival via univariate and multivariate analysis.

Discussion

That baseline poor renal function (higher BUN, higher creatinine, higher proteinuria, and lower eGFR) were risk factors for poor long term renal function, is a finding of our study in consistence with other studies3,10, including Toroto Risk Score with 85–90% of the accuracy3. However, we believed more new parameters are needed. Firstly, this score was based on clinical and laboratory variables in Western population of Italy, Finland and Toronto. The application to other races is not clear. Besides, parameters in this model are late markers. This model was developed 20 years ago and the outcome prediction of iMN had not improved since. Thirdly, most clinical skepticism of this model is the lack of long-term studies. Finally, they only collected common parameters for analyzed (background renal function, blood pressure, and ages). Therefore, more and newer biomarkers to predict outcomes of iMN are needed.

The morphologic severity of histologic sample with the diagnosed of iMN is related to long-term renal outcomes10,11. Staging of iMN (Ehrenreich-Churg) was reported more than 40 years ago9, and it was focused only on the status of GBM. The predictive value for renal outcome has not been discussed except by Marx et al. in 199910. The HR for ESRD of stage III/IV vs. I/II was found to be high at 5.3 (p = 0.002). After that, no study has validated the predictive power of such staging system. In our study, we found no statistical significance between different disease stages (HR = 2.41, 0.78–7.46 of 95% CI, p = 0.127). We believe the background histological severity is not reflected only GBM status, but also by glomerular segmental sclerosis and tubulointerstitial injury both of which can affect the long-term renal outcome. According to Chen et al.11, over a median follow-up of 34 months, segmental sclerosis and tubulointerstitial injury are independent risk factors respectively for 20 and 50% of renal function decline and the progression to ESRD. In brief, GBM status (Ehrenreich-Churg) alone failed to fully predict long-term renal outcome.

This present study is the first of its kind to show that serum C3 ≤ 93.4 mg/dl predicted poor renal outcome (HR = 2.15, p = 0.017) with good predictive power (AUC = 0.643). The complement system is important for the pathogenesis of iMN. Subepithelial immune deposits activate the complement system which starts activation of the C3 component, conversion of C5, and subsequent formation of the C5b-9 complex in the podocyte membrane12,13,14,15. In 1984, C3 deposits were first detected in half of those patients with idiopathic iMN16,17. Furthermore, C3d deposits were also detected in all patients associated with IgG deposits17. Patients with glomerular C3 deposits exhibited more severe proteinuria, which supported the association of complement activation with disease severity in human iMN17. Recently, using more sensitive immunohistological stainings, C3 deposits can be detected in almost all cases of iMN18,19. The greater the deposition of C3, the lower the serum C3 level, which predicted poor long-term renal function. In addition, low C3 level is a risk factor for poor patient survival as found in our study. Patients with poor renal function may tend to receive more immunosuppressants in treatment and consequently wound develop moreimmunosuppressant related complications, such as infections.

Both mean value of NLR > 3.34 (p < 0.01) and PLR > 14.48 (p = 0.002) predicted poor renal outcome. This is the first study to report such findings. We are also the first to demonstrate that NLR levels predicted renal outcome in iMN patients. NLR is a simple and inexpensive laboratory marker of the inflammatory status, including cirrhosis20, cardiovascular disease21, malignancy20,22,23, community-acquired pneumonia24, carotid atherosclerosis25 and stroke-associated pneumonia26. Its normal levels are between 0.78 and 3.5327. NLR predicts renal prognosis in lupus nephritis28 and granulomatosis with polyangiitis29. In addition, NLR is a risk factor for community-acquired acute kidney injury and NLR is associated with poor recovery of renal function30. Neutrophilia and lymphocytopenia are both indicative of disease severity. Also, an increased NLR level may be related to a low lymphocyte count. It has been proposed that under physiologic stress, the induced increase in cortisol secretion causes lymphopenia31. Similarly, PLR is also considered an inflammatory marker in thrombotic events, inflammatory diseases, and malignancies32,33. Recently, PLR has been associated with all-cause of mortality in the elderly with chronic kidney disease34, and it is associated with higher C-reactive protein in ESRD patients35. Platelets are viewed as immune cells and can interact with other types of immune cells, including endothelial cells, T-cells, neutrophils, and mononuclear phagocytes. These interactions might initiate and exacerbate the inflammation in the arterial wall36. Although without definite association, we still recommend routine checks for iMN patients, of such easily obtainable markers (like NLR and PLR), to improve the predictive power of renal outcome,especially NLR. NLR > 3.34 may have higher predictive power than PLR > 14.48. Firstly, both multivariable and univariate Cox regression models showed that NLR is a significant predictor of renal survival. NLR had predictive power only in univariate Cox regression. Besides, the predictive power (AUC) of NLR > 3.34 is higher than PLR > 14.48 (0.684 vs. 0.600)

There are several limitations of this study. Firstly, the subjects were chosen from a single medical center even though the samples of renal biopsies were relatively large in number (>8000 over 30 years). Secondly, there was insufficient data on treatments. The standard treatment for iMN was consistent according to current guidelines in this medical center. Angiotensin-converting Enzyme inhibitors (ACEis) or angiotensin II Receptor Blockers (ARBs) were prescribed to all patients with iMN. This immunosuppressive therapy was given for those experiencing a progressively declining GFR, or refractory and persistent proteinuria above 4 g/day after maximal ACEis or ARBs. Thirdly, we did not measure anti-PLA2R antibody37. However, in clinical practice, we did not measure this marker. Therefore, our study reflected results in the real world. Finally, we did not record complications of immunosuppressants, which could cause more infection-related mortality.

Conclusions

Stages of iMN did not fully explain the risks for ESRD. This is the first study to identify serum levels of C3 ≤ 93.4 mg/dl predicted poor renal outcome with good predictive power. Easily obtained markers, NLR > 3.34 predicted poor renal outcomes.

References

  1. 1.

    Glassock, R. J. Diagnosis and natural course of membranous nephropathy. Seminars in nephrology 23, 324–332 (2003).

  2. 2.

    Chapter 7: Idiopathic membranous nephropathy. Kidney international supplements 2, 186–197, https://doi.org/10.1038/kisup.2012.20 (2012).

  3. 3.

    Cattran, D. C. et al. Validation of a predictive model of idiopathic membranous nephropathy: its clinical and research implications. Kidney Int 51, 901–907 (1997).

  4. 4.

    Branten, A. J. et al. Urinary excretion of beta2-microglobulin and IgG predict prognosis in idiopathic membranous nephropathy: a validation study. J Am Soc Nephrol 16, 169–174, https://doi.org/10.1681/ASN.2004040287 (2005).

  5. 5.

    Hofstra, J. M., Deegens, J. K., Willems, H. L. & Wetzels, J. F. Beta-2-microglobulin is superior to N-acetyl-beta-glucosaminidase in predicting prognosis in idiopathic membranous nephropathy. Nephrol Dial Transplant 23, 2546–2551, https://doi.org/10.1093/ndt/gfn007 (2008).

  6. 6.

    Hoxha, E. et al. Phospholipase A2 receptor autoantibodies and clinical outcome in patients with primary membranous nephropathy. J Am Soc Nephrol 25, 1357–1366, https://doi.org/10.1681/ASN.2013040430 (2014).

  7. 7.

    Barbagallo, C. et al. Upregulated microRNAs in membranous glomerulonephropathy are associated with significant downregulation of IL6 and MYC mRNAs. J Cell Physiol, https://doi.org/10.1002/jcp.27851 (2018).

  8. 8.

    Levey, A. S. et al. Using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate. Annals of internal medicine 145, 247–254 (2006).

  9. 9.

    Churg, J. & Ehrenreich, T. Membranous nephropathy. Perspect Nephrol Hypertens 1(Pt 1), 443–448 (1973).

  10. 10.

    Marx, B. E. & Marx, M. Prediction in idiopathic membranous nephropathy. Kidney Int 56, 666–673, https://doi.org/10.1046/j.1523-1755.1999.00598.x (1999).

  11. 11.

    Chen, Y. et al. Pathological predictors of renal outcomes in nephrotic idiopathic membranous nephropathy with decreased renal function. J Nephrol 27, 307–316, https://doi.org/10.1007/s40620-014-0057-0 (2014).

  12. 12.

    Cybulsky, A. V., Quigg, R. J. & Salant, D. J. The membrane attack complex in complement-mediated glomerular epithelial cell injury: formation and stability of C5b-9 and C5b-7 in rat membranous nephropathy. J Immunol 137, 1511–1516 (1986).

  13. 13.

    Cybulsky, A. V., Rennke, H. G., Feintzeig, I. D. & Salant, D. J. Complement-induced glomerular epithelial cell injury. Role of the membrane attack complex in rat membranous nephropathy. J Clin Invest 77, 1096–1107, https://doi.org/10.1172/JCI112408 (1986).

  14. 14.

    Groggel, G. C., Adler, S., Rennke, H. G., Couser, W. G. & Salant, D. J. Role of the terminal complement pathway in experimental membranous nephropathy in the rabbit. J Clin Invest 72, 1948–1957, https://doi.org/10.1172/JCI111159 (1983).

  15. 15.

    Salant, D. J., Belok, S., Madaio, M. P. & Couser, W. G. A new role for complement in experimental membranous nephropathy in rats. J Clin Invest 66, 1339–1350, https://doi.org/10.1172/JCI109987 (1980).

  16. 16.

    Doi, T., Mayumi, M., Kanatsu, K., Suehiro, F. & Hamashima, Y. Distribution of IgG subclasses in membranous nephropathy. Clin Exp Immunol 58, 57–62 (1984).

  17. 17.

    Doi, T. et al. Demonstration of C3d deposits in membranous nephropathy. Nephron 37, 232–235, https://doi.org/10.1159/000183255 (1984).

  18. 18.

    Segawa, Y. et al. IgG subclasses and complement pathway in segmental and global membranous nephropathy. Pediatr Nephrol 25, 1091–1099, https://doi.org/10.1007/s00467-009-1439-8 (2010).

  19. 19.

    Endo, M. et al. Glomerular deposition and urinary excretion of complement factor H in idiopathic membranous nephropathy. Nephron Clin Pract 97, c147–153, https://doi.org/10.1159/000079174 (2004).

  20. 20.

    Personeni, N. et al. Prognostic value of the neutrophil-to-lymphocyte ratio in the ARQ 197-215 second-line study for advanced hepatocellular carcinoma. Oncotarget 8, 14408–14415, https://doi.org/10.18632/oncotarget.14797 (2017).

  21. 21.

    Tamhane, U. U. et al. Association between admission neutrophil to lymphocyte ratio and outcomes in patients with acute coronary syndrome. Am J Cardiol 102, 653–657, https://doi.org/10.1016/j.amjcard.2008.05.006 (2008).

  22. 22.

    Zhang, L. et al. Prognostic significance of neutrophil to lymphocyte ratio in patients with gallbladder carcinoma. HPB (Oxford) 18, 600–607, https://doi.org/10.1016/j.hpb.2016.03.608 (2016).

  23. 23.

    Stotz, M. et al. Increased neutrophil-lymphocyte ratio is a poor prognostic factor in patients with primary operable and inoperable pancreatic cancer. Br J Cancer 109, 416–421, https://doi.org/10.1038/bjc.2013.332 (2013).

  24. 24.

    Cataudella, E. et al. Neutrophil-To-Lymphocyte Ratio: An Emerging Marker Predicting Prognosis in Elderly Adults with Community-Acquired Pneumonia. J Am Geriatr Soc 65, 1796–1801, https://doi.org/10.1111/jgs.14894 (2017).

  25. 25.

    Corriere, T. et al. Neutrophil-to-Lymphocyte Ratio is a strong predictor of atherosclerotic carotid plaques in older adults. Nutr Metab Cardiovasc Dis 28, 23–27, https://doi.org/10.1016/j.numecd.2017.10.022 (2018).

  26. 26.

    Nam, K. W. et al. High Neutrophil-to-Lymphocyte Ratio Predicts Stroke-Associated Pneumonia. Stroke 49, 1886–1892, https://doi.org/10.1161/STROKEAHA.118.021228 (2018).

  27. 27.

    Forget, P. et al. What is the normal value of the neutrophil-to-lymphocyte ratio? BMC Res Notes 10, 12, https://doi.org/10.1186/s13104-016-2335-5 (2017).

  28. 28.

    Ata Bora Ayna, S. E., Coşkun, B. N., Harman, H. & Pehlivan, Y. Neutrophil to Lymphocyte Ratio and Mean Platelet Volume as Inflammatory Indicators in Systemic Lupus Erythematosus Nephritis. Archives of rheumatology 21, 5 (2017).

  29. 29.

    Kucuk, H. et al. Predictive value of neutrophil/lymphocyte ratio in renal prognosis of patients with granulomatosis with polyangiitis. Renal failure 39, 273–276, https://doi.org/10.1080/0886022X.2016.1259633 (2017).

  30. 30.

    Erdem, E. Neutrophil lymphocyte ratio in aute renal failure. Indian journal of nephrology 25, 126–127, https://doi.org/10.4103/0971-4065.145099 (2015).

  31. 31.

    Azab, B., Shah, N., Akerman, M. & McGinn, J. T. Jr. Value of platelet/lymphocyte ratio as a predictor of all-cause mortality after non-ST-elevation myocardial infarction. Journal of thrombosis and thrombolysis 34, 326–334, https://doi.org/10.1007/s11239-012-0718-6 (2012).

  32. 32.

    Yildiz, A. et al. The Utility of the Platelet-Lymphocyte Ratio for Predicting No Reflow in Patients With ST-Segment Elevation Myocardial Infarction. Clinical and applied thrombosis/hemostasis: official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis 21, 223–228, https://doi.org/10.1177/1076029613519851 (2015).

  33. 33.

    Toprak, E. et al. Platelet-to-lymphocyte ratio: A new inflammatory marker for the diagnosis of preterm premature rupture of membranes. Journal of the Turkish German Gynecological Association 18, 122–126, https://doi.org/10.4274/jtgga.2017.0028 (2017).

  34. 34.

    Tatar, E. et al. The association of neutrophil/lymphocyte ratio and platelet/lymphocyte ratio with clinical outcomes in geriatric patients with stage 3-5 chronic kidney disease. Acta clinica Belgica 71, 221–226, https://doi.org/10.1080/17843286.2016.1159797 (2016).

  35. 35.

    Ahbap, E. et al. Neutrophil-to-lymphocyte ratio and platelet-tolymphocyte ratio in evaluation of inflammation in end-stage renal disease. Clin Nephrol 85, 199–208, https://doi.org/10.5414/CN108584 (2016).

  36. 36.

    Borissoff, J. I., Spronk, H. M. & ten Cate, H. The hemostatic system as a modulator of atherosclerosis. The New England journal of medicine 364, 1746–1760, https://doi.org/10.1056/NEJMra1011670 (2011).

  37. 37.

    Beck, L. H. Jr. et al. M-type phospholipase A2 receptor as target antigen in idiopathic membranous nephropathy. The New England journal of medicine 361, 11–21, https://doi.org/10.1056/NEJMoa0810457 (2009).

Download references

Acknowledgements

This study was supported by grants TCVGH-1063601B, and TCVGH-1073604C from Taichung Veterans General Hospital.

Author information

Shang-Feng Tsai, Ming-Ju Wu and Cheng-Hsu Chen conceived of the presented idea. Shang-Feng Tsai and Cheng-Hsu Chen. developed the theory and performed the computations. Ming-Ju Wu and Cheng-Hsu Chen verified the analytical methods. All authors discussed the results and contributed to the final manuscript. Shang-Feng Tsai wrote the manuscript with input from all authors. Ming-Ju Wu and Cheng-Hsu Chen conceived the study and were in charge of overall direction and planning.

Correspondence to Cheng-Hsu Chen.

Ethics declarations

Competing Interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.