Introduction

Diabetic kidney disease (DKD) is one of the most common microvascular complications of type 2 diabetes mellitus (T2DM) and remains a leading cause of chronic kidney disease and end-stage renal disease worldwide,1 Despite advances in glucose-lowering therapies and renoprotective interventions, the burden of DKD continues to increase globally.2,3 Approximately 30–40% of patients with T2DM eventually develop DKD, which substantially increases the risks of cardiovascular events, renal failure, and mortality.4,5 Therefore, early identification of individuals at high risk for DKD is clinically important for timely intervention and delaying renal function decline.

Accumulating evidence suggests that chronic low-grade inflammation plays a critical role in the pathogenesis and progression of DKD.6,7 Multiple inflammatory mechanisms, including immune cell activation, oxidative stress, endothelial dysfunction, inflammasome activation, and cytokine-mediated renal injury, have been implicated in diabetic renal damage.8–10 Clinically, inflammatory activity is often assessed using circulating biomarkers such as C-reactive protein and interleukin-6. However, these markers are not routinely measured in many clinical settings. In recent years, several hematological inflammatory indices derived from routine blood tests, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), monocyte-to-lymphocyte ratio (MLR), and pan-immune-inflammation value (PIV), have attracted increasing attention as inexpensive and readily accessible inflammatory biomarkers associated with DKD and other metabolic disorders.11

The systemic inflammatory response index (SIRI), calculated as peripheral blood neutrophil count × monocyte count / lymphocyte count, is a composite inflammatory marker integrating information from neutrophils, monocytes, and lymphocytes.12 Compared with single-ratio inflammatory indices, SIRI may better reflect the balance between innate immune activation and adaptive immune regulation.13–15 From a biological perspective, neutrophils may contribute to renal injury through neutrophil extracellular traps and inflammasome activation, whereas monocytes/macrophages participate in chronic inflammation and renal fibrosis progression. Relative lymphopenia may indicate impaired immune regulatory capacity. Therefore, the composite nature of SIRI may provide a more comprehensive reflection of inflammation-related renal injury than individual leukocyte parameters alone.

Previous studies have linked elevated SIRI levels to adverse outcomes in cardiovascular disease, malignancy, metabolic disorders, and chronic kidney disease.16,17 Evidence specifically regarding SIRI and DKD, however, remains limited and somewhat inconsistent. A previous study reported that SIRI and other inflammatory indices were associated with DKD in patients with T2DM.11 In contrast, a National Health and Nutrition Examination Survey (NHANES)-based study suggested that traditional inflammatory markers such as NLR might outperform SIRI in identifying DKD.18 These discrepancies may reflect differences in study populations, disease severity, hospitalization status, and analytical approaches. Moreover, most previous studies primarily focused on linear associations, while the potential nonlinear relationship between SIRI and DKD has not been fully explored. In addition, peripheral blood inflammatory indices may not completely reflect the complex intra-renal immune microenvironment involved in DKD progression, and their clinical interpretation should therefore be made cautiously.

Therefore, the present retrospective cross-sectional study aimed to investigate the association between SIRI and DKD among hospitalized patients with T2DM. We further explored potential nonlinear relationships using restricted cubic spline analysis, assessed effect modification across clinically relevant subgroups, and performed sensitivity analyses to further evaluate the potential influence of estimated glomerular filtration rate (eGFR) adjustment on the observed associations.

MethodsStudy Design and Participants

This retrospective cross-sectional study included hospitalized patients with type 2 diabetes mellitus (T2DM) admitted to Wenzhou Hospital of Traditional Chinese Medicine between January 2016 and December 2022. T2DM was diagnosed according to the American Diabetes Association criteria. Patients aged ≥18 years with available clinical and laboratory data were eligible for inclusion.

Exclusion criteria included: (1) type 1 diabetes mellitus, gestational diabetes, or other specific types of diabetes; (2) acute infectious diseases, active autoimmune diseases, malignancy, hematological disorders, or other acute inflammatory conditions that could substantially confound peripheral blood cell counts; (3) severe hepatic dysfunction; (4) severe hypertension, defined according to the 2020 International Society of Hypertension Global Hypertension Practice Guidelines as systolic blood pressure ≥180 mmHg and/or diastolic blood pressure ≥110 mmHg or hypertensive emergencies;19 and (5) missing key data required for SIRI calculation or DKD diagnosis. A flowchart of the participant selection process is presented in Figure 1.

Figure 1 Flowchart of participant selection.

This study was approved by the Ethics Committee of Wenzhou Hospital of Traditional Chinese Medicine (Approval No. WZY2023-LW-081-01). Due to the retrospective nature of the study, the requirement for informed consent was waived.

Clinical and Laboratory Data Collection

Demographic characteristics, medical history, medication use, anthropometric measurements, and laboratory data were retrospectively extracted from the hospital electronic medical record system. Peripheral venous blood samples were collected after an overnight fast during routine clinical care upon admission. Complete blood counts were measured using the Sysmex XN-20 automated hematology analyzer. The systemic inflammatory response index (SIRI) was calculated as: neutrophil count (×109/L) × monocyte count (×109/L) / lymphocyte count (×109/L). Urinary albumin-to-creatinine ratio (ACR) was measured by immunoturbidimetry on a Roche c701 analyzer (Roche Diagnostics, Mannheim, Germany). Urine samples were either random spot urine or second morning urine, as per routine clinical practice. The coefficient of variation (CV) for the urinary albumin assay was ≤5.0%, as documented in the laboratory quality control records. All laboratory measurements were performed in the hospital’s certified clinical laboratory using standard automated analyzers and internal quality control procedures in accordance with manufacturer instructions.

Definitions

Diabetic kidney disease (DKD) was defined according to the Kidney Disease: Improving Global Outcomes (KDIGO) 2020 Clinical Practice Guideline as urinary albumin-to-creatinine ratio (ACR) ≥30 mg/g and/or estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2.20 Given the retrospective design, DKD classification was based on available single-time clinical measurements obtained during hospitalization, without repeated confirmatory testing.

eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation.

Hyperlipidemia was defined as total cholesterol ≥240 mg/dL, triglycerides ≥200 mg/dL, LDL-C ≥160 mg/dL, HDL-C <40 mg/dL, or previous physician diagnosis.21 Hyperuricemia was diagnosed according to the Chinese multi-disciplinary consensus on hyperuricemia and related diseases.22

Statistical Analysis

Continuous variables were expressed as mean ± standard deviation (SD) or median (interquartile range, IQR) according to data distribution, while categorical variables were presented as numbers (percentages). Group differences were compared using Student’s t-test, Mann–Whitney U-test, chi-square test, or one-way analysis of variance as appropriate. A two-sided P-value < 0.05 was considered statistically significant.

Participants were categorized into quartiles according to SIRI levels. Multivariable logistic regression models were used to evaluate the association between SIRI and DKD. SIRI was analyzed both as a continuous variable (per IQR increase) and as categorical quartiles. Three hierarchical models were constructed: Model 1: unadjusted; Model 2: adjusted for age, sex, and HbA1C; Model 3: further adjusted for hypertension, hyperlipidemia, Congestive heart failure (CHF), Cardiovascular disease (CVD), Diabetic retinopathy (DR), hyperuricemia, and hepatic insufficiency.

Given that eGFR constitutes part of the diagnostic criteria for DKD and may lie on the causal pathway between systemic inflammation and renal dysfunction, **it was not included in the primary models (Model 1–3) to avoid potential over-adjustment**. Sensitivity analyses with additional adjustment for eGFR were performed to assess the robustness of the findings.

RCS analysis with 3 knots at the 5th, 50th, and 95th percentiles of the SIRI distribution was used to explore potential nonlinear associations between SIRI and DKD. Subgroup analyses were conducted stratified by age (<60 vs ≥60 years), sex, hypertension, and hyperlipidemia, with interaction terms tested in the regression models. ROC curve analysis was performed to evaluate the discriminatory ability of SIRI for DKD and to identify the optimal cutoff value.

A causal mediation analysis was conducted to assess the mediating effect of eGFR on the association between SIRI and DKD using the `mediation` package in R. The mediator model (linear regression with eGFR as outcome) and the outcome model (logistic regression with DKD as outcome) were adjusted for the same covariates as Model 3. Nonparametric bootstrap with 2000 replications was used to estimate the average causal mediation effect (ACME), average direct effect (ADE), total effect, and proportion mediated.

Missing data were handled by complete-case analysis. Multicollinearity was assessed using variance inflation factors (VIF). All statistical analyses were performed using R software (version 4.4.0). The `rms` package was used for RCS analysis, the `pROC` package for ROC analysis, and the `mediation` package for mediation analysis. This study was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline.

ResultsBaseline Characteristics

A total of 2,421 hospitalized patients with type 2 diabetes mellitus (T2DM) were included in the analysis, of whom 892 (36.84%) had diabetic kidney disease (DKD). As shown in Table 1, compared with the non-DKD group (n=1,529), patients with DKD (n=892) were significantly older [median 67.00 (58.00–74.00) vs 52.00 (38.00–63.00) years, P<0.001], had a higher proportion of males (71.86% vs 50.95%, P<0.001), higher HbA1c levels, and lower eGFR. They also had a significantly higher prevalence of hypertension, congestive heart failure (CHF), atherosclerotic cardiovascular disease (ASCVD), and diabetic retinopathy (all P<0.001). Interestingly, the DKD group showed lower prevalence of hyperlipidemia and hyperuricemia. Patients with DKD had significantly higher neutrophil and monocyte counts, lower lymphocyte counts, and consequently higher SIRI levels (median 1.31 (0.83–2.12) vs 0.84 (0.57–1.20), P<0.001) (Table 2).

Table 1 Characteristics of Participants with Type 2 Diabetes Mellitus

Table 2 Inflammatory Response Index of Participants with Type 2 Diabetes Mellitus

Association Between SIRI and DKD

As shown in Table 3, when SIRI was analyzed as a continuous variable (per IQR increase of 0.88), it was significantly associated with increased odds of DKD in the unadjusted Model I (OR 1.54, 95% CI 1.43–1.67) and Model II (OR 1.24, 95% CI 1.14–1.34). However, in the fully adjusted Model III, the association was attenuated and no longer statistically significant (OR 1.02, 95% CI 0.94–1.10, P=0.629). When SIRI was categorized into quartiles, a clear dose-response relationship was observed (P for trend <0.001). Compared with the lowest quartile (Q1: SIRI <0.64), the highest quartile (Q4: SIRI >1.53) was associated with significantly higher odds of DKD in the fully adjusted Model III (adjusted OR 3.14, 95% CI 2.31–4.28, P<0.001).

Table 3 Association Between SIRI and DKD in Patients with Type 2 Diabetes Mellitus

Nonlinear Relationship

Restricted cubic spline analysis demonstrated a significant nonlinear relationship between SIRI and the risk of DKD (P for nonlinearity < 0.001, Figure 2). The adjusted OR increased progressively with SIRI up to around 2.0, followed by a plateau and slight decline at higher values, indicating a potential threshold effect.

Figure 2 Restricted cubic spline analysis of the association between SIRI and DKD. Knots were placed at the 5th, 50th, and 95th percentiles of SIRI. The solid line represents the adjusted odds ratio (OR) and the shaded area represents the 95% confidence interval. The model was adjusted for age, sex, HbA1c (%), hypertension, hyperlipidemia, CHF, ASCVD, diabetic retinopathy, hyperuricemia, and hepatic insufficiency. P for nonlinearity < 0.001. The risk of DKD increased progressively with SIRI up to approximately 2.0, followed by a plateau and slight decline.

Subgroup Analyses

Subgroup analyses using a SIRI cutoff of 1.3 showed that the positive association between elevated SIRI and DKD was significantly stronger in patients aged ≥60 years (P for interaction = 0.01) and in those with HbA1C ≥7% (P for interaction = 0.015). The association remained consistent across subgroups of sex, hypertension, and hyperlipidemia (Figure 3).

Figure 3 Subgroup analyses of the association between elevated SIRI (cutoff = 1.3) and DKD. Odds ratios (ORs) with 95% confidence intervals were derived from multivariable logistic regression models. Data are presented as number of DKD cases/total participants (percentage). P values for interaction indicate whether the association differed significantly across subgroups. The association was significantly stronger in patients aged ≥60 years (P = 0.01) and those with HbA1c ≥7% (P = 0.015), but did not differ significantly by sex, hypertension, or hyperlipidemia status.

Mediation Analyses

Mediation analysis revealed that eGFR partially mediated the association between SIRI and DKD (Table 4). The ACME was 0.024 (95% CI: 0.018–0.029), accounting for 46.2% (95% CI: 33.8–63.8%) of the total effect. The ADE was 0.027 (95% CI: 0.015–0.041). These results suggest that nearly half of the effect of SIRI on DKD risk is mediated through reduced eGFR.

Table 4 Mediation Analysis of eGFR in the Association Between SIRI and DKD

Discriminative Ability

Receiver operating characteristic (ROC) curve analyses were performed to evaluate the discriminatory ability of SIRI for DKD (Figure 4). The AUC for SIRI alone was 0.698. After incorporating age and sex into the model, the AUC increased to 0.792, while the model including age, sex, and SIRI achieved an AUC of 0.806. DeLong’s test demonstrated that the addition of SIRI to the age- and sex-adjusted model significantly improved the predictive performance for DKD (P < 0.001). The optimal cutoff value of SIRI for identifying DKD was 0.366, with a sensitivity of 52.0% and a specificity of 78.9%. Overall, these findings suggest that SIRI may provide incremental discriminatory value for DKD beyond conventional demographic factors.

Figure 4 Receiver operating characteristic (ROC) curves for discrimination of DKD. ROC curves are shown for SIRI alone (AUC = 0.698), age and sex combined (AUC = 0.792), and the combination of age, sex, and SIRI (AUC = 0.806). DeLong’s test showed that adding SIRI significantly improved predictive performance (P < 0.001). The optimal cutoff value of SIRI was 0.366 (sensitivity: 52.0%, specificity: 78.9%).

Sensitivity Ability

Sensitivity analyses were performed to evaluate the robustness of the findings (Figure 5). When eGFR was additionally adjusted in the fully adjusted model, the continuous association between SIRI and DKD remained non-significant (OR 1.04, 95% CI 0.95–1.15). The results were consistent after excluding patients with congestive heart failure or atherosclerotic cardiovascular disease (OR 1.24, 95% CI 1.12–1.39). Analyses using SIRI quartiles (Q4 vs Q1) also showed a significant association (OR 3.14, 95% CI 2.31–4.28). Overall, these sensitivity analyses supported the robustness of the primary findings.

Figure 5 Sensitivity analyses of the association between SIRI and DKD. Forest plot showing odds ratios (ORs) and 95% confidence intervals under different modeling strategies. “Continuous SIRI (adjusted eGFR)”: fully adjusted model including eGFR (OR = 1.04, 95% CI: 0.95–1.15). “SIRI tertile trend”: analyses with and without eGFR adjustment. “Continuous SIRI (no CHF/CVD)”: analysis after excluding patients with CHF or CVD (OR = 1.24, 95% CI: 1.12–1.39). SIRI quartile analysis (Q4 vs Q1) also showed a significant association (OR = 3.14, 95% CI: 2.31–4.28). Overall, these sensitivity analyses supported the robustness of the primary findings.

Discussion

In this retrospective cross-sectional study of 2,421 hospitalized patients with type 2 diabetes mellitus (T2DM), elevated systemic inflammatory response index (SIRI) levels were significantly associated with increased odds of diabetic kidney disease (DKD) when analyzed by quartiles, even after multivariable adjustment. However, the association was attenuated and became non-significant when SIRI was modeled as a continuous variable. Restricted cubic spline analysis revealed a significant nonlinear relationship (P for nonlinearity < 0.0001), with the risk of DKD increasing progressively up to SIRI around 2.0, followed by a plateau and slight decline at higher levels. Subgroup analyses demonstrated that the association was stronger in older patients (≥60 years) and those with poorer glycemic control (HbA1C ≥7%). Mediation analysis further showed that eGFR partially mediated this association, accounting for approximately 46.2% of the total effect.

Chronic low-grade inflammation plays a central role in the pathogenesis and progression of diabetic kidney disease. Sustained hyperglycemia promotes oxidative stress, endothelial dysfunction, and activation of multiple inflammatory signaling pathways, ultimately leading to structural and functional renal damage.8 Innate immune activation, particularly involving neutrophils and monocyte-derived macrophages, has been increasingly recognized as a key contributor to renal injury in diabetes.23,24 Neutrophils may exacerbate renal damage through the formation of neutrophil extracellular traps and activation of inflammasome pathways, while macrophages promote chronic inflammation and renal fibrosis through persistent cytokine secretion and tissue remodeling.25 Increasing evidence indicates that aberrant inflammasome activation amplifies pyroptosis, cytokine release, and fibrotic signaling in chronic kidney disease and DKD.26,27 In contrast, lymphocyte depletion may reflect impaired adaptive immune regulation. These mechanisms align with the observed associations in the present study and are supported by recent literature on immune cell crosstalk in DKD. Consistent with recent findings by Bulu et al,28 who reported opposing trends of inflammatory and pro-resolving mediators in DKD, our results further support the important role of systemic inflammation imbalance in the pathogenesis of diabetic kidney disease.

Previous studies have reported associations between inflammatory indices and diabetic kidney disease. Elevated levels of SIRI, neutrophil-to-lymphocyte ratio, and systemic immune-inflammation index have been associated with DKD in patients with type 2 diabetes.29,30 However, some population-based studies have reported inconsistent findings regarding the relative predictive performance of different inflammatory markers.18 These discrepancies may be attributed to differences in study populations, disease severity, ethnicity, and clinical settings. In particular, hospitalized patients tend to exhibit higher inflammatory burden and metabolic instability compared with community-based populations, which may enhance the association between composite inflammatory indices and DKD.

The nonlinear pattern observed in restricted cubic spline analysis is noteworthy. The risk increased with rising SIRI levels but tended to plateau at higher values (>2.0). This may indicate a threshold effect, beyond which additional inflammatory burden contributes less incrementally to renal injury, possibly due to dominance of other pathophysiological mechanisms or compensatory responses. Such non-monotonic relationships have been reported in other studies of inflammatory markers in metabolic diseases. Subgroup analyses revealed significant interactions with age and HbA1c. The stronger association in older patients may relate to “inflammaging,” whereby age-related chronic inflammation amplifies susceptibility to renal damage.31,32 In patients with higher HbA1c, hyperglycemia-driven injury may overshadow inflammatory effects, whereas in those with relatively better glycemic control, inflammation may play a more prominent role.

A novel aspect of this study is the mediation analysis, which demonstrated that eGFR partially mediated the association between SIRI and DKD (proportion mediated 46.2%, 95% CI 33.8–63.8%). This suggests that systemic inflammation contributes to DKD both directly and indirectly through deterioration of renal function. Because eGFR is part of the DKD definition and may lie on the causal pathway, it was deliberately excluded from primary models to avoid over-adjustment. Sensitivity analyses with eGFR adjustment showed expected attenuation, further supporting its mediating role. Receiver operating characteristic analysis indicated that SIRI had moderate discriminatory ability for DKD, which improved when combined with basic demographic variables. As an inexpensive marker derived from routine blood tests, SIRI may have potential utility for risk stratification in hospitalized T2DM patients. This study has several limitations. First, its cross-sectional design prevents causal inference and raises the possibility of reverse causation,33 as impaired renal function itself can induce systemic inflammation and alter blood cell counts. Second, single measurements of SIRI during hospitalization may be influenced by acute stress, subclinical infection, or cortisol elevation. Third, the hospital-based cohort may introduce Berkson’s bias, limiting generalizability to outpatient or community populations. Fourth, information on diabetes duration, smoking, and medication use (eg, SGLT2 inhibitors, statins, ACEi/ARB) was incomplete, leaving potential residual confounding Fifth, DKD was defined based on single measurements without confirmation, which may cause misclassification. Finally, although multiple sensitivity analyses were performed, unmeasured confounders cannot be fully excluded. Despite these limitations, strengths of this study include a relatively large sample, comprehensive covariate adjustment, evaluation of nonlinearity and subgroups, formal mediation analysis, and multiple sensitivity analyses.

Conclusion

In this retrospective cross-sectional study, elevated SIRI levels were independently associated with increased odds of DKD among hospitalized patients with T2DM. The association appeared nonlinear and was partially mediated through renal function impairment. Given its low cost and routine clinical availability, SIRI may serve as a practical inflammatory biomarker for identifying patients at higher risk of DKD. Nevertheless, prospective longitudinal studies with repeated inflammatory measurements and external validation are needed to confirm these findings and further clarify the clinical utility of SIRI in DKD risk assessment.

Data Sharing Statement

The datasets used and/or analysed during the current study are available from Feng Wang on reasonable request.

Ethics Approval and Consent to Participate

This study adhered strictly to the principles delineated in the Declaration of Helsinki and received approval from the Ethics Committee of Wenzhou Hospital of Traditional Chinese Medicine (Approval Number: WZY2023-LW-081-01). Considering the retrospective design of the study, the requirement for informed consent was waived. Furthermore, all data were anonymized to prevent any potential breach of patient privacy.

Acknowledgments

We thank all the participants and colleagues who contributed to this study.

Author Contributions

Xuan Li: Conceptualization, Methodology, Formal analysis, Writing – original draft. Binquan Fang: Data curation, Investigation, Formal analysis. Kainuo Yu: Formal analysis, Validation. Jiamin Xu: Formal analysis, Validation. Rucheng Chen: Methodology, Writing – review & editing. Nianzhao Chen: Investigation, Resources, Writing – review & editing. Feng Wang: Conceptualization, Methodology, Supervision, Project administration, Writing – review & editing.

All authors made substantial contributions to the conception and design of the study, acquisition of data, analysis and interpretation of data. All authors participated in drafting the article or revising it critically for important intellectual content.

All authors gave final approval of the version to be published, have agreed on the journal to which the article has been submitted, and agree to be accountable for all aspects of the work.

Funding

This research was supported by:Science Technology Department of Zhejiang Province Research and Development Project(2022C03156); Zhejiang Traditional Chinese Medicine Major Research Project (2018ZY011); Zhejiang Traditional Chinese Medicine Science Research Fund Project (2020ZB143); National Famous Traditional Chinese Medicine Expert Inheritance Studio Construction Project, No: Renjiaohan [2021] No. 270 of Guozhong Medical Office.

Disclosure

The authors declare that they have no competing interests.

References

1. Bonner R, Albajrami O, Hudspeth J, Upadhyay A. Diabetic kidney disease. Prim Care. 2020;47:645–12. doi:10.1016/j.pop.2020.08.004

2. Sun H, Saeedi P, Karuranga S, et al. IDF Diabetes Atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabet Res Clin Pract. 2022;183:109119. doi:10.1016/j.diabres.2021.109119

3. de Boer IH, Rue TC, Hall YN, Heagerty PJ, Weiss NS, Himmelfarb J. Temporal trends in the prevalence of diabetic kidney disease in the United States. JAMA. 2011;305:2532–2539. doi:10.1001/jama.2011.861

4. Thipsawat S. Early detection of diabetic nephropathy in patient with type 2 diabetes mellitus: a review of the literature. Diab Vasc Dis Res. 2021;18:14791641211058856. doi:10.1177/14791641211058856

5. Alicic RZ, Rooney MT, Tuttle KR. Diabetic kidney disease: challenges, progress, and possibilities. Clin J Am Soc Nephrol. 2017;12:2032–2045. doi:10.2215/CJN.11491116

6. Fujita T, Hemmi S, Kajiwara M, et al. Complement-mediated chronic inflammation is associated with diabetic microvascular complication. Diabetes Metab Res Rev. 2013;29:220–226. doi:10.1002/dmrr.2380

7. Li HD, You YK, Shao BY, et al. Roles and crosstalks of macrophages in diabetic nephropathy. Front Immunol. 2022;13:1015142. doi:10.3389/fimmu.2022.1015142

8. Barrera-Chimal J, Jaisser F. Pathophysiologic mechanisms in diabetic kidney disease: a focus on current and future therapeutic targets. Diabetes Obes Metab. 2020;22(1):16–31. doi:10.1111/dom.13969

9. Jorch SK, Kubes P. An emerging role for neutrophil extracellular traps in noninfectious disease. Nat Med. 2017;23:279–287. doi:10.1038/nm.4294

10. Peng QY, An Y, Jiang ZZ, Xu Y. The role of immune cells in DKD: mechanisms and targeted therapies. J Inflamm Res. 2024;17:2103–2118. doi:10.2147/JIR.S457526

11. Li L, Shen Q, 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 Manag. 2022;18:1157–1166. doi:10.2147/TCRM.S393135

12. Qi Q, Zhuang L, Shen Y, et al. A novel systemic inflammation response index (SIRI) for predicting the survival of patients with pancreatic cancer after chemotherapy. Cancer. 2016;122:2158–2167. doi:10.1002/cncr.30057

13. Schietroma M, Romano L, Schiavi D, et al. Systemic inflammation response index (SIRI) as predictor of anastomotic leakage after total gastrectomy for gastric cancer. Surg Oncol. 2022;43:101791. doi:10.1016/j.suronc.2022.101791

14. Xu Y, He H, Zang Y, et al. Systemic inflammation response index (SIRI) as a novel biomarker in patients with rheumatoid arthritis: a multi-center retrospective study. Clin Rheumatol. 2022;41:1989–2000. doi:10.1007/s10067-022-06122-1

15. Zhao S, Dong S, Qin Y, Wang Y, Zhang B, Liu A. Inflammation index SIRI is associated with increased all-cause and cardiovascular mortality among patients with hypertension. Front Cardiovasc Med. 2022;9:1066219. doi:10.3389/fcvm.2022.1066219

16. Dziedzic EA, Gąsior JS, Tuzimek A, et al. Investigation of the Associations of Novel Inflammatory Biomarkers-Systemic Inflammatory Index (SII) and Systemic Inflammatory Response Index (SIRI)-with the severity of coronary artery disease and acute coronary syndrome occurrence. Int J Mol Sci. 2022;23(17):9553. doi:10.3390/ijms23179553

17. Jomrich G, Paireder M, Kristo I, et al. High systemic immune-inflammation index is an adverse prognostic factor for patients with gastroesophageal adenocarcinoma. Ann Surg. 2021;273:532–541. doi:10.1097/SLA.0000000000003370

18. Luo Y, Bai R. Analysis of correlation between dietary fiber intake and risk of diabetic kidney disease in adults with type 2 diabetes mellitus: results from the United States National Health and Nutrition Examination Surveys 2009-2018. Ren Fail. 2025;47:2479573. doi:10.1080/0886022X.2025.2479573

19. Unger T, Borghi C, Charchar F, et al. 2020 International society of hypertension global hypertension practice guidelines. Hypertension. 2020;75:1334–1357. doi:10.1161/HYPERTENSIONAHA.120.15026

20. Rossing P. KDIGO 2020 Clinical practice guideline for diabetes management in chronic kidney disease. Kidney Int. 2020;98:S1–s115. doi:10.1016/j.kint.2020.06.019

21. Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults E. Executive summary of the third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult treatment panel III). JAMA. 2001;285(19):2486–2497. doi:10.1001/jama.285.19.2486

22. Zhou JG. Chinese multi-disciplinary consensus on the diagnosis and treatment of hyperuricemia and its related diseases. Zhonghua Nei Ke Za Zhi. 2017;56:235–248. doi:10.3760/cma.j.issn.0578-1426.2017.03.021

23. Hu X, Chen S, Ye S, Chen W, Zhou Y. New insights into the role of immunity and inflammation in diabetic kidney disease in the omics era. Front Immunol. 2024;15:1342837. doi:10.3389/fimmu.2024.1342837

24. Satheesan A, Kumar J, Leela KV, Murugesan R, Chaithanya V, Angelin M. Review on the role of nucleotide-binding oligomerization domain-like receptor protein 3 (NLRP3) inflammasome pathway in diabetes: mechanistic insights and therapeutic implications. Inflammopharmacology. 2024;32:2753–2779. doi:10.1007/s10787-024-01556-2

25. Gupta A, Singh K, Fatima S, et al. Neutrophil extracellular traps promote NLRP3 inflammasome activation and glomerular endothelial dysfunction in diabetic kidney disease. Nutrients. 2022;14:2965. doi:10.3390/nu14142965

26. Walle LV, Lamkanfi M. Drugging the NLRP3 inflammasome: from signalling mechanisms to therapeutic targets. Nat Rev Drug Discov. 2024;23:43–66. doi:10.1038/s41573-023-00822-2

27. Islamuddin M, Qin X. Renal macrophages and NLRP3 inflammasomes in kidney diseases and therapeutics. Cell Death Discov. 2024;10:229. doi:10.1038/s41420-024-01996-3

28. Bulu A, Onalan E, Yakar B, et al. Maresin 1 and CHI3L1 levels exhibit opposing trends and correlations with renal dysfunction in diabetic nephropathy. Medicina. 2025;61:1247. doi:10.3390/medicina61071247

29. Liu W, Zheng S, 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;17:517–531. doi:10.2147/DMSO.S447026

30. Zhang F, Han Y, Mao Y, Li W. The systemic immune-inflammation index and systemic inflammation response index are useful for predicting mortality in patients with diabetic nephropathy. Diabetol Metab Syndr. 2024;16:282. doi:10.1186/s13098-024-01536-0

31. Sato Y, Yanagita M. Immune cells and inflammation in AKI to CKD progression. Am J Physiol Renal Physiol. 2018;315:F1501–f1512. doi:10.1152/ajprenal.00195.2018

32. Poon IKH, Ravichandran KS. Targeting Efferocytosis in Inflammaging. Annu Rev Pharmacol Toxicol. 2024;64:339–357. doi:10.1146/annurev-pharmtox-032723-110507

33. Grabulosa CC, Manfredi SR, Canziani ME, et al. Chronic kidney disease induces inflammation by increasing Toll-like receptor-4, cytokine and cathelicidin expression in neutrophils and monocytes. Exp Cell Res. 2018;365:157–162. doi:10.1016/j.yexcr.2018.02.022