Skip to main content
Download PDF

Association between neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and diabetic retinopathy among diabetic patients without a related family history

Diabetology & Metabolic Syndrome volume 12, Article number: 55 (2020) Cite this article

Abstract

Background

Diabetic retinopathy (DR) is a specific neurovascular complication of diabetes mellitus (DM). Clinically, family history is a widely recognized risk factor for DR, assisting diagnosis and risk strata. However, among a great amount of DR patients without hereditary history like hypertension and diabetes, direct and simple risk factors to assist clinical decisions are still required. Herein, we intend to investigate the associated risk factors for these DR patients based on systemic inflammatory response indexes, neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR).

Methods

We consecutively enrolled 1030 patients with a definite diagnosis of type 2 diabetes mellitus (T2DM) from the endocrinology department of the Second hospital of People in Yun Nan. Based on funduscopy and family history checking, we excluded patients with a family history of hypertension and diabetes and finally enrolled 264 patients with DR and 206 patients with non-diabetic retinopathy (NDR). Through correlation analysis, univariate and multivariate regression, we further explore the association between NLR, PLR, and DR. On top of that, we investigate the effect of NLR and PLR on risk reclassification of DR.

Results

Compared with NDR patients, NLR and PLR levels are significantly higher among DR patients (NLR: 2.36 ± 1.16 in DR group versus 1.97 ± 1.06 in NDR group, p < 0.001; PLR: 11.62 ± 4.55 in DR group versus10.56 ± 4.45 in NDR group, p = 0.012). According to univariate analysis, NLR and PLR add risks to DR. After fully adjusting co-founders, NLR, as both continuous and categorical variate, remains an independent risk factor for DR (OR (95%CI): 1.37 (1.06, 1.78) P = 0.018). And though PLR was not independently associated with DR as a continuous variable (OR (95%CI) 1.05 (0.99, 1.11) p = 0.135), the highest quantile of PLR add two-fold increased risk (OR (95%CI) 2.20 (1.05, 4.59) p = 0.037) in the fully adjusted model for DR. In addition, addition of PLR and NLR to the established factor hemoglobin (Hb) improved the discriminability of the model and assisted the reclassification of DR. After combining PLR and NLR the Area under curve (AUC) of Hb based model raised from 0.76 to 0.78, with a category-free net reclassification improvement (NRI) of 0.532 (p < 0.001) and integrated discrimination improvement (IDI) of 0.029 (p < 0.001).

Conclusions

Systemic inflammatory response indexes NLR and PLR were associated with the presence of DR among patients without associated family history and contributed to improvements in reclassification of DR in addition to Hb.

Background

Diabetic retinopathy (DR), as a specific neurovascular complication of diabetes mellitus(DM),become a major reason for blinding people aged 20–74 [1]. The pathogenesis of DR was complicated, with diverse factors involvement. According to the major researches, commonly acknowledged risk factors include family history, hyperglycemia, hypertension, and hyperlipidemia, long-duration of diabetes, diabetic nephropathy, blood glucose fluctuation, obesity and gestation [2,3,4,5]. Particularly, inheritable and unchangeable factors such as genetic polymorphism were closely associated with the incidence and progress of DR [5]. Due to the high costs and low cooperation rate among patients, family history was regarded as a direct or indirect interpretation of inheritable risk factor which helps clinicians to evaluate the risk for DR. It is reported that presence of family history, diabetes, and hypertension, always indicated lower age of the patients which is associated to rather longer duration of diabetes for these patients [6, 7]. In addition, researches revealed a family history of diabetes and hypertension also contributed to a higher frequency of DR [8, 9]. However, in clinical practice, despite the complicated inheritable factors, a large number of diabetic patients without obvious family history also take up a major part of DR patients. Exploring risk factors for these people is quite important for clinical decisions.

Recently, increasing studies reported that routine blood examination could provide rich and effective information to assist risk stratification of disease [10, 11].The promising index neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) conveyed a systematic inflammatory response in our body and has been evidenced as predictive and prognostic factors for DM and the related complications [12,13,14,15]. Moreover, Hemoglobin (Hb) was also a well-established biomarker for DR [16, 17]. However, the association between these factors and DR among patients without diabetes or hypertension family history remains unclear. Herein, our study aimed at clarifying the relationship between NLR, PLR, and DR in patients without inheritable risk factors, and further detect the contribution of NLR and PLR to disease reclassification when combining the traditional factor Hb.

Methods

Participants and study design

We consecutively enrolled a total of 1030 type 2 diabetic patients undergoing funduscopy from the endocrinology department of the Second hospital of People in Yun Nan. Diagnosis of type 2 diabetes was made according to the 1999 World Health Organization criteria [18]. The diagnosis of diabetic was based on the 2002 International Clinical Classification Standard [19]. And the non-diabetic retinopathy group (NDR) was defined as patients who had been clearly diagnosed with type 2 diabetes and had no diabetic retinopathy on fundus. Besides patients with a family history of hypertension and diabetes, patients with severe systemic disease, glaucoma, trauma, non-diabetic retinopathy, pregnancy, malignant tumors, severe cardiovascular and cerebrovascular diseases, liver and kidney dysfunction, blood disease, recent surgery, infection or other severe stress condition were also excluded. Finally, we successfully enrolled 264 patients with DR and 206 patients without DR (the NDR group).

Clinical information and biochemical examination

All participants received routine examination and blood examination and were asked in detail about their disease history, medical history, and personal history. After collecting patients fasting peripheral blood, blood routine test and white blood cell classification were performed. Specifically, the blood routine and blood cell count were detected using a fully automated blood analyzer from Xisen Meikang (XE-2100, Japan); nucleic acid fluorescent staining, laser counting and flow cytometry were used to determine white blood cells and white blood cell classification; colorimetric method was used to determine hemoglobin concentration; and double sheath flow impedance method was applied to determine platelet count and red blood cell count. Neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and monocyte-to-lymphocyte ratio (MLR) were calculated for all blood samples. All biochemical analyses of the same samples were performed in our hospital, including the analyses of fasting blood glucose (FPG), glycosylated hemoglobin A1c (HbA1c), triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), serum creatinine (Scr), and blood urea nitrogen (BUN). Among them, the glucose determination kit (glycokinase method) was used to determine fasting blood glucose; the HbA1c determination kit (enzymatic method) was used to determine HbA1c in plasma; the creatinine detection kit (enzymatic method) was used to determine serum creatinine; the urea nitrogen detection kit (Enzymatic method) were used for determination of serum urea nitrogen level. Total cholesterol kit (liquid) (oxidase method), triglyceride kit (liquid) (glycerol phosphate oxidase method), lipoprotein cholesterol detection kit (direct method), and low-density lipoprotein cholesterol detection kit (direct method) were used for measurements of total cholesterol, triglyceride, high-density lipoprotein cholesterol and low-density lipoprotein cholesterol in serum, respectively.

Statistics analysis

Distribution normality was initially tested through the Kolmogorov–Smirnov test. Continuous data are showed by the mean ± standard deviation (SD) and were compared by independent Student t test or one-way analysis of variance test. While variables without normality were expressed by median plus IQR. The Chi squared test or Fisher’s exact test was used to compare categorical variables. Due to the order of magnitude of PLR, a scaling factor of 10 was included to explain unit conversion. The correlation was assessed by the Spearman correlation test. NLR, PLR were analyzed as continuous variates and also respectively divided into quartiles, with the first quartile representing the lowest levels and the fourth quartile the highest. Univariate logistic regression models were used to examine possible factors associated with DR. Multivariable logistic regression analysis was conducted to assess the independent association between NLR, PLR and DR. Variables with known clinical risk, significance at the 5% level from the univariate analyses were included as co-founders in for an adjustment. After adjusting for age, gender, diabetes duration, hypertension, BMI, Scr, white blood cell (WBC), Hb, BUN, TG, FPG and HBA1c, odds ratios with 95% confidence intervals were reported. In logistics regression, an Akaike information criterion (AIC) based stepwise variable selection method was used to acquire the optimal logistic regression model.

All statistical analyses were performed by SPSS software (version 22.0; SPSS, Inc., Chicago, IL, USA). P-values less than 0.05 (P < 0.05) were considered significant and the significance was two-tailed.

Results

Baseline

As shown in Table 1, gender, presence of hypertension, smoking, drinking, diabetes duration, FPG, WBC, Hb, BUN, Scr and TG are significantly different between DR and NDR group (P < 0.05). Specifically, compared with the NDR group, DR patients have longer diabetes duration, higher levels of FPG, WBC, BUN, Scr and TG, a higher proportion of female and hypertension presence. However, levels of Hb were lower in DR patients. In addition, no difference was observed in age, BMI, HBA1c, TC, LDL-C, HDL-C levels as well as the treatment of diabetes. According to Fig. 1, levels of NLR (2.36 ± 1.16 in DR group versus 1.97 ± 1.06 in NDR group, p < 0.001) and PLR(11.62 ± 4.55 in DR group versus10.56 ± 4.45 in NDR group, p = 0.012)were significantly higher in DR group.

Table 1 Baseline characteristics of the DR and NDR groups
Full size table
Fig. 1
figure 1

Comparison of NLR and PLR in different group a Boxplot showed that DR patients had higher NLR than the NDR patients (p < 0.001**). b Boxplot showed that DR patients had higher PLR than the NDR patients (p = 0.012)

Full size image

Correlation analysis of NLR, PLR and Hb and major clinical factors

As shown in Table 2, according to correlation analysis, NLR positively correlated with MLR, PLR, diabetes duration, BUN and Scr, and negatively correlated with Hb, but displayed insignificant correlation with age, gender, hypertension, BMI, FPG, and HbA1c. Meanwhile, PLR correlated positively with NLR, MLR and diabetes duration, and negatively with BMI and Hb. In addition, Hb correlated positively with BMI and negatively correlated with gender, hypertension, age, NLR, PLR, diabetes course, and BUN.

Table 2 Correlation analysis of NLR, PLR and Hb and major clinical factors
Full size table

Univariate analysis

As shown in Table 3, our univariate regression analysis revealed that gender of female, presence of hypertension, long course of diabetes,higher levels of FPG, WBC, NLR, PLR BUN, Scr and TG adds risk to presence of DR, while higher counts of Hb was related to lower risk of DR. However, no association was found between age, BMI, HbA1c, TC, HDL-C, LDL-C, MLR and presence of DR.

Table 3 Univariate analysis
Full size table

Associations between NLR, PLR, and DR

NLR and DR

By conducting multivariate analysis, we found that NLR was associated with DR independent of other known factors. With a unit increase of NLR, the risk for DR would raise 37%. Furthermore, when treated as a category variate divided according to its quantile, the association of NLR and DR still exists. As Table 4 demonstrated, from the crude model to simple or complex model, there was a 2.8 fold increased risk for DR in the highest quantile of NLR (OR, 95%CI 2.80 (1.32, 5.95) p = 0.007) (Table 4).

Table 4 Independent association between NLR and DR
Full size table

PLR and DR

We also included PLR in multivariate logistics regression by adjusting other co-founders and observed that PLR was not an independent risk factor for DR as a continuous variable, but the ranked level of the index assists the risk stratification. Specifically, after full adjustment, the highest quantile of PLR held add 2.2 times of risk to the presence of DR compared with the first quantile(OR(95%CI)2.20 (1.05, 4.59), P = 0.037) (Table 5).

Table 5 Independent association between PLR and DR
Full size table

NLR and PLR for reclassification of DR

Hb and DR

Particularly, we also assess the performance of the previous factor-Hb in our diabetic patients’ group. By applying crude and different multivariate models, we confirmed that no matter treated as a continuous variable or categorical variable, Hb was stably related to lower risk of DR with the higher quartile displaying lower risk probability (Table 6).

Table 6 Independent association between Hb and DR
Full size table

A combination of PLR, NLR, and Hb for predicting DR

In order to evaluate the prognostic value of NLR and PLR for improving risk stratification of DR, we performed a receiver-operating characteristic (ROC) analyses to calculate the area under the curve (AUC) of each factor and assess the performance of a combination of these factors (Table 7). Though NLR and PLR alone didn’t perform better than Hb, a combination of NLR, PLR, and Hb indeed result in a model with increased predictive performance (area under the ROC curve 0.78 (95%CI 0.74–0.82) versus. 0.76 (95%CI 0.72–0.81)) (Fig. 2). Furthermore, the addition of NLR, PLR significantly improved the risk reclassification over using Hb alone, with a considerable category-free net reclassification improvement(NRI) and a meaningful integrated discrimination improvement (IDI) for DR among diabetic patients without a family history.(NRI(95%CI) 0.53 (0.36–0.71) p < 0.001; IDI(95%CI) 0.03 (0.01–0.04); p < 0.001.)

Table 7 A combination of PLR, NLR and Hb for predicting DR
Full size table
Fig. 2
figure 2

Receiver operating characteristic (ROC) curves of NLR, PLR and Hb for DR a The ROC curves using Hb, NLR or PLR alone for discriminating DR (Hb, black, NLR, red, PLR, green) b The ROC curves included and not included NLR and PLR for predicting DR, (Model1: black, a combination of NLR, PLR and Hb for predicting DR, Model2: red, using Hb along to predict DR)

Full size image

Discussion

Our research demonstrated the association of systemic inflammatory response index with diabetic retinopathy among type 2 diabetic patients without related family histories. First of all, we verified that levels of NLR and PLR but not MLR were higher in the DR group. Furthermore, according to our multivariate analysis, not only did NRL serve as an independent risk factor but also the highest quartile of both NLR and PLR added risk to DR. More importantly, addition of NLR and PLR to Hb-based model improved reclassification of DR. Through our study, we provide the simple and available blood-based index for DR, promoting the risk stratification of DR among type 2 diabetic patients without family history.

Although the association between blood inflammatory index and DR drew much attention previously [12, 20,21,22],our study further deciphered the association and the clinical application of these indicators in T2DM patients without associated family history. Herein, we investigate the association between the indicators and DR more comprehensively in a larger population (n = 470) and revealed that a combination of NLR, PLR and Hb displayed significantly improved discriminability and raised sensitivity compared with using Hb alone. Therefore, combining the three factors might be helpful in clinical practice to improve the identification of DR in T2DM patients without family history of diabetes and hypertension.

Chronic inflammation plays an essential role in the initiation and progression of type 2 diabetes and further accelerates the deterioration of micro-angiopathy and macrovascular disease in patients with diabetes [23]. Previous studies have evidenced that peripheral blood leukocytes and their subgroups are associated with macrovascular and microvascular complications among patients with type 2 diabetes [24]. Specifically, peripheral blood leukocytes include lymphocytes, basophils, neutrophils, eosinophils, and monocytes, each type of which holds a unique biological function in systemic inflammation. NLR and PLR are two indexes that represent the integration of two factors and are considered to be new markers of the systemic inflammatory response [14]. Increasing studies have confirmed their association with type 2 diabetes [14, 25]. DR is a common micro-angiopathic complication in diabetes. More and more evidence indicates that inflammation plays an important part in the early and progressive stage of DR [26,27,28], through inducing the formation of new blood vessels and macular edema [26], damaging the glial crosstalk and causing neuronal loss [29]. In addition, studies have also found that many inflammatory cytokines (such as CRP, TNF-α and VEGF, etc.) increase in patients with DR [30]. And intervention and regulation targeted at the inflammatory response in patients with diabetic retinopathy [31] can prohibit the progression of diabetes and retinopathy.

As two major indexes related to systemic inflammatory, PLR and NLR have been proved to be associated with diabetes and its complications [12, 14]. Apart from genetic epigenetic factors (family history of diabetes and hypertension), our study showed that higher NLR levels significantly increased the risk of DR, which is consistent with the previous results. But compared with the former one, our research has a larger sample size with an enrollment of 470 patients and also excludes the inheritable family history. NLR represents peripheral blood neutrophils to lymphocytes ratio, which integrates different but complementary immune pathways in circulating blood. First of all, elevated NLR can be a manifestation of an increased number of neutrophils, which adhere to the endothelial cell, leading to vascular endothelial damage, and in turn causing extensive chronic inflammation [11, 32]. Hence the NLR might reveal the enhanced microvascular inflammation in DR patients. Secondly, lymphocytes serve as a major part of the body’s immune response. They have the ability to control and regulate inflammatory responses, and a relatively higher proportion of CD4 T cells were proved to be anti-atherosclerotic [33]. And our study found a decrease in absolute count of lymphocytes among peripheral blood in patients with high-level NLR (NLR:2.36 ± 1.16 in DR group versus 1.97 ± 1.06 in NDR group, p < 0.001;absolute count of lymphocytes:(1.98 ± 0.64)*109/L in DR group versus (2.11 ± 0.67)*109/L in NDR group, p = 0.031), which indicates the possibility of insufficient immunoregulation due to the fewer lymphocytes, In addition, this study also demonstrated that NLR levels were positively correlated with the duration of diabetes, BUN, and Scr, which is consistent with the previous results [34].

Previous studies have found that PLR is closely related to diabetes and can be used to assess the progress of the disease [35], predict and evaluate the diabetes-related lower limb vascular disease [15], atherosclerosis and diabetic foot ulcers [36]. However, Atak et al. didn’t found an association between PLR and DR [35], which might be attributed to the small sample size. In our study, though the PLR as a continuous variate didn’t show independent association with DR, the highest quartile of PLR indeed adds more than 2-fold risk to the presence of DR. It has been commonly admitted that platelets participate in thrombosis. Moreover, until now, increasing studies have proved that platelets played an important role in the immuno-inflammatory response. Specifically, platelets can release a variety of immune-regulating cytokines, chemokines, and other mediators, thus regulating the inflammation response in blood vessels in an autocrine or paracrine manner [37]. Meanwhile, it could also regulate neutrophils, endothelial cells, and lymph directly, allowing them to recruit towards injured tissue [38]. Similarly, based on the regulatory function of platelets, increased PLR might represent the relatively active inflammatory response of platelets among DR patients. Additionally, our study also indicates a positive correlation between PLR and diabetes courses and a negative correlation between PLR and BMI. Taken together, we provide the large-sample size-based evidence for the role of PLR in DR, and further mechanism studies are also needed in the future.

We also proved that in diabetic patients without a family history of diabetes and hypertension, Hb levels were still negatively associated with the risk of DR independent of established factors. Hemoglobin is a special protein transporting oxygen within red blood cells. And low hemoglobin levels might lead to tissue ischemia and hypoxia, which is one of the key mechanisms of DR occurrence [39]. Studies have found that hemoglobin levels were negatively related to endothelial function and lower hemoglobin levels directly resulted in organ damage [40]. In addition, the level of hemoglobin is an indicator of the anemia condition in our body. According to previous studies, it has been found that the anemia patients held a high level of vascular endothelial growth factor (VEGF) which is closely related to retinal neovascularization [41, 42]. Some studies indicated that anemia may enhance oxidative stress [43], because the antioxidant capacity of red cells can be damaged due to anemia [44], which in turn promotes oxidative stress and accelerates presence of DR [45]. In particular, in this study, a combination of systemic inflammation indicators NLR, PLR, and Hb was shown to increase the predictability of DR and help DR reclassification.

The established relationships between some clinical factors and DR in our patient’s group were in line with previous ones. Firstly, we found that blood lipid was a risk factor for DR, and the results of this study are consistent with previous one [46], indicating higher TG increased the risk of DR. In addition, in consistency with previous studies, diabetic nephropathy significantly increases the risk of DR with shared pathological mechanism [47, 48]. We also observed higher levels of BUN and Scr in DR patients, revealing a potential interaction multi-organ complication under the background of diabetes.

In summary, in people with type 2 diabetes without a family history of diabetes and hypertension, systemic inflammation indicators NLR and PLR are closely related to DR. Higher NLR and PLR increase the risk of DR, and after combined with Hb indicators, they contributed to DR reclassification. To provide more practical and reliable guidance for clinical diagnosis, further multi-center prospective clinical studies and basic researches are also required to elucidate the relationship between the PLR NLR and DR.

Limitations

A series of limitations still exist in our study. First of all, our study is a single-center study, patients’ recruitment, staff characteristics, and departmental protocols might add a limitation to the universality of our results. Additionally, with a lack of follow-up, we were unable to verify the predictive value of the factors, the further prospective cohort is still required to clarify the value PLR, NLR in predicting DR among T2DM patients in clinical practice. Finally, as our participants were type 2 diabetic patients without family history of hyperextension and diabetes, the results might not be extrapolated to other populations.

Conclusion

Systemic inflammatory response indexes NLR and PLR were associated with the presence of DR among patients without related family history and improved discriminability and re-classification of hemoglobin-based predictive model.

Availability of data and materials

The datasets used during the present study are available from the corresponding author on reasonable request.

Abbreviations

DR:

Diabetic retinopathy

DM:

Diabetes mellitus

NLR:

Neutrophil-to-lymphocyte ratio

PLR:

Platelet-to-lymphocyte ratio

T2DM:

Type 2 diabetes mellitus

NDR:

Non-diabetic retinopathy

Hb:

Hemoglobin

AUC:

Area under curve

NRI:

Net reclassification improvement

IDI:

Integrated discrimination improvement

MLR:

Monocyte-to-lymphocyte ratio

FPG:

Fasting blood glucose

HbA1c:

Glycosylated hemoglobin A1c

TG:

Triglyceride

TC:

Total cholesterol

LDL-C:

Low-density lipoprotein cholesterol

HDL-C:

High-density lipoprotein cholesterol

Scr:

Serum creatinine

BUN:

Blood urea nitrogen

SD:

Standard deviation

WBC:

White blood cell

AIC:

Akaike information criterion

BMI:

Body mass index

References

  1. Solomon SD, Chew E, Duh EJ, Sobrin L, Sun JK, VanderBeek BL, et al. Diabetic retinopathy: a position statement by the American Diabetes Association. Diabet Care. 2017;40:412–8.

    Google Scholar 

  2. Chinese Medical Association Diabetes Division Retinopathy Group. Expert consensus on prevention and treatment of diabetic retinopathy. Chinese J Diabet Mellitus. 2018;10:241–7.

    Google Scholar 

  3. Song SJ, Wong TY. Current concepts in diabetic retinopathy. DiabetMetab J. 2014;38:416–25.

    Google Scholar 

  4. Kuo JZ, Wong TY, Rotter JI. Challenges in elucidating the genetics of diabetic retinopathy. JAMA Ophthalmol. 2014;132:96–107.

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Wat N, Wong RL, Wong IY. Associations between diabetic retinopathy and systemic risk factors. Hong Kong Med J. 2016;22:589–99.

    CAS  PubMed  Google Scholar 

  6. Molyneaux L, Constantino M, Yue D. Strong family history predicts a younger age of onset for subjects diagnosed with type 2 diabetes. Diabet Obes Metab. 2004;6:187–94.

    CAS  Google Scholar 

  7. Yamamoto-Honda R, Takahashi Y, Mori Y, Yamashita S, Yoshida Y, Kawazu S, et al. A positive family history of hypertension might be associated with an accelerated onset of type 2 diabetes: results from the National Center Diabetes Database (NCDD-02). Endocr J. 2017;64:515–20.

    CAS  PubMed  Google Scholar 

  8. Anwar SB, Asif N, Naqvi SAH, Malik S. Evaluation of multiple risk factors involved in the development of diabetic retinopathy. Pak J Med Sci. 2019;35:156–60.

    PubMed  PubMed Central  Google Scholar 

  9. Hu Y, Teng W, Liu L, Chen K, Liu L, Hua R, et al. Prevalence and risk factors of diabetes and diabetic retinopathy in Liaoning province, China:a population-based crosssectional study. PLoS One. 2015;10:e0121477.

    PubMed  PubMed Central  Google Scholar 

  10. Hu Z, Tan S, Chen S, Qin S, Chen H, Qin S, et al. Diagnostic value of hematological parameters platelet to lymphocyte ratio and hemoglobin to platelet ratio in patients with colon cancer. Clin Chim Acta. 2019;501:48–52.

    PubMed  Google Scholar 

  11. Azab B, Zaher M, Weiserbs KF, Torbey E, Lacossiere K, Gaddam S, et al. Usefulness of neutrophil to lymphocyte ratio in predicting short- and long-term mortality after non-ST-elevation myocardial infarction. Am J Cardiol. 2010;106:470–6.

    PubMed  Google Scholar 

  12. Liu J, Liu X, Li Y, Quan J, Wei S, An S, et al. The association of neutrophil to lymphocyte ratio, mean platelet volume, and platelet distribution width with diabetic retinopathy and nephropathy: a meta-analysis. Biosci Rep. 2018;38:72.

    Google Scholar 

  13. Ulu SM, Dogan M, Ahsen A, Altug A, Demir K, Acartürk G, et al. Neutrophil-to-lymphocyte ratio as a quick and reliable predictive marker to diagnose the severity of diabetic retinopathy. Diabet Technol Ther. 2013;15:942–7.

    CAS  Google Scholar 

  14. Mertoglu C, Gunay M. Neutrophil-Lymphocyte ratio and Platelet-Lymphocyte ratio as useful predictive markers of prediabetes and diabetes mellitus. Diabetes Metab Syndr. 2017;11:S127–31.

    PubMed  Google Scholar 

  15. Liu N, Sheng J, Pan T, Wang Y. Neutrophil to lymphocyte ratio and platelet to lymphocyte ratio are associated with lower extremity vascular lesions in Chinese patients with type 2 diabetes. Clin Lab. 2019. https://doi.org/10.7754/clin.lab.2018.180804.

    Article  PubMed  Google Scholar 

  16. Lee MK, Han KD, Lee JH, Sohn SY, Jeong JS, Kim MK, et al. High hemoglobin levels are associated with decreased risk of diabetic retinopathy in Korean type 2 diabetes. Sci Rep. 2018;8:5538.

    PubMed  PubMed Central  Google Scholar 

  17. Traveset A, Rubinat E, Ortega E, Alcubierre N, Vazquez B, Hernández M, et al. Lower hemoglobin concentration is associated with retinal ischemia and the severity of diabetic retinopathy in type 2 diabetes. J Diabet Res. 2016;2016:3674946.

    Google Scholar 

  18. Alberti KG, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications. Diabet Med. 1998;15:539–53.

    CAS  PubMed  Google Scholar 

  19. Wilkinson CP, Ferris FL, Klein RE, Lee PP, Agardh CD, Davis M, et al. Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology. 2003;110:1677–82.

    CAS  PubMed  Google Scholar 

  20. Luo WJ, Zhang WF. The relationship of blood cell-associated inflammatory indices and diabetic retinopathy: a Meta-analysis and systematic review. Int J Ophthalmol. 2019;12:312–23.

    PubMed  PubMed Central  Google Scholar 

  21. Demirtas L, Degirmenci H, Akbas EM, Ozcicek A, Timuroglu A, Gurel A, et al. Association of hematological indicies with diabetes, impaired glucose regulation and microvascular complications of diabetes. Int J Clin Exp Med. 2015;8:11420–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Yue S, Zhang J, Wu J, Teng W, Liu L, Chen L. Use of the monocyte-to-lymphocyte ratio to predict diabetic retinopathy. Int J Environ Res Public Health. 2015;12:10009–19.

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Fujita T, Hemmi S, Kajiwara M, Yabuki M, Fuke Y, Satomura A, et al. Complement-mediated chronic inflammation is associated with diabetic microvascular complication. Diabetes Metab Res Rev. 2013;29:220–6.

    CAS  PubMed  Google Scholar 

  24. Tong PC, Lee KF, So WY, Ng MH, Chan WB, Lo MK, et al. White blood cell count is associated with macro- and microvascular complications in chinese patients with type 2 diabetes. Diabet Care. 2004;27:216–22.

    Google Scholar 

  25. Sefil F, Ulutas KT, Dokuyucu R, Sumbul AT, Yengil E, Yagiz AE, et al. Investigation of neutrophil lymphocyte ratio and blood glucose regulation in patients with type 2 diabetes mellitus. J Int Med Res. 2014;42:581–8.

    CAS  PubMed  Google Scholar 

  26. Kern TS. Contributions of inflammatory processes to the development of the early stages of diabetic retinopathy. Exp Diabet Res. 2007;2007:95103.

    Google Scholar 

  27. Rübsam A, Parikh S, Fort PE. Role of Inflammation in diabetic retinopathy. Int J Mol Sci. 2018;19:942.

    PubMed Central  Google Scholar 

  28. Tang J, Kern TS. Inflammation in diabetic retinopathy. Prog Retin Eye Res. 2011;30:343–58.

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Lange C, Storkebaum E, de Almodovar CR, Dewerchin M, Carmeliet P. Vascular endothelial growth factor: A neurovascular target in neurological diseases. Nat Rev Neurol. 2016;12:439–54.

    CAS  PubMed  Google Scholar 

  30. Nalini M, Raghavulu BV, Annapurna A, Avinash P, Chandi V, Swathi N. Correlation of various serum biomarkers with the severity of diabetic retinopathy. Diabetes Metab Syndr. 2017;11:S451–4.

    PubMed  Google Scholar 

  31. Malukiewicz G, Stafiej J, Lesiewska H, Sikorski B. Use of nonsteroidal anti-inflammatory drugs in diabetic retinopathy. Klinika oczna. 2016;118:44–7.

    PubMed  Google Scholar 

  32. Sala A, Folco G. Neutrophils, endothelial cells, and cysteinyl leukotrienes: a new approach to neutrophil-dependent inflammation? Biochem Biophys Res Commun. 2001;283:1003–6.

    CAS  PubMed  Google Scholar 

  33. Wang RT, Zhang JR, Li Y, Liu T, Yu KJ. Neutrophil-Lymphocyte ratio is associated with arterial stiffness in diabetic retinopathy in type 2 diabetes. J Diabet Compli. 2015;29:245–9.

    Google Scholar 

  34. Chittawar S, Dutta D, Qureshi Z, Surana V, Khandare S, Dubey TN. Neutrophil-lymphocyte ratio is a novel reliable predictor of nephropathy, retinopathy, and coronary artery disease in indians with type-2 diabetes. Indian J Endocrinol Metab. 2017;21:864–70.

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Atak B, Aktas G, Duman TT, Erkus E, Kocak MZ, Savli H. Diabetes control could through platelet-to-lymphocyte ratio in hemograms. Rev Assoc Med Bras. 1992;2019(65):38–42.

    Google Scholar 

  36. Mineoka Y, Ishii M, Hashimoto Y, Yamashita A, Nakamura N, Fukui M. Platelet to lymphocyte ratio correlates with diabetic foot risk and foot ulcer in patients with type 2 diabetes. Endocr J. 2019;66:905–13.

    CAS  PubMed  Google Scholar 

  37. Chatterjee M, Geisler T. Inflammatory Contribution of Platelets Revisited: new Players in the Arena of Inflammation. Semin Thromb Hemost. 2016;42:205–14.

    CAS  PubMed  Google Scholar 

  38. Jenne CN, Urrutia R, Kubes P. Platelets: bridging hemostasis, inflammation, and immunity. Int J Lab Hematol. 2013;35:254–61.

    CAS  PubMed  Google Scholar 

  39. Irace C, Scarinci F, Scorcia V, Bruzzichessi D, Fiorentino R, Randazzo G, et al. Association among low whole blood viscosity, haematocrit, haemoglobin and diabetic retinopathy in subjects with type 2 diabetes. Br J Ophthalmol. 2011;95:94–8.

    CAS  PubMed  Google Scholar 

  40. Sonmez A, Yilmaz MI, Saglam M, Kilic S, Eyileten T, Uckaya G, et al. The relationship between hemoglobin levels and endothelial functions in diabetes mellitus. Clin J Am Soc Nephrol. 2010;5:45–50.

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Dunst J, Becker A, Lautenschläger C, Markau S, Becker H, Fischer K, et al. Anemia and elevated systemic levels of vascular endothelial growth factor (VEGF). Strahlenther Onkol. 2002;178:436–41.

    PubMed  Google Scholar 

  42. Aiello LP, Pierce EA, Foley ED, Takagi H, Chen H, Riddle L, et al. Suppression of retinal neovascularization in vivo by inhibition of vascular endothelial growth factor (VEGF) using soluble VEGF-receptor chimeric proteins. Proc Natl Acad Sci USA. 1995;92:10457–61.

    CAS  PubMed  Google Scholar 

  43. Chung JO, Park SY, Chung DJ, Chung MY. Relationship between anemia, serum bilirubin concentrations, and diabetic retinopathy in individuals with type 2 diabetes. Medicine. 2019;98:e17693.

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Grune T, Sommerburg O, Siems WG. Oxidative stress in anemia. Clin Nephrol. 2000;53:S18–22.

    CAS  PubMed  Google Scholar 

  45. Kowluru RA, Mishra M. Oxidative stress, mitochondrial damage and diabetic retinopathy. Biochim Biophys Acta. 2015;1852:2474–83.

    CAS  PubMed  Google Scholar 

  46. Liu Y,Yang J,Tao L,Lv H,Jiang X,Zhang M,et al.Risk factors of diabetic retinopathy and sight-threatening diabetic retinopathy: a cross-sectional study of 13473 patients with type 2 diabetes mellitus in mainland China.BMJ Open.2017; 7:e016280.

  47. Pedro RA, Marc BB, Raul NG, Antonio MR, Aida VM, Ramon SA, et al. Glomerular filtration rate and/or ratio of urine albumin to creatinine as markers for diabetic retinopathy: a ten-year follow-up study. J Diabet Res. 2018;2018:1–9.

    Google Scholar 

  48. Phipps JA, Feener EP. The kallikrein–kinin system in diabetic retinopathy: lessons for the kidney. Kidney Int. 2008;73:1114–9.

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

This study was supported by the Endocrine Clinical Medical Center of Yunnan Province, No. ZX2019‑02‑02; the Natural Science Foundation of China (No. 81760734; No. 31660313); the Natural Science Foundation of Yunnan Province (No. 2017FA048; No. 2017FE467); the fund of the Diabetic Innovation Team (No. 2019HC002); and the fund for medical leaders in Yunnan Province (No. L‑201609).

Author information

Author notes

  1. Jin-Rui Wang and Zhongli Chen contributed equally to this study

Authors and Affiliations

  1. Department of Endocrinology and Metabolism, The Second People’s Hospital of Yunnan Province, Fourth Affiliated Hospital of Kunming Medical University, Kunming, 650021, China

    Jin-Rui Wang, Hui-Jun Yang, Wen-Yu Tao, Yi-Ping Li, Chao-Fang Bai, Yue-Chuan Yin, Jian-Mei Duan, Yuan-Yuan Zhou, Xin-Qian Geng & Ying Yang

  2. Department of Cardiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, 200001, Shanghai, China

    Zhongli Chen & Ke Yang

  3. The Second People’s Hospital of Qujing City, Yunnan, Qujing, 655000, China

    Ze-Jia Jiang

Authors
  1. Jin-Rui Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhongli Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ke Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hui-Jun Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wen-Yu Tao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yi-Ping Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ze-Jia Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Chao-Fang Bai
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yue-Chuan Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Jian-Mei Duan
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Yuan-Yuan Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Xin-Qian Geng
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Ying Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

JRW and ZLC contributed equally to analysis and manuscript preparation; YY and KY was responsible for revising the manuscript for important intellectual content and final approval of the version of the article to be published; HJY, ZZJ, CFB, JMD and YCY were responsible for collecting data and helped perform the analysis with constructive discussions. KY, WYT, YPL, YYZ and XQG revised the manuscript; All authors read and approved the final manuscript.

Corresponding author

Correspondence to Ying Yang.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the ethics committee of the Fourth Affiliated Hospital of Kunming Medical University (No.2020039).

Consent for publication

Not applicable.

Competing interests

The authors declare that they have 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, JR., Chen, Z., Yang, K. et al. Association between neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and diabetic retinopathy among diabetic patients without a related family history. Diabetol Metab Syndr 12, 55 (2020). https://doi.org/10.1186/s13098-020-00562-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13098-020-00562-y

Keywords

Diabetology & Metabolic Syndrome

ISSN: 1758-5996

Contact us