With rapidly increased prevalence, diabetes and its chronic complications have drawn more concerns of people. DN is a common complication of diabetes, it usually starts from glomerular damage indicated by micro-albuminuria. Recently, American Diabetes Association (ADA) recommended to cancel the statement of “micro-” or “macro-albuminuria” in the consideration of the continuousness of disease. On the other hand, tubulo-interstitial injury is also responsible for increased protein filtration and loss of renal function. Therefore, in this study, we defined DN as UACR > 30mg/g or eGFR < 60ml/min·1.73m2 or both, but those with eGFR < 30ml/min·1.73m2 was excluded for the purpose of early diagnosis.
Inflammation plays an important role in the development and progression of DN. NLR could be used as a marker of systemic inflammation. As previously reported, diabetic patients were prone to have higher NLR levels than those of healthy volunteers [1.93 IQR (1.43, 2.68) vs 1.61 IQR (1.31, 2.16), P < 0.001]. A longitudinal study reported that, after 3-year follow-up of diabetic patients, the lowest NLR tertile included fewer patients (2.7%) of worsening renal functions than those of the middle and the highest NLR tertiles. Based on the results of our study, NLR positively correlated to UACR in patients with type 2 diabetes, DN patients had higher NLR levels than those of non-DN patients (2.21±1.05 vs 1.67±0.71, P=0.002), corresponding to the results of previous studies[13, 21, 22, 24, 27, 28]. Some of the studies didn’t provide the values of NLR in DN and non-DN groups[3, 21]. Other clinical researches gave the mean values of NLR ranged from 1.56 to 2.20, 1.96 to 2.60 and 2.03 to 3.60 in diabetic patients with normo-, micro- and macro-alubuminuria, respectively[13, 22, 24, 29]. Apparently, there were overlaps among different groups, which further supported DN to be a kind of continuously progressed disease.
Moreover, NLR independently predicted DN diagnosis after adjusted by multi-variables, based on the results of both our study and previous studies[21, 22, 30]. However, an adequate cutoff value of NLR had never been clearly elucidated in the past. A recently published study of meta-analysis ever focused on this point of view. Regretfully, the results only provided a standardized mean difference (SMD) value of NLR (SMD = 0.63, 95%CI: 0.43-0.83, P < 0.001), which could scarcely help clinical doctors to make any decisions. ROC curve analysis had also been preformed by some of the studies. Akbas et al reported a NLR cutoff value of 1.7 in predicting albuminuria of diabetes, with a sensitivity of 61.8% and a specificity of 70.5% (AUC 0.660, 95%CI 0.590-0.725, P = 0.0001). This result is similar to the ROC analysis of our study that NLR cutoff value of 2.04 had a sensitivity of 48.9%, a specificity of 80.8%, and AUC of 0.666. However, these results also revealed that NLR, as a single predictor, had a moderate efficiency in predicting DN, let alone the fact that NLR value of 1.7 and 2.04 could barely separate DN from diabetic patients without DN (with mean NLR value from 1.56 to 2.20[13, 22, 24, 29]). A more efficient but simple model was necessary.
Based on the logistic regression analysis, we selected 4 independent factors [duration of diabetes, NLR, SBP and Lp(a)] as variables for the construction of discriminant equation in predicting DN. Therefore, model 1 was established. Using this model, the AUC was elevated to 0.819 with a sensitivity of 76.9% and a specificity of 75.3%. However, Lp(a) might not be easily acquired in some cases. Accordingly, model 2 was established excluding the factor of Lp(a), with only a slight loss of AUC (0.817) compared to model 1 (0.819), but an obvious improvement of sensitivity (74.4%) compared to NLR alone (48.9%).
In clinical practice, invasive kidney biopsy was rarely operated in the diagnosis of DN, repeated blood drawing and expensive urine test were usually opposed too. The medical expense was another concern. Routine blood test remained more easier to be accepted by most of the patients. In this situation, when the lab data of creatine and UACR were difficult to obtain, we recommended clinical practitioners to use model 1 or model 2 for quick glimpses at DN prediction. Because they were user-friendly and easily to be calculated, with an acceptable predictive accuracy, and might be easily stored as an excel document in the office computer. Especially the model 2, which need only 2 additional clinical parameters of SBP and diabetes duration, except for NLR value.
In addition, according to our study, NLR value of 2.50 with a high specificity (91.9%) is another user-friendly tool to identify the patients at high risk of DN, even if it had a fairly low sensitivity of 29.8%. It would be useful to remind doctors to pay attention to the high probability of DN, and persuade this part of patients to accept necessary blood or urine tests further. For another important reason, as clinical doctors, we deeply understood that there were too many data need to be remember in daily work, and too many trivial things to distract our attentions, so a very simple but efficient tool would be more fascinated. Above all, even the discriminant analysis provided a method of better overall accuracy in predicting DN, we still strongly recommended the NLR threshold of 2.50 as the clinical use to locate the patients at high risk of DN, irrespective of the low sensitivity. Because it’s pretty simple and easy to be remembered. With this tool alone, we might specifically determine around 1/3 patients with DN only, but without it, we may lose all of them.
With regards to the inner relationship between NLR and DN, in a hypothesis, excessive nutrients could activate the pancreatic islets, liver, adipose and muscle tissues to release chemokine and cytokines, such as tumor necrosis factor-α (TNF-α), interleukin-1 (IL-1) and interleukin-8 (IL-8, also called CXCL-8). They could help to recruit immune cells and promote inflammation. In particular, IL-8 was the specific chemokine of neutrophils. When binding to CXCR-1 and CXCR-2, IL-8 could induce chemotaxis, migration, aggregation and activation of neutrophils, and participate in tissue injury and repair. A previous animal study had shown that IL-8 antagonist could reduce renal volume and UACR level, improve creatinine clearance in male mice with diabetes, attenuate high glucose induced mesangial injury, and inhibit JAK2/STAT3 and ERK1/2 pathways at molecular level. In vivo study had also indicated an elevated level of urinary CXCL-8 of DN patients as compared to those of control.
Our study had several limitations. First, this was a retrospective study with relatively small sample size, leading to the restricted generalization of the results. Validation of some large-scale studies remained necessary. Second, a validation cohort is absent, even though the results of our study were quite close to those of previously reported. We also look forward other researchers to verifying our results using their reported data set. Third, the factors impact on NLR were not completely expelled, like the most frequent complications of hypertension and dyslipidemia. Of course, we’ve already tried to exclude their influences by performing multi-variable regression analysis. On the other hand, they were truly existing in the real world, and probably shared a common mechanism with diabetes and DN.