Yijue Wang1,2,3†
Boyu Cai
Nannan Zhang- 1National Center for Birth Defect Monitoring, Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
- 2West China School of Stomatology, Sichuan University, Chengdu, Sichuan, China
- 3Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, Sichuan University, Chengdu, Sichuan, China
- 4Scaled Manufacturing Center of Biological Products, Management Office of National Facility for Translational Medicine, West China Hospital of Sichuan University, Chengdu, China
- 5College of Life Sciences, Sichuan University, Chengdu, Sichuan, China
Introduction: The significance of immune-inflammation indexes in diabetic nephropathy (DN) was assessed in this meta-analysis to offer guidance for clinical diagnosis and treatment for DN.
Methods: We performed a meta-analysis on the association between immune-inflammation indexes and the incidence and prognosis of DN, specifically focusing on the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI). We thoroughly searched PubMed, Web of Science, Embase, and Cochrane from inception to September 2024. The statistical analysis was performed using R 4.2.3 software.
Results: 56 studies were ultimately included, comprising 50 that examined the association between DN incidence and immune-inflammation indexes and 8 that examined the association between DN prognosis and immune-inflammation indexes. The levels of NLR, MLR, PLR, and SII were significantly higher in DN patients than in non-DN ones. Besides, high NLR, MLR, SII, and SIRI were associated with elevated incidence of DN. Moreover, the high NLR group was more prone to a poor prognosis than the low NLR group (OR: 1.372, 95% CI: 1.160-1.624).
Conclusions: Immune-inflammation indexes can, to a certain extent, serve as a biomarker to predict the occurrence of DN. In addition, high NLR has a potential association with the occurrence of poor prognosis in DN.
1 Introduction
Diabetic nephropathy (DN) is one of the most prevalent and severe chronic microvascular complications of diabetes (1), clinically characterized by progressive renal hypofunction, with or without proteinuria, which affects approximately 25%-40% of diabetes mellitus patients (2). The global incidence of DN constantly rises, and it is reported that its incidence is expected to increase by about 50% over the next two decades, resulting in approximately 783 million patients worldwide (3). DN has nowadays been the major cause of chronic kidney disease (CKD) and end-stage renal disease(ESRD) requiring dialysis or transplantation, placing a heavy burden on the economy and public health systems globally (4). However, DN has often been in an intermediate to advanced stage once persistent proteinuria develops due to insidious and progressive onset, greatly increasing the difficulty of treatment and leading to a poor prognosis (5). Moreover, a radical cure for DN remains an unfulfilled medical requirement, so early screening and detection and timely control of DN are critical to patients’ quality of life and prognosis.
Chronic inflammation, inflammation, and oxidative stress play important roles in DN progression (6, 7). As confirmed by several studies, inflammatory factors including chemokines, TNF-α, adhesion molecules, and interleukins (8, 9) are significant contributors to the development of DN (6, 10). Inflammatory factors cause inflammatory infiltration and injury in renal tissue by participating in the recruitment and infiltration of inflammatory cells, and affect the structure and function of the kidney by promoting the proliferation of renal mesangial cells and the deposition of extracellular matrix. However, these cytokines are high in cost of analysis and thus are not routinely used in clinical practice. Novel immune-inflammation indexes developed based on hemogram parameters (neutrophil/lymphocyte/platelet counts) commonly include platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index (SII) [(neutrophil count × platelet count)/lymphocyte count], systemic inflammation response index (SIRI) [(neutrophil count × monocyte count)/lymphocyte count] (11). These indexes provide a more sensitive picture of the immune-inflammation balance in the body than a single blood cell count (12). Moreover, immune-inflammation indexes that are simple in calculation and easy to access have been applied as new markers for systemic inflammatory response in a variety of diseases and are also recognized as independent predictors for incidence, mortality, and long-term survival rate in many clinical settings (13–15). The association of immune-inflammation indexes with DN remained controversial in previous retrospective studies. A paired study found no correlation between NLR and DN among 1192 patients with Type 2 diabetes mellitus (T2DM) (16), whereas more studies have suggested the correlation of NLR with DN (12, 17). One of the possible reasons for this contradiction is an insufficient sample size of a single study, making the statistical validity questionable. Therefore, the association of immune-inflammation indexes with DN requires further evidence-based study.
Liu et al. described NLR’s correlation with DN in a meta-analysis (2), but they failed to convincingly clarify the relationship between NLR and DN grade due to the limited studies included (2). Therefore, this meta-analysis was conducted on all available studies on the association of immune-inflammation indexes with the incidence and prognosis of DN. This study intends to assess the value of immune-inflammation indexes for predicting DN incidence, progression, and prognosis, hoping to offer references for decision-making of clinical diagnosis and treatment of DN. Meanwhile, timely monitoring of the changes in these indexes in T2DM patients may also offer new ideas and methods for DN prevention and treatment.
2 Materials and methods
2.1 Search strategy
This meta-analysis was performed following the statement of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). We searched PubMed, Web of Science, Embase, and Cochrane from inception to September 2024. Medical subject headings and keywords were used: Diabetic Kidney Disease, Lymphocytes, Monocytes, and Neutrophils. The search strategy and search terms are provided in Supplementary Table S1. This meta-analysis was registered with PROSPERO (CRD42024578732).
2.2 Study selection
Inclusion criteria: i) case-control studies with the expression profile of blood-derived immune-inflammation indexes (NLR, PLR, MLR, SII, and SIRI) in T2DM patients with or without DN; ii) case-control studies reporting odds ratios (ORs), as well as studies presenting sufficient data to compute ORs or reporting ORs derived from multivariable analyses; iii) cohort studies reporting the incidence rate or prognosis of T2DM patients with DN under different levels of immune-inflammation indexes over the follow-up period; iv)T2DM patients diagnosed with DN based on the criteria established by the American Diabetes Association (18). Notably, the outcome (prognosis) of DN was described as either of the following: a) all-cause mortality, b) cardiovascular mortality, c) rapid eGFR decline, or d) renal failure. Decreasing in eGFR of ≥ 25% from baseline during the follow-up was defined as an eGFR decline.
Exclusion criteria: i) duplicate publications; ii) animal or cell studies; iii) editorials, letters, meeting abstracts, and comments; iv) systematic reviews or meta-analyses.
The references of original studies were manually searched. Two researchers (WYJ and LY) were responsible independently for the study screening and selection, and the results were checked by a third researcher (ZNN).
2.3 Data extraction and quality evaluation
Two researchers (WYJ and LY) extracted the following data independently: i) study characteristics: author, study name and year, study period, region, and study design; ii) patient demographics: population, DN diagnostic criteria, immune-inflammation indexes, sample size, gender distribution, age, HbA1c, Albuminuria (microalbuminuria, macroalbuminuria), eGFR, and duration of disease; iii) pooled OR with 95% Cl for the association of immune-inflammation indexes with DN; iv) values of immune-inflammation indexes (mean ± standard deviation) in T2DM patients with or without DN. Albuminuria including microalbuminuria and macroalbuminuria defined as 30 mg/g ≤albumin-to-creatinine ratio (UACR)≤ 300 mg/g or UACR > 300 mg/g.
The modified Newcastle-Ottawa Scale (NOS) (19) was used for quality evaluation from selection, comparability, and exposure/outcome. Each study was rated as low (0–4), moderate (5–6), and high quality (7–9).
2.4 Statistical analysis
This study reported the incidence and prognosis of DN (Figure 1). Categorical and continuous variables that satisfied the inclusion criteria were documented. Outcomes were reported as the pooled OR, SMD, and 95% CI, and the interquartile range or median was transformed into mean ± SD by a standard approach (20, 21). The I² test was performed for heterogeneity, and P<0.1 and I2>50% were indicative of high heterogeneity, and then a random-effects model was utilized for all analyses. Subgroup analyses were conducted based on the region, age, sample size, HbA1c, albuminuria, eGFR, and duration of disease to explore the source of heterogeneity. Sensitivity analyses were performed on the overall results, which were not conducted if the number of studies was limited (less than three). Publication bias was explored by Egger’s tests and funnel plots, which were not conducted if the number of studies was limited (less than ten). R 4.2.3 was adopted for statistical analyses.
Figure 1. Flow diagram based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis showing the method of identifying trials and reasons for exclusion.
3 Results
3.1 Study characteristics
Initially, 2017 studies were retrieved from the databases, and two studies (16, 22) were obtained by manual search. The article filtering process is shown in Figure 1. Ultimately, this meta-analysis included 56 eligible studies (12, 16, 17, 22–74). Among them, 48 studies (12, 17, 24–27, 29, 31–58, 60–65, 67–71, 73, 74) only reported incidence-related data, six (22, 23, 30, 59, 66, 72) only presented prognosis-related data, and two (17, 28) provided both. There were 47 case-control studies, eight cohort studies, and one case-control plus cohort study. 20 studies were conducted in China (12, 16, 29, 30, 32, 35, 36, 40–42, 52–54, 56, 65, 67, 68, 72–74), 11 in Turkey (24, 33, 34, 46, 49, 51, 58, 63, 64, 70, 71), 10 in India (17, 26, 27, 38, 45, 50, 60–62, 69), four in US (22, 37, 55, 72), three in Japan (23, 48, 59), and one article from each of the other countries. NLR was investigated in 47 studies (16, 17, 22–28, 30–36, 38–42, 44–50, 52–62, 64–67, 70, 72–74), PLR in 15 studies (28, 29, 41, 43, 55, 71), MLR in six studies (24, 28, 32, 34, 44, 45, 47, 52, 54–56, 58, 62, 69, 71), SII in eight studies (12, 22, 37, 52, 55, 62, 63, 68), and SIRI in two studies (12, 55). Notably, 15 studies (12, 22, 24, 28, 32, 37, 44, 45, 47, 52, 54–56, 58, 62) reported the association of immune-inflammation indexes with DN (Supplementary Table S2). In addition, the NOS scores of the included studies were 6-8 (Supplementary Tables S3&4), suggesting moderate to high quality.
3.2 Association of immune-inflammation indexes with DN incidence: meta-analysis
3.2.1 Differences in NLR levels between DN and non-DN patients
The meta-analysis covered 48 datasets from 35 studies (16, 17, 22, 24, 25, 27, 31–35, 38–42, 44–50, 52–54, 56–58, 60–62, 64, 65, 70, 73) containing 9,266 DN patients and 13,829 non-DN patients (control group) (Figure 2A). A random-effects model was adopted due to significant heterogeneity among the included studies (I2 = 100.0%, P<0.001). The level of NLR was higher in DN patients than in non-DN patients (SMD=1.737, 95% CI: 0.813-2.662).
Figure 2. Forest plots illustrating the outcomes of the connection between NLR and DN incidence. (A) Forest plots for NLR levels in DN patients; (B) Forest plots for incidence of DN in high NLR and low NLR. NLR, Neutrophil-to-Lymphocyte Ratio.
Subgroup analyses revealed no significant difference in heterogeneity (Table 1). The predictive value of NLR as a continuous variable for DN vanished in subgroups with age greater than 60 years, sample size greater than 310, and eGFR less than 90 mL/min/1.73 m2, however, it still had statistical significance in other subgroups.
Table 1. Subgroup analysis of the relationship between NLR (Continuous & Categorical), PLR (Continuous) with DN based on Region, Age, Sample size, HbA1c, albuminuria, eGFR, and Disease duration year.
3.2.2 DN incidence in high and low NLR groups
The meta-analysis covered 22 datasets from 13 studies (17, 26, 28, 36, 52, 54–56, 64, 67, 72–74) (Figure 2B). A random-effects model was adopted due to significant heterogeneity (I2 = 100%, P<0.01). It was found that the high NLR group had an incidence of DN 1.94 times higher than the low NLR group (OR=1.941, 95% CI: 1.609-2.341), suggesting a close association of high NLR with DN.
Subgroup analyses showed that the high heterogeneity in the pooled result might be attributed to variations in influencing factors like region, sample size, HbA1c, eGFR, and disease duration (Table 1). No statistically significant difference was observed among subgroups (Table 1).
3.2.3 Differences in PLR levels between DN and non-DN patients
Seventeen datasets from 13 studies (24, 32, 34, 44, 45, 47, 52, 54, 56, 58, 62, 69, 71) containing 1,925 DN patients and 2,802 non-DN patients (control group) were incorporated into the meta-analysis (Figure 3A). A random-effects model was adopted due to significant heterogeneity (I2 = 93.0%, P<0.001). DN patients had higher PLR levels than non-DN ones (SMD=0.637, 95% CI: 0.307-0.967).
Figure 3. Forest plots illustrating the outcomes of the connection between PLR and DN incidence. (A) Forest plots for PLR levels in DN patients; (B) Forest plots for incidence of DN in high PLR and low PLR. PLR, Platelet-to-Lymphocyte Ratio.
Subgroup analyses revealed that the variation in UACR might contribute to considerable heterogeneity (Table 1). The predictive value of NLR as a continuous variable for DN vanished in subgroups with patients from America, HbA1c greater than 8%, and eGFR greater than 90 mL/min/1.73 m2, but it still had statistical significance in other subgroups.
3.2.4 DN incidence in high and low PLR groups
Five datasets from four studies (28, 52, 54, 55) were incorporated into the meta-analysis (Figure 3B). A random-effects model was adopted due to significant heterogeneity (I2 = 70%, P<0.01). The DN incidence displayed no statistically significant difference between the high and low PLR groups (OR=1.279 1, 95% CI: 0.917-1.784).
3.2.5 Differences in MLR levels between DN and non-DN patients
Five datasets from four studies (29, 41, 43, 71) containing 276 DN patients and 498 non-DN patients (control group) provided data for the meta-analysis (Figure 4A). A random-effects model was utilized due to high heterogeneity (I2 = 78.0%, P<0.01). DN patients had higher MLR levels than non-DN ones (SMD=0.830, 95% CI: 0.207-1.453).
![Two forest plots representing meta-analysis data. Panel A: Studies compared based on standard mean difference. The plot shows green squares and horizontal lines for confidence intervals, with a diamond at the bottom representing overall effect (0.830 [0.207; 1.453]). Heterogeneity is indicated (I² = 78%). Panel B: Studies compared based on odds ratio. The plot shows red squares and horizontal lines for confidence intervals, with a diamond at the bottom representing overall effect (2.728 [1.259; 5.911]). Heterogeneity is indicated (I² = 87%).](https://www.frontiersin.org/files/Articles/1532682/fendo-16-1532682-HTML/image_m/fendo-16-1532682-g004.jpg)
Figure 4. Forest plots illustrating the outcomes of the connection between MLR and DN incidence. (A) Forest plots for MLR levels in DN patients; (B) Forest plots for incidence of DN in high MLR and low MLR. MLR, Monocyte-to-Lymphocyte Ratio.
3.2.6 DN incidence in high and low MLR groups
Five datasets from four studies (28, 29, 41, 55) provided data for the meta-analysis (Figure 4B). A random-effects model was utilized due to high heterogeneity (I2 = 70%, P<0.01). It was found that the high MLR group had an incidence of DN 2.73 times higher than the low MLR group (OR=2.728, 95% CI: 1.259-5.911).
3.2.7 Differences in SII levels between DN and non-DN patients
The meta-analysis included nine datasets from seven studies (12, 22, 37, 52, 62, 63, 68) containing 6,530 DN patients and 10,003 non-DN patients (control group) (Figure 5A). A random-effects model was utilized due to high heterogeneity (I2 = 100.0%, P<0.001). DN patients had higher SII levels than non-DN ones (SMD=5.412, 95% CI: 0.708-10.116).
Figure 5. Forest plots illustrating the outcomes of the connection between SII and DN incidence. (A) Forest plots for SII levels in DN patients; (B) Forest plots for incidence of DN in high SII and low SII. SII, Systemic Immune-Inflammation Index.
3.2.8 DN incidence in high and low SII groups
The meta-analysis included nine datasets from five studies (12, 37, 52, 55, 68) (Figure 5B). A random-effects model was utilized due to high heterogeneity (I2 = 79%, P<0.01). It was found that the high SII group had an incidence of DN 1.19 times higher than the low SII group (OR=1.189, 95% CI: 1.048-1.349).
3.2.9 DN incidence in high and low SIRI groups
The meta-analysis was conducted with three datasets from two studies (12, 55) (Supplementary Figure S1). A random-effects model was adopted. It was found that the high SIRI group had an incidence of DN 2.20 times higher than the low SIRI group (OR=2.197, 95% CI: 1.545-3.124). There was no heterogeneity (I2 = 0%, P=0.57).
3.3 Association of immune-inflammation indexes with DN prognosis: meta-analysis
Twelve datasets from eight studies (16, 22, 23, 28, 30, 59, 66, 72) containing 15,670 patients reported the relationship between high NLR and poor prognosis of DN (Supplementary Figure S2). Analysis of the pooled effect showed that the high NLR group was more prone to a poor prognosis than the low NLR group (OR: 1.372, 95% CI: 1.160-1.624, I2 = 83%).
Subgroup analyses were conducted based on different outcomes. Analysis of the pooled effect showed that the high NLR group had cardiovascular mortality and incidence of renal failure in DN 1.75 and 1.10 times, respectively, higher than the low NLR group (Table 2). However, all-cause mortality and eGFR decline had no statistically significant difference between the two groups. Besides, the variation in these outcomes might contribute to considerable heterogeneity.
Table 2. Subgroup analysis of the relationship between NLR with DN prognosis based on outcome.
3.4 Sensitivity analyses
Leave-one-out sensitivity analyses were performed to examine the stability of the results (Supplementary Figures S3, S4). The pooled results of “Differences in MLR levels between DN and non-DN patients” became statistically no significant after the study “Chen 2024-a” “Huang 2020-b” and “Koack 2020” were removed (Supplementary Figure S3E). The pooled results of “ Differences in SII levels between DN and non-DN patients” became statistically no significant after the study “Guo 2022-b” “Survarna 2023-c” “Taslamacioglu 2023” “Yan 2023-a” and “Yan 2023-b” were removed (Supplementary Figure S3G). This may suggest a degree of uncertainty regarding the robustness of the pooled results for continuous variables in MLR and SII. However, the other results demonstrated stability, indicating that the meta-analysis results were robust despite significant heterogeneity among the included studies.
3.5 Publication bias
Funnel plots were used to evaluate publication bias in the combined results of more than 10 studies included (Figure 6; Supplementary Figure S5). The Egger’s test was further used to evaluate the asymmetry observed in the funnel plot. The results suggest that the following combined analysis may have publication bias: “3.2.1 Differences in NLR Levels between DN and non-DN Patients” (P=0.046), “3.2.2 Incidence of DN in High and low NLR Groups” (P<0.001), “3.2.3 Differences in PLR Levels between DN and non-DN Patients” (P=0.022), and “3.3 The correlation between immune inflammatory indicators and the prognosis of DN (P<0.001).
Figure 6. Funnel plot of publication bias between immune-inflammation index and DN incidence. (A) Funnel plot for NLR continues (3.2.1); (B) Funnel plot for NLR categorical (3.2.2); (C) Funnel plot for PLR continuous (3.2.3).
4 Discussion
Systemic inflammation is increasingly implicated in the pathogenesis and poor prognosis of DN (75). Hematological studies in T2DM patients show elevated leukocy (76, 77), indicating an active inflammatory response that may drive disease progression. Given the limitations of traditional markers like serum creatinine and proteinuria, novel indicators are needed. Immune-inflammation indexes, including NLR, PLR, MLR, SII, and SIRI, provide sensitive assessments of systemic inflammation (11). This study is the first large-scale analysis (56 studies, 53,278 participants) to examine their roles in DN. Notably, elevated NLR was associated with increased risks of adverse outcomes, including cardiovascular mortality and renal failure progression, highlighting its potential as a prognostic biomarker for DN. This study indicates that elevated immune-inflammatory indices are associated with the development and progression of DN, thereby offering clinicians a novel means to aid in the prevention of DN onset and the monitoring of its progression.
Liu et al. (2018) reported the expression changes of NLR in DN and found that NLR was significantly elevated in patients with DN (SMD = 0.63) (2). Consistent with these previous findings, our study demonstrated that the incidence of DN in the high NLR group was 1.94 times that of the low NLR group, and that NLR was significantly increased in DN patients (SMD = 1.73). Building upon Liu’s foundational work, our study leveraged the most up-to-date data, with a broader search scope, a larger number of included studies, and a substantially greater sample size. Moreover, our research not only assessed NLR but also incorporated several emerging immune-inflammatory markers such as PLR, MLR, SII, and SIRI, providing a comprehensive and systematic analysis of their associations with DN risk. Notably, we were the first to quantitatively analyze the relationship between high NLR and DN prognosis across multiple studies. Our conclusions not only reinforce the predictive value of NLR for the occurrence of DN but also systematically summarize its prognostic significance in DN. Compared to previous studies, our research offers a more comprehensive perspective and greater clinical relevance. Besides, the sensitivity analyses demonstrated the stability of our results.
Moreover, subgroup analyses were conducted to identify the source of heterogeneity (78, 79). First, the significant heterogeneity in the pooled results of NLR as a categorical variable could be attributed to the combination of several confounders. Specifically, inter-study geographic differences, diversity of HbA1c levels, sample size, inconsistency in eGFR, and variability in disease duration could explain the heterogeneity in DN incidence in the high NLR group. However, the heterogeneity in the results of NLR as a continuous variable was not adequately explained by the subgroup analyses. The heterogeneity in the results of PLR as a continuous variable was possibly related to proteinuria. Notably, subgroup analyses revealed higher pooled effect sizes for PLR in patients with macroalbuminuria than those with microalbuminuria, suggesting a potential association between immune-inflammation indexes and renal function in DN patients.
This study focused on NLR’s association with DN prognosis. As reported previously, NLR is associated with adverse outcomes of various diseases (cardiovascular disease (80), T2DM (81), coronary artery disease (6), malignancies (14, 82), and sepsis (83)), with enhanced chronic inflammation and elevated NLR considered as its pathogenesis (6, 84). However, the association of NLR with DN and its prognosis is poorly understood. To our knowledge, this meta-analysis filled the research gap by analyzing the association of high NLR with the adverse prognosis of DN for the first time. The results of the meta-analysis showed that there was a certain correlation between high NLR and poor prognosis in patients with DN, consistent with previous studies (28, 30, 59, 85, 86). However, subgroup analyses indicated that NLR demonstrated potential predictive value for cardiovascular mortality and renal failure progression in patients with DN. Nevertheless, no statistically significant associations were observed with all-cause mortality or eGFR decline. This discrepancy may stem from the limited number of included studies, substantial sample heterogeneity, and a paucity of high-quality prospective investigations, collectively compromising the robustness and statistical power of these specific findings. Based on the current evidence, NLR shows promise for predicting adverse outcomes in the DN population. However, before NLR becomes an effective prognostic prediction tool, more cohort tracking data support is still needed. Future research should prioritize large-scale, multicenter longitudinal studies to definitively establish clinical thresholds for various immune-inflammatory biomarkers and validate their practical utility in prognostic assessment for DN.
An accumulating body of research has recently indicated the key role of inflammatory responses in DN development (8, 44). Neutrophils are important elements in the inflammatory response, and they can be activated by metabolic disorders such as hyperglycemia. Then activated neutrophils can release such inflammatory mediators as IL-1, TNF-α, chemokines, and ROS, which can further worsen the inflammatory response and injury in renal tissues (87). Monocytes can also be activated upon stimulation with inflammatory factors to release inflammatory mediators and participate in fibrosis, worsening the inflammation and injury of renal tissues and thus facilitating DN progression (87, 88). In addition, activated lymphocytes may be implicated in the fibrosis of renal tissues by releasing growth factors and cytokines, thus promoting glomerulosclerosis and interstitial fibrosis and aggravating the pathological changes of DN (89, 90). Activated platelets in DN can release growth factors and pro-fibrotic factors and interact with endothelial cells to facilitate endothelial cell injury and fibrosis, leading to glomerulosclerosis and interstitial fibrosis as well as vascular endothelial dysfunction, worsening renal microcirculatory disorders, and tissue hypoxia, ultimately promoting DN development. Meanwhile, abnormally activated platelets may exacerbate vascular injury and microcirculatory disorders, causing renal ischemia and reperfusion injury, and further aggravating kidney injury (89, 91).
Different immune-inflammation indexes correspond to different inflammation statuses in DN. Specifically, elevated NLR in DN suggests enhanced inflammation and immune cell activity, increased release of inflammatory mediators, and inflammation-related injury. Elevated PLR implies more active inflammatory responses in DN and may also correlate with increased platelet activation. Then platelet activation and aggregation may lead to thrombosis (92). Abnormally elevated SII suggests systemic inflammation and increases in the systemic pain index and inflammatory markers (CRP, WBC, and NLR), while high levels of inflammatory markers can affect the vascular endothelial cell function and increase oxidative stress and fibrosis, thus damaging the structure and function of glomerular filtration membrane, and ultimately facilitating DN development. These immune-inflammation indexes with the above characteristics provide important clues for knowing the inflammation status in DN, which can help physicians develop more effective treatment strategies and monitor disease progression. A meta-analysis has shown that anti-inflammatory therapy can effectively lower the risk of cardiovascular events in T2DM patients, suggesting that targeting inflammation can reduce the risk of diabetic complications (93). Future studies are required to further identify whether DN patients with elevated NLR or other inflammation indexes can benefit from anti-inflammatory therapies and interventions, thereby ameliorating their quality of life and prognosis.
However, this meta-analysis still had some limitations worth considering. First, all of the eligible data originated from Asia and the Americas, especially China, Turkey, the United States, and India. Therefore, the conclusions should be interpreted in this geographic context and generalized with caution to Europe, Africa, and other regions. In addition, even after subgroup analyses, some of the pooled results (e.g., the pooled results of NLR as a continuous variable) still had heterogeneity that could not be fully explained. Although it was difficult to identify the source of heterogeneity, it was hypothesized that race, treatment, and other factors possibly had a potential impact on the heterogeneity in the included studies. Notably, the inability to standardize cut-off values may be a source of heterogeneity. This is primarily because the cut-off values varied greatly across studies, and a considerable proportion of the reported ORs were derived from multivariate analyses, which did not provide information on the specific cut-off values used (37, 41). Finally, it was confirmed by funnel plots and Egger’s tests that the pooled results were affected by publication bias, and therefore the conclusions of this meta-analysis should be interpreted with the potential impact of publication bias considered.
5 Conclusion
In conclusion, the association of blood-derived immune-inflammation indexes with the incidence and prognosis of DN was comprehensively assessed in this meta-analysis. High-level immune-inflammation indexes may serve as predictors for DN incidence, and high NLR is potentially associated with the occurrence of poor prognosis of DN. In the future, more longitudinal studies are needed to clarify the association between immune-inflammation indexes and DN prognosis. This study offers realistic support to the role of systemic inflammation in DN onset and progression and reveals the significant potential of immune-inflammation indexes as biomarkers of inflammation for assessing the risk and prognosis of DN.
Author contributions
YW: Conceptualization, Formal analysis, Investigation, Writing – original draft, Writing – review & editing. YL: Conceptualization, Writing – review & editing. WG: Methodology, Writing – review & editing. BC: Methodology, Writing – review & editing. ML: Supervision, Writing – review & editing. YYL: Supervision, Writing – review & editing. NZ: Funding acquisition, Supervision, Writing – review & editing.
Funding
The author(s) declare financial support was received for the research and/or publication of this article. This study was supported by the Natural science foundation project of Sichuan (2024NSFSC0599), the Key Research and Development Program of Chengdu (2023-YF09-00052-SN) and the Young Teachers’ Science and Technology Innovation Capability Enhancement Project of Sichuan University (2024SCUQJTX037).
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declare that no Generative AI was used in the creation of this manuscript.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2025.1532682/full#supplementary-material
Supplementary Figure 1 | Forest plots showing the outcomes for incidence of DN in high SIRI and low SIRI. SIRI, Systemic Inflammation Response Index.
Supplementary Figure 2 | Forest plots showing the outcomes for prognosis of DN in high NLR and low NLR.
Supplementary Figure 3 | The sensitive analysis of selected studies. (A) NLR levels in DN patients;(B) incidence of DN in high NLR and low NLR;(C) PLR levels in DN patients;(D) incidence of DN in high PLR and low PLR;(E) MLR levels in DN patients; (F) incidence of DN in high MLR and low MLR;(G) SII levels in DN patients;(H) incidence of DN in high SII and low SII;(I) incidence of DN in high SIRI and low SIRI
Supplementary Figure 4 | The sensitive analysis of the prognosis of DN in high NLR and low NLR.
Supplementary Figure 5 | Funnel plot of publication bias between NLR and DN prognosis.
Abbreviations
DN, nephropathy; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; MLR, monocyte-to-lymphocyte ratio; SII, systemic immune-inflammation index; SIRI: systemic inflammation response index; CKD, chronic kidney disease; ESRD, end-stage renal disease.
References
1. Chen J, Liu Q, He J, and Li Y. Immune responses in diabetic nephropathy: Pathogenic mechanisms and therapeutic target. Front Immunol. (2022) 13:958790. doi: 10.3390/kidneydial2030038
2. 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. doi: 10.1042/bsr20180172
3. Hoogeveen EK. The epidemiology of diabetic kidney disease. Kidney Dialysis. (2022) 2(3):433–42. doi: 10.3390/kidneydial2030038
4. Liao J, Wei D, Sun C, Yang Y, Wei Y, and Liu X. Prognostic value of the combination of neutrophil-to-lymphocyte ratio, monocyte-to-lymphocyte ratio and platelet-to-lymphocyte ratio on mortality in patients on maintenance hemodialysis. BMC Nephrol. (2022) 23(3):393. doi: 10.1186/s12882-022-03020-1
5. Cleveland KH and Schnellmann RG. Pharmacological Targeting of Mitochondria in Diabetic Kidney Disease. Pharmacol Rev. (2023) 75(2):250–62. doi: 10.1124/pharmrev.122.000560
6. Qiao S, Gao W, and Guo S. Neutrophil-Lymphocyte Ratio (NLR) for Predicting Clinical Outcomes in Patients with Coronary Artery Disease and Type 2 Diabetes Mellitus: A Propensity Score Matching Analysis. Ther Clin Risk Manage. (2020) 16:437–43. doi: 10.2147/tcrm.S244623
7. Rayego-Mateos S, Rodrigues-Diez RR, Fernandez-Fernandez B, Mora-Fernández C, Marchant V, Donate-Correa J, et al. Targeting inflammation to treat diabetic kidney disease: the road to 2030. Kidney Int. (2023) 103(2):282–96. doi: 10.1016/j.kint.2022.10.030
8. Wang Y, Zhao SY, Wang YC, Xu J, and Wang J. The immune-inflammation factor is associated with diabetic nephropathy: evidence from NHANES 2013-2018 and GEO database. Sci Rep. (2024) 14(1):17760. doi: 10.1038/s41598-024-68347-1
9. Cai A, Shen J, Yang X, Shao X, Gu L, Mou S, et al. Dapagliflozin alleviates renal inflammation and protects against diabetic kidney diseases, both dependent and independent of blood glucose levels. Front Immunol. (2023) 14:1205834. doi: 10.3389/fimmu.2023.1205834
10. Rayego-Mateos S, Morgado-Pascual JL, Opazo-Ríos L, Guerrero-Hue M, García-Caballero C, Vázquez-Carballo C, et al. Pathogenic Pathways and Therapeutic Approaches Targeting Inflammation in Diabetic Nephropathy. Int J Mol Sci. (2020) 21(11):3798. doi: 10.3390/ijms21113798
11. Liu YC, Chuang SH, Chen YP, and Shih YH. Associations of novel complete blood count-derived inflammatory markers with psoriasis: a systematic review and meta-analysis. Arch Dermatol Res. (2024) 316(6):228. doi: 10.1007/s00403-024-02994-2
12. Liu W, Zheng S, and Du X. Association of Systemic Immune-Inflammation Index and Systemic Inflammation Response Index with Diabetic Kidney Disease in Patients with Type 2 Diabetes Mellitus. Diabetes Metab Syndr Obes. (2024) 17:517–31. doi: 10.2147/dmso.S447026
13. Peng L, Liu L, Chai M, Cai Z, and Wang D. Predictive value of neutrophil to lymphocyte ratio for clinical outcome in patients with atrial fibrillation: a systematic review and meta-analysis. Front Cardiovasc Med. (2024) 11:1461923. doi: 10.3389/fcvm.2024.1461923
14. Pei B, Zhang J, Lai L, and Chen H. Neutrophil-to-lymphocyte ratio as a predictive biomarker for hyperprogressive disease mediated by immune checkpoint inhibitors: a systematic review and meta-analysis. Front Immunol. (2024) 15:1393925. doi: 10.3389/fimmu.2024.1393925
15. Shevchenko I, Grigorescu CC, Serban D, Cristea BM, Simion L, Gherghiceanu F, et al. The Value of Systemic Inflammatory Indices for Predicting Early Postoperative Complications in Colorectal Cancer. Medicina (Kaunas). (2024) 60(9). doi: 10.3390/medicina60091481
16. Zhang R, Chen J, Xiong Y, Wang L, Huang X, Sun T, et al. Increased neutrophil count Is associated with the development of chronic kidney disease in patients with diabetes. J Diabetes. (2022) 14(7):442–54. doi: 10.1111/1753-0407.13292
17. Rakesh B, Pradeep N, and Nischal G. Correlation between Neutrophil to Lymphocyte Ratio and Urine Albumin to Creatinine Ratio in Diabetic Nephropathy Patients: A Cross-sectional Study. J Clin Diagn Res. (2024) 18(5):OC33–OC7. doi: 10.7860/jcdr/2024/68347.19405
18. Committee ADAPP. 11. Chronic Kidney Disease and Risk Management: Standards of Medical Care in Diabetes—2022. Diabetes Care. (2021) 45(Supplement_1):S175–S84. doi: 10.2337/dc22-S011
19. Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol. (2010) 25(9):603–5. doi: 10.1007/s10654-010-9491-z
20. Shi J, Luo D, Weng H, Zeng XT, Lin L, Chu H, et al. Optimally estimating the sample standard deviation from the five-number summary. Res Synth Methods. (2020) 11(5):641–54. doi: 10.1002/jrsm.1429
21. Luo D, Wan X, Liu J, and Tong T. Optimally estimating the sample mean from the sample size, median, mid-range, and/or mid-quartile range. Stat Methods Med Res. (2018) 27(6):1785–805. doi: 10.1177/0962280216669183
22. Xie R, Bishai DM, Lui DTW, Lee PCH, and Yap DYH. Higher Circulating Neutrophil Counts Is Associated with Increased Risk of All-Cause Mortality and Cardiovascular Disease in Patients with Diabetic Kidney Disease. Biomedicines. (2024) 12(8):1907. doi: 10.3390/biomedicines12081907
23. Akase T, Kawamoto R, Ninomiya D, Kikuchi A, and Kumagi T. Neutrophil-to-lymphocyte ratio is a predictor of renal dysfunction in Japanese patients with type 2 diabetes. Diabetes Metab Syndr. (2020) 14(4):481–7. doi: 10.1016/j.dsx.2020.04.029
24. Akbas EM, Demirtas L, Ozcicek A, Timuroglu A, Bakirci EM, Hamur H, et al. Association of epicardial adipose tissue, neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio with diabetic nephropathy. Int J Clin Exp Med. (2014) 7(7):1794–801.
25. Assulyn T, Khamisy-Farah R, Nseir W, Bashkin A, and Farah R. Neutrophil-to-lymphocyte ratio and red blood cell distribution width as predictors of microalbuminuria in type 2 diabetes. J Clin Lab Anal. (2020) 34(7):e23259. doi: 10.1002/jcla.23259
26. Bhattacharyya S, Jain N, Verma H, and Sharma K. A Cross-sectional Study to Assess Neutrophil Lymphocyte Ratio as a Predictor of Microvascular Complications in Type 2 Diabetes Mellitus Patients. J Clin Diagn Res. (2021) 15(7):OC59–62. doi: 10.7860/jcdr/2021/47046.15266
27. Bloch MH, Iqbal F, Shafiq N, and Bhatti A. Role of neutrophil / lymphocyte ratio in diabetes 2 nephropathy. Med Forum Monthly. (2020) 31(7):29–32.
28. Cardoso CRL, Leite NC, and Salles GF. Importance of hematological parameters for micro- and macrovascular outcomes in patients with type 2 diabetes: the Rio de Janeiro type 2 diabetes cohort study. Cardiovasc Diabetol. (2021) 20(1):133. doi: 10.1186/s12933-021-01324-4
29. Chen J, Li Z-Y, Xu F, Wang C-Q, Li WW, Lu J, et al. Low Levels of Metrnl are Linked to the Deterioration of Diabetic Kidney Disease. Diabetes Metab Syndrome Obesity. (2024) 17:959–67. doi: 10.2147/dmso.S452055
30. Cheng Y, Shang J, Liu D, Xiao J, and Zhao Z. Development and validation of a predictive model for the progression of diabetic kidney disease to kidney failure. Renal Failure. (2020) 42(1):550–9. doi: 10.1080/0886022X.2020.1772294
31. Chollangi S, Rout NK, Satpathy SK, Panda B, and Patro S. Exploring the Correlates of Hematological Parameters With Early Diabetic Nephropathy in Type 2 Diabetes Mellitus. Cureus. (2023) 15(5):e39778. doi: 10.7759/cureus.39778
32. Chong H, Li J, Chen C, Wang W, Liao D, and Zhang K. The diagnostic model for early detection of gestational diabetes mellitus and gestational diabetic nephropathy. J Clin Lab Analysis. (2022) 36(9):e24627. doi: 10.1002/jcla.24627
33. Ciray H, Aksoy AH, Ulu N, Cizmecioglu A, Gaipov A, and Solak Y. Nephropathy, but not Angiographically Proven Retinopathy, is Associated with Neutrophil to Lymphocyte Ratio in Patients with Type 2 Diabetes. Exp Clin Endocrinol Diabetes. (2015) 123(5):267–71. doi: 10.1055/s-0035-1547257
34. 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(7):11420–7.
35. Fang Y, Wang B, Pang B, Zhou Z, Xing Y, Pang P, et al. Exploring the relations of NLR, hsCRP and MCP-1 with type 2 diabetic kidney disease: a cross-sectional study. Sci Rep. (2024) 14(1):3211. doi: 10.1038/s41598-024-53567-2
36. Gao J-L, Shen J, Yang L-P, Liu L, Zhao K, Pan X-R, et al. Neutrophil-to-lymphocyte ratio associated with renal function in type 2 diabetic patients. World J Clin Cases. (2024) 12(14):2308–15. doi: 10.12998/wjcc.v12.i14.2308
37. Guo W, Song Y, Sun Y, Du H, Cai Y, You Q, et al. Systemic immune-inflammation index is associated with diabetic kidney disease in Type 2 diabetes mellitus patients: Evidence from NHANES 2011-2018. Front Endocrinol (Lausanne). (2022) 13:1071465. doi: 10.3389/fendo.2022.1071465
38. Gupta N, Karoli R, Singh PS, and Shrivastava A. The relationship between neutrophil/lymphocyte ratio, albuminuria and renal dysfunction in diabetic nephropathy. J Indian Acad Clin Med. (2018) 19(4):265–8.
39. Gurmu MZ, Genet S, Gizaw ST, Feyisa TO, and Gnanasekaran N. Neutrophil-lymphocyte ratio as an inflammatory biomarker of diabetic nephropathy among type 2 diabetes mellitus patients: A comparative cross-sectional study. SAGE Open Med. (2022) 10:20503121221140231. doi: 10.1177/20503121221140231
40. Huang L, Xie Y, Dai S, and Zheng H. Neutrophil-to-lymphocyte ratio in diabetic microangiopathy. Int J Clin Exp Pathol. (2017) 10(2):1223–32.
41. Huang Q, Wu H, Wo M, Ma J, Fei X, and Song Y. Monocyte-lymphocyte ratio is a valuable predictor for diabetic nephropathy in patients with type 2 diabetes. Med (Baltimore). (2020) 99(19):e20190. doi: 10.1097/md.0000000000020190
42. Huang W, Huang J, Liu Q, Lin F, He Z, Zeng Z, et al. Neutrophil-lymphocyte ratio is a reliable predictive marker for early-stage diabetic nephropathy. Clin Endocrinol (Oxf). (2015) 82(2):229–33. doi: 10.1111/cen.12576
43. Ibrahim HMM, Bahgat HM, Sharshar DA, and Ramzy TAA. Monocyte lymphocyte ratio, IL 6, and their association with increased carotid intima-media thickness as simple predictive markers for nephropathy in Egyptian diabetic patients. Egyptian J Internal Med. (2024) 36(1). doi: 10.1186/s43162-024-00284-x
44. Jaaban M, Zetoune AB, Hesenow S, and Hessenow R. Neutrophil-lymphocyte ratio and platelet-lymphocyte ratio as novel risk markers for diabetic nephropathy in patients with type 2 diabetes. Heliyon. (2021) 7(7):e07564. doi: 10.1016/j.heliyon.2021.e07564
45. Jayashree K, Senthilkumar GP, Vadivelan M, and Parameswaran S. Circulating 18-Glycosyl Hydrolase Protein Chitiotriosidase-1 is Associated with Renal Dysfunction and Systemic Inflammation in Diabetic Kidney Disease. Int J Appl Basic Med Res. (2023) 13(3):159–67. doi: 10.4103/ijabmr.ijabmr_42_23
46. Kahraman C, Kahraman NK, Aras B, Coşgun S, and Gülcan E. The relationship between neutrophil-to-lymphocyte ratio and albuminuria in type 2 diabetic patients: a pilot study. Arch Med Sci. (2016) 12(3):571–5. doi: 10.5114/aoms.2016.59931
47. Kamrul-Hasan ABM, Mustari M, Asaduzzaman M, Gaffar MAJ, Chanda PK, Rahman MM, et al. Evaluation of neutrophil-lymphocyte ratio and platelet-lymphocyte ratio as markers of diabetic kidney disease in Bangladeshi patients with type 2 diabetes mellitus. J Diabetol. (2021) 12(1):58–62. doi: 10.4103/jod.jod_4_20
48. Kawamoto R, Ninomiya D, Kikuchi A, Akase T, Kasai Y, Kusunoki T, et al. Association of neutrophil-to-lymphocyte ratio with early renal dysfunction and albuminuria among diabetic patients. Int Urol Nephrol. (2019) 51(3):483–90. doi: 10.1007/s11255-018-02065-2
49. Kaya Y, Karatas A, and Irende I. Relationship between glomerular filtration rate with uric acid and neutrophil to lymphocyte ratioin diabetic patients. Ann Clin Analytical Med. (2019) 10(4):436–40. doi: 10.4328/acam.5953
50. Khandare SA, Chittawar S, Nahar N, Dubey TN, and Qureshi Z. Study of Neutrophil-lymphocyte Ratio as Novel Marker for Diabetic Nephropathy in Type 2 Diabetes. Indian J Endocrinol Metab. (2017) 21(3):387–92. doi: 10.4103/ijem.IJEM_476_16
51. Kocak MZ, Aktas G, Duman TT, Atak BM, Kurtkulagi O, Tekce H, et al. Monocyte lymphocyte ratio As a predictor of Diabetic Kidney Injury in type 2 Diabetes mellitus; The MADKID Study. J Diabetes Metab Disord. (2020) 19(2):997–1002. doi: 10.1007/s40200-020-00595-0
52. Li J, Wang X, Jia W, Wang K, Wang W, Diao W, et al. Association of the systemic immuno-inflammation index, neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio with diabetic microvascular complications. Front Endocrinol (Lausanne). (2024) 15:1367376. doi: 10.3389/fendo.2024.1367376
53. Li JJ, Sa RL, Yan ZL, and Zhang Y. Evaluating new biomarkers for diabetic nephropathy: Role of alpha2- macroglobulin, podocalyxin, alpha-L-fucosidase, retinol-binding protein- 4, and cystatin C. World J Diabetes. (2024) 15(6):1212–25. doi: 10.4239/wjd.v15.i6.1212
54. Li L, Shen Q, and Rao S. Association of Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio with Diabetic Kidney Disease in Chinese Patients with Type 2 Diabetes: A Cross-Sectional Study. Ther Clin Risk Manage. (2022) 18:1157–66. doi: 10.2147/tcrm.S393135
55. Li X, Wang L, Liu M, Zhou H, and Xu H. Association between neutrophil-to-lymphocyte ratio and diabetic kidney disease in type 2 diabetes mellitus patients: a cross-sectional study. Front Endocrinol (Lausanne). (2023) 14:1285509. doi: 10.3389/fendo.2023.1285509
56. Long W, Wang X, Lu L, Wei Z, and Yang J. Development of a predictive model for the risk of microalbuminuria: comparison of 2 machine learning algorithms. J Diabetes Metab Disord. (2024) 23:1899–908. doi: 10.1007/s40200-024-01440-4
57. Mattared AM, Esheba NE, Elshora OA, and Mohamed WS. Mean platelet volume and neutrophil to lymphocyte ratio in prediction of early diabetic nephropathy in type 2 diabetics. Diabetes Metab Syndr. (2019) 13(2):1469–73. doi: 10.1016/j.dsx.2019.02.029
58. Onalan E, Gozel N, and Donder E. Can hematological parameters in type 2 diabetes predict microvascular complication development? Pak J Med Sci. (2019) 35(6):1511–5. doi: 10.12669/pjms.35.6.1150
59. Sato H, Takeuchi Y, Matsuda K, Kagaya S, Saito A, Fukami H, et al. Pre-Dialysis Neutrophil-Lymphocyte Ratio, a Novel and Strong Short-Term Predictor of All-Cause Mortality in Patients With Diabetic Nephropathy: Results From a Single-Center Study. Ther Apher Dial. (2017) 21(4):370–7. doi: 10.1111/1744-9987.12533
60. Singh A, Jha AK, Kalita BC, Jha DK, and Alok Y. Neutrophil lymphocyte ratio: a reliable biomarker for diabetic nephropathy? Int J Diabetes Developing Countries. (2022) 42(3):523–8. doi: 10.1007/s13410-021-01000-z
61. Subramani M, Anbarasan M, Deepalatha S, Muthumani LN, and Vasudevan P. Role of neutrophil-lymphocyte ratio as a prognostic marker for type 2 diabetic nephropathy among Indians. Bioinformation. (2023) 19(4):375–9. doi: 10.6026/97320630019375
62. Suvarna R, Biswas M, Shenoy RP, and Prabhu MM. Association of clinical variables as a predictor marker in type 2 diabetes mellitus and diabetic complications. Biomed (India). (2023) 43(1):335–40.
63. Taslamacioglu Duman T, Ozkul FN, and Balci B. Could Systemic Inflammatory Index Predict Diabetic Kidney Injury in Type 2 Diabetes Mellitus? Diagnostics (Basel). (2023) 13(12):2063. doi: 10.3390/diagnostics13122063
64. Tutan D and Doğan M. Evaluation of Neutrophil/Lymphocyte Ratio, Low-Density Lipoprotein/Albumin Ratio, and Red Cell Distribution Width/Albumin Ratio in the Estimation of Proteinuria in Uncontrolled Diabetic Patients. Cureus. (2023) 15(8):e44497. doi: 10.7759/cureus.44497
65. Wang K, Xu W, Zha B, Shi J, Wu G, and Ding H. Fibrinogen to Albumin Ratio as an Independent Risk Factor for Type 2 Diabetic Kidney Disease. Diabetes Metab Syndr Obes. (2021) 14:4557–67. doi: 10.2147/dmso.S337986
66. Wheelock KM, Saulnier PJ, Tanamas SK, Vijayakumar P, Weil EJ, Looker HC, et al. White blood cell fractions correlate with lesions of diabetic kidney disease and predict loss of kidney function in Type 2 diabetes. Nephrol Dial Transplant. (2018) 33(6):1001–9. doi: 10.1093/ndt/gfx231
67. Xi C, Wang C, Rong G, and Deng J. A Nomogram Model that Predicts the Risk of Diabetic Nephropathy in Type 2 Diabetes Mellitus Patients: A Retrospective Study. Int J Endocrinol. (2021) 2021:6672444. doi: 10.1155/2021/6672444
68. Yan P, Yang Y, Zhang X, Zhang Y, Li J, Wu Z, et al. Association of systemic immune-inflammation index with diabetic kidney disease in patients with type 2 diabetes: a cross-sectional study in Chinese population. Front Endocrinol (Lausanne). (2023) 14:1307692. doi: 10.3389/fendo.2023.1307692
69. Yashilha D, Shini Rubina SK, Nanda Kumar R, and Anuba PA. Association Between Monocyte-to-High-Density Lipoprotein (HDL) Cholesterol Ratio and Proteinuria in Patients With Type 2 Diabetes Mellitus: A Prospective Observational Study. Cureus J Med Sci.. (2023) 15(9):e45783. doi: 10.7759/cureus.45783
70. Yay F, Bayram E, Aggul H, Güçlü C, and Ayan D. Can immature granulocytes and neutrophil-lymphocyte ratio be biomarkers to evaluate diabetic nephropathy?: A cross-sectional study. J Diabetes Complications. (2024) 38(9):108807. doi: 10.1016/j.jdiacomp.2024.108807
71. Zahid Kocak M, Ak0tas G, Erkus E, Duman TT, Atak BM, and Savli H. Mean platelet volume to lymphocyte ratio as a novel marker for diabetic nephropathy. J Coll Physicians Surgeons Pakistan. (2018) 28(11):844–7.
72. Zeng G, Lin Y, Xie P, Lin J, He Y, and Wei J. Relationship of the Neutrophil-Lymphocyte Ratio with All-Cause and Cardiovascular Mortality in Patients with Diabetic Kidney Disease: A Prospective Cohort Study of NHANES Study. J Multidiscip Healthc. (2024) 17:2461–73. doi: 10.2147/jmdh.S465317
73. Zhang D, Ye S, and Pan T. The role of serum and urinary biomarkers in the diagnosis of early diabetic nephropathy in patients with type 2 diabetes. PeerJ. (2019) 7:e7079. doi: 10.7717/peerj.7079
74. Zhang J, Zhang R, Wang Y, Wu Y, Li H, Han Q, et al. Effects of neutrophil-lymphocyte ratio on renal function and histologic lesions in patients with diabetic nephropathy. Nephrol (Carlton). (2019) 24(11):1115–21. doi: 10.1111/nep.13517
75. Charlton A, Garzarella J, Jandeleit-Dahm KAM, and Jha JC. Oxidative Stress and Inflammation in Renal and Cardiovascular Complications of Diabetes. Biol (Basel). (2020) 10(1):18. doi: 10.3390/biology10010018
76. Le TN, Bright R, Truong VK, Li J, Juneja R, and Vasilev K. Key biomarkers in type 2 diabetes patients: A systematic review. Diabetes Obes Metab. (2024) 27(1):7–22. doi: 10.1111/dom.15991
77. Du Y, Zhang Q, Zhang X, Song Y, Zheng J, An Y, et al. Correlation between inflammatory biomarkers, cognitive function and glycemic and lipid profiles in patients with type 2 diabetes mellitus: A systematic review and meta-analysis. Clin Biochem. (2023) 121-122:110683. doi: 10.1016/j.clinbiochem.2023.110683
78. Adane T, Melku M, Worku YB, Fasil A, Aynalem M, Kelem A, et al. The Association between Neutrophil-to-Lymphocyte Ratio and Glycemic Control in Type 2 Diabetes Mellitus: A Systematic Review and Meta-Analysis. J Diabetes Res. (2023) 2023:3117396. doi: 10.1155/2023/3117396
79. Huang F, Zeng Y, Lv L, Chen Y, Yan Y, Luo L, et al. Predictive value of urinary cell cycle arrest biomarkers for all cause-acute kidney injury: a meta-analysis. Sci Rep. (2023) 13(1):6037. doi: 10.1038/s41598-023-33233-9
80. Zhao Y, Ghaedi A, Azami P, Nabipoorashrafi SA, Drissi HB, Dezfouli MA, et al. Inflammatory biomarkers in cardiac syndrome X: a systematic review and meta-analysis. BMC Cardiovasc Disord. (2024) 24(1):276. doi: 10.1186/s12872-024-03939-3
81. Qiu Z, Huang C, Xu C, and Xu Y. Predictive role of neutrophil-to-lymphocyte ratio in metabolic syndrome: Meta-analysis of 70,937 individuals. BMC Endocr Disord. (2024) 24(1):155. doi: 10.1186/s12902-024-01689-z
82. Tan S, Zheng Q, Zhang W, Zhou M, Xia C, and Feng W. Prognostic value of inflammatory markers NLR, PLR, and LMR in gastric cancer patients treated with immune checkpoint inhibitors: a meta-analysis and systematic review. Front Immunol. (2024) 15:1408700. doi: 10.3389/fimmu.2024.1408700
83. Buonacera A, Stancanelli B, Colaci M, and Malatino L. Neutrophil to Lymphocyte Ratio: An Emerging Marker of the Relationships between the Immune System and Diseases. Int J Mol Sci. (2022) 23(7):3636. doi: 10.3390/ijms23073636
84. Yoshitomi R, Nakayama M, Sakoh T, Fukui A, Katafuchi E, Seki M, et al. High neutrophil/lymphocyte ratio is associated with poor renal outcomes in Japanese patients with chronic kidney disease. Ren Fail. (2019) 41(1):238–43. doi: 10.1080/0886022x.2019.1595645
85. Yüce A, Yerli M, Erkurt N, and Çakar M. The Preoperative Neutrophil-Lymphocyte Ratio Is an Independent Predictive Factor in Predicting 1-Year Mortality in Amputated Diabetic Foot Patients. J Foot Ankle Surg. (2023) 62(5):816–9. doi: 10.1053/j.jfas.2023.04.007
86. Lau LFS, Ng JKC, Fung WWS, Chan GCK, Cheng PM, Chow KM, et al. Relationship between Serial Serum Neutrophil-Lymphocyte Ratio, Cardiovascular Mortality, and All-Cause Mortality in Chinese Peritoneal Dialysis Patients. Kidney Blood Press Res. (2023) 48(1):414–23. doi: 10.1159/000530554
87. Zheng F, Ma L, Li X, Wang Z, Gao R, Peng C, et al. Neutrophil Extracellular Traps Induce Glomerular Endothelial Cell Dysfunction and Pyroptosis in Diabetic Kidney Disease. Diabetes. (2022) 71(12):2739–50. doi: 10.2337/db22-0153
88. Swaminathan SM, Rao IR, Bhojaraja MV, Attur RP, Nagri SK, Rangaswamy D, et al. Role of novel biomarker monocyte chemo-attractant protein-1 in early diagnosis & predicting progression of diabetic kidney disease: A comprehensive review. J Natl Med Assoc. (2024) 116(1):33–44. doi: 10.1016/j.jnma.2023.12.001
89. Peng QY, An Y, Jiang ZZ, and Xu Y. The Role of Immune Cells in DKD: Mechanisms and Targeted Therapies. J Inflammation Res. (2024) 17:2103–18. doi: 10.2147/jir.S457526
90. Liu Y, Lv Y, Zhang T, Huang T, Lang Y, Sheng Q, et al. T cells and their products in diabetic kidney disease. Front Immunol. (2023) 14:1084448. doi: 10.3389/fimmu.2023.1084448
91. Zheng Z and Zheng F. Immune Cells and Inflammation in Diabetic Nephropathy. J Diabetes Res. (2016) 2016:1841690. doi: 10.1155/2016/1841690
92. Zhang YY, Liu FH, Wang YL, Liu JX, Wu L, Qin Y, et al. Associations between peripheral whole blood cell counts derived indexes and cancer prognosis: An umbrella review of meta-analyses of cohort studies. Crit Rev Oncol Hematol. (2024) 204:104525. doi: 10.1016/j.critrevonc.2024.104525
Keywords: diabetic nephropathy, immune-inflammation index, biomarkers, diagnostic techniques, prognosis, meta-analysis, systematic review
Citation: Wang Y, Liu Y, Gu W, Cai B, Lei M, Luo Y and Zhang N (2025) Association of immune-inflammation indexes with incidence and prognosis of diabetic nephropathy: a systematic review and meta-analysis. Front. Endocrinol. 16:1532682. doi: 10.3389/fendo.2025.1532682
Received: 22 November 2024; Accepted: 18 July 2025;
Published: 18 August 2025.
Edited by:
Brian D. Adams, Brain Institute of America, United StatesReviewed by:
Arianna Toscano, University Hospital of Policlinico G. Martino, ItalyRuiyan Xie, The University of Hong Kong, Hong Kong SAR, China
Copyright © 2025 Wang, Liu, Gu, Cai, Lei, Luo and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Nannan Zhang, bmFubmFuNzY4N0AxNjMuY29t
†These authors have contributed equally to this work and share first authorship