Monocyte human leukocyte antigen-DR-mediated diabetic nephropathy progression is a promising therapeutic target

Objective: Increasing evidence has suggested that immune cells are known to play a pivotal role in the progression of diabetic nephropathy (DN). However, the specific causal role of monocyte surface markers, particularly human leukocyte antigen-DR (HLA-DR), remains unclear. This study aims to investigate the causal relationship between monocyte HLA-DR expression and DN risk and to validate its clinical relevance.

Methods: We first employed a detailed two-sample Mendelian randomization (MR) analysis to investigate the potential causal relationships involving immune cell signatures particularly monocytes and DN risk. Then, to clinically validate these findings, we used flow cytometry to detect the median fluorescence intensity (MFI) of HLA-DR on monocyte in peripheral blood from DN patients and normal controls and analyzed its correlation with key renal function indicators of DN patients.

Results: Two monocyte-related immune cell signatures were identified significantly associated with DN risk via MR analyses after false discovery rate (FDR) correction: HLA-DR on CD14+ CD16− monocyte (OR = 1.189, 95% CI: 1.113–1.270, P_FDR = 1.83 × 10⁻⁴) and HLA-DR on CD14+ monocyte (OR = 1.188, 95% CI: 1.107–1.276, P_FDR = 6.69 × 10⁻⁴), as determined by the inverse variance weighting method. The MFI of HLA-DR on CD14+ CD16− (p <0.001) and CD14+ monocyte (p <0.05) was higher in DN patients compared with normal controls, respectively. Clinical validation confirmed that the elevated HLA-DR MFI on these monocyte subsets was significantly correlated with worsening renal function, showing positive correlations with serum creatinine levels and negative correlations with estimated glomerular filtration rate.

Conclusions: Our findings demonstrate that the elevated MFI of HLA-DR on CD14+ and CD14+ CD16− monocyte is associated with DN and is significantly associated with worsened renal function, highlighting monocyte HLA-DR as a key immune mediator in DN progression and a promising therapeutic target for intervention.

1 Introduction

Diabetic nephropathy (DN) is a major complication of diabetes mellitus and has become the leading cause of end-stage renal disease worldwide (1). Its management poses a substantial economic burden and is frequently complicated by comorbidities such as hypertension, stroke, and cardiovascular disease (2). At present, there are no specific treatments for DN, except symptomatic treatment by actively controlling blood pressure and glucose and lipid levels as well as improving lifestyle (3, 4). Once the disease progresses to the middle or late stages, it cannot be reversed (5). Therefore, understanding the pathogenesis of DN is crucial for developing new interventions.

Traditionally, the pathogenesis of DN primarily includes metabolic disturbance, hemodynamic changes, oxidative stress, inflammation, and immune dysregulation (6, 7). Among diverse immune cells implicated in DN, such as mast cells, neutrophils, T cells, B cells, natural killer (NK) cells, and dendritic cells, monocytes and macrophages have emerged as pivotal mediators in the initiation and progression of renal injury (8–11). Abnormal DNA methylation in peripheral blood mononuclear cells can promote the inflammatory activation of immune cells in DN (12). Among various immune cells, monocytes also play an important role in DN. A study found that elevated growth hormone in patients with type 1 diabetes stimulates an increase in tumor necrosis factor-α signaling derived from podocytes. The elevated tumor necrosis factor-α further induces the differentiation of monocytes into macrophages, leading to an increase in macrophage recruitment, podocyte damage, and proteinuria, thereby promoting inflammation and fibrosis in the kidneys (13). Another study found that in multiple diabetic mouse models accompanied by dyslipidemia and elevated triglyceride-rich lipoproteins, diabetic dyslipidemia can induce lipid accumulation in monocytes and promote the increase of foam monocytes in the circulation, thereby increasing lipid uptake in the circulation and promoting the progression of atherosclerosis and DN (14). Cagney Cristancho et al. found in their research that in patients with type 2 diabetes, circulating monocytes and serum albumin are significantly associated with albuminuria; their results support the potential role of the innate immune system in microvascular terminal organ damage and urine protein loss in diabetes, suggesting that it may be transformed into clinical markers and incorporated into the risk assessment model for diabetes prognosis (15). Monocytes are key mediators of the innate immune response, and their functional state is often defined by the expression of surface markers. Human leukocyte antigen DR (HLA-DR), a major histocompatibility complex class II molecule, is of particular importance. It is a critical marker of monocyte activation and immunocompetence, essential for antigen presentation and the subsequent activation of adaptive immunity (16). However, the specific role and clinical significance of HLA-DR expression on monocyte in DN remain poorly defined. Elucidating this link is essential for a comprehensive understanding of DN immunopathogenesis and could reveal novel therapeutic targets for slowing disease progression.

While observational studies have implicated immune cell features such as monocyte HLA-DR expression in DN (17, 18), these findings are susceptible to bias and cannot establish causality. Mendelian randomization (MR) addresses this limitation by serving as a powerful causal inference method that leverages instrumental variables to minimize confounding (19). In this study, we undertook a comprehensive two-sample MR assessment to explore the causal link involving immune cell signatures particularly monocyte as well as DN. To further validate these genetic findings and assess their clinical relevance, we collected peripheral blood from patients with DN and normal controls to examine the correlation between HLA-DR expression on monocyte and key indicators of renal function.

2 Materials and methods

2.1 Study design

In this study, we examined the causal link between 731 immune cell signatures, categorized into seven groups, and DN using a two-sample MR approach. MR leverages genetic variations as proxies for risk factors. To ensure valid instrumental variable (IV) selection in causal inference, three critical conditions must be met (20): (1) the genetic variation is associated with the exposure; (2) it is not linked with confounders affecting the outcome; (3) it influences the outcome solely through the exposure pathway. Ethical approval was obtained from relevant institutional review boards, and informed consent was secured from all participants.

2.2 Data source description

The study utilized summary statistics for each immunophenotype, accessible from the GWAS catalog (accession numbers GCST90001391 to GCST90002121) (21). The analysis encompassed 731 immunophenotypes, including 118 absolute counts (AC), 389 median fluorescence intensities (MFI) indicating surface antigen levels, 32 morphological parameters (MP), and 192 relative counts (RC); for specific information, refer to Supplementary Table 1. The MFI, AC, and RC data encompassed various cell types like B cells, conventional dendritic cells, mature T cells, monocytes, myeloid cells, TBNK, and Treg panels, whereas MP data included conventional dendritic cell and TBNK panels. The original genome-wide association study (GWAS) for immune traits involved 3,757 European individuals from the central-eastern coast of Sardinia, Italy, with no overlapping cohorts. Around 22 million genotyped single-nucleotide polymorphisms (SNPs), imputed using the Sardinian sequence-based reference panel, were assessed with adjustments for covariates like sex, age, and age squared (22). The datasets presented in this study come from IEU Open GWAS (https://gwas.mrcieu.ac.uk/datasets/), and the GWAS data related to DN were obtained from the FinnGen study (accession numbers finn-bDM_NEPHROPATHY_EXMORE). FinnGen is a large-scale genomic initiative analyzing over 500,000 samples from Finnish biobanks to link genetic variations with health data, aiming to elucidate disease mechanisms and susceptibility. This project represents a collaboration between research institutions and biobanks across Finland and international industry partners (23). The dataset included 3,283 cases and 181,704 controls of European ancestry. After quality control and imputation, approximately 16 million variants were assessed. The participants in the selected exposure and outcome datasets were all of European descent, encompassing both male and female subjects, which minimizes the risk of population stratification. Moreover, the exposure and outcome datasets were from two different geographical locations, Italy and Finland, with no overlapping sample. This separation effectively mitigates the risk of weak instrument bias caused by sample overlap (24).

2.3 Selection of instrumental variables

IV significance levels for each immunophenotype were set at 1 × 10⁻⁵ based on a recent MR study of immune traits (25). The 1000 Genomes Project linkage disequilibrium structure (r²< 0.1 with any other associated SNP within 10 Mb) was tested with the initially selected SNPs, ensuring that selected IVs could independently predict exposure. For each IV, we calculated the percentage of phenotypic variation it explained, along with the F-statistic, to gauge its efficacy and mitigate weak instrumental bias. SNPs with F-statistic< 10 were considered weak instruments and excluded from IVs (26). The F-statistic was calculated using the following formula:

$$F=\frac{R^2}{1-R^2}\times \frac{N-K-1}{K}$$

where R² represents the proportion of exposure variance explained by the IVs, N denotes the sample size of the exposure GWAS, and K indicates the number of SNPs.

2.4 Sample collection and flow cytometry

Peripheral venous blood samples were collected from 24 patients diagnosed with DN by renal biopsy and 24 healthy volunteers. The inclusion criteria for DN patients were the following: (1) age ≥18 years at diagnosis of DN; (2) the patients had relatively complete clinical data; (3) the patients were diagnosed as DN by renal biopsy. The exclusion criteria were as follows: (1) patients with infection, viral hepatitis type B, viral hepatitis type C, tuberculosis, tumors, hematological diseases, autoimmune diseases (such as antinuclear antibody with obvious abnormalities), and other systemic diseases; (2) the pathological results of renal biopsy were associated with membranous nephropathy, IgA nephropathy, ischemic renal injury, mesangial proliferative glomerulonephritis, obesity-related nephropathy, podocytosis, tubular interstitial nephropathy, hepatitis B virus antigen positive deposition, and other renal pathologic types. The inclusion criteria for the healthy control group were as follows: physically healthy adults (age ≥18 years), with age and gender matched to the DN group. Peripheral blood monocyte cells were isolated from control and DN patients according to Ficoll. Then, 5×10⁵ PBMC were resuspended with 100 μL phosphate buffered saline in the flow sample tube. PBMC were stained with fluorochrome-coupled antibodies against Brilliant Violet 421™ anti-human CD14 (BioLegend, # 325627), CD16-FITC (Beckman, # B49215), and PE anti-human HLA-DR (BioLegend, # 307605) and incubated at 4 °C for 30 min in the dark; eBioscience™ Fixable Viability Dye eFluor™ 780 (Invitrogen, # 65-0865-14) antibody staining was used to exclude dead cells. After staining, the cells were washed with 1 mL phosphate buffered saline and centrifuged at 450×g for 7 min; the supernatant was discarded, and then the stained cells were suspended with 100 μL phosphate-buffered saline for flow cytometry. All samples were acquired on a flow cytometer (Beckman) and analyzed with FlowJo software (v10.8.1, BD Biosciences). This study was conducted with the approval of the Ethics Committee of the First Hospital of Jilin University, under approval number 2022-417.

2.5 Statistical analysis

All of the MR analyses were performed using R software (version 4.3.1). To determine the causal relationship between the 731 immunophenotypes and DN, we primarily employed inverse variance weighted (IVW), weighted median (WM), and Mendelian randomization–Egger (MR-Egger) methodologies, utilizing the “Mendelian Randomization” package (V 0.4.3) (27). Heterogeneity among the selected IVs was assessed using Cochran’s Q statistic and corresponding p-values, and a random effects model was applied to depict this heterogeneity (19). The MR-Egger method was instrumental in examining the potential asymmetry due to horizontal pleiotropy of multiple genetic variants (28). Additionally, the MR pleiotropy residual sum and outlier (MR-PRESSO) method was implemented to identify and exclude significant horizontal pleiotropic outliers that could skew the results (29). Scatter and funnel plots were generated to confirm the absence of outliers in the scatter plots and demonstrate the robustness and homogeneity of the correlations in the funnel plots (30). Finally, we conducted a reverse Mendelian randomization analysis by swapping the exposure and outcome datasets, using the same parameters as in the forward analysis. Statistical analysis was performed using GraphPad Prism 8.0 (San Diego, CA). Continuous variables with normal distribution were presented as mean ± standard deviation; non-normal variables reported as median (interquartile range) were used to describe the mean value of the data subject to non-normal distribution. Student’s t-test and Mann–Whitney U test were used to compare between two groups. The chi-square test was used to compare the categorical data between the two groups. Pearson or Spearman correlation was used for correlation analysis. For the two continuous variables that met the normality assumption, we applied Pearson correlation analysis, and for those that did not, we applied Spearman correlation analysis. A value of p<0.05 was considered as statistically significant.

3 Results

3.1 Development of IVs used to genetically predict each immunophenotype

Across different batches of analysis, between 3 and 724 independent, non-palindromic, and genome-wide significant SNP loci were selected as IVs to investigate 731 immune phenotypes. These generated IVs explained between 0.005% and 5.199% of the variance in their respective immune phenotypes. All genetic instruments exhibited F-statistics greater than 10, indicating satisfactory robustness (Supplementary Table 2).

3.2 Exploration of causal effect of immunophenotypes on DN

A two-sample MR analysis was conducted to assess the causal effects of immunophenotypes upon DN, utilizing the IVW method. A total of 34 MR results for immunophenotypes with a P-value of less than 0.05 for the IVW method without correction for the false discovery rate (FDR) method were visualized in the circle graph (Figure 1). Following adjusting for multiple tests using the FDR method, four immunophenotypes were identified at the 0.05 significance level, exhibiting harmful effects on DN. These included CD16− CD56 on HLA DR+ NK (TBNK panel), CD33dim HLA DR+ CD11b+ Activated Cells (myeloid cell panel), HLA DR on CD14+ CD16− monocyte (monocyte panel), and HLA DR on CD14+ monocyte (monocyte panel).

Figure 1

Figure 1. MR results with a p-value of less than 0.05 for the IVW method without FDR correction were visualized in circle plots. MR, mendelian randomization; IVW, inverse variance weighting; FDR, false discovery rate.

The odds ratio (OR) of HLA DR on CD14+ CD16− monocyte on DN risk was ~1.189 (95% confidence interval (CI) = 1.113–1.270, p = 2.51 × 10⁻⁷, P_FDR = 1.83 × 10⁻⁴). The MR-Egger and WM method as complementary tests also obtained a similar result: MR-Egger (OR = 1.136, 95% CI = 1.010–1.277, p = 0.047) and WM (OR = 1.232, 95% CI = 1.120–1.354, p = 1.58 × 10⁻⁵). OR of HLA DR on CD14+ monocyte for DN risk was ~1.188 (95% CI = 1.107–1.276, p = 1.83 × 10⁻⁶, P_FDR = 6.69 × 10⁻⁴) using the IVW method. WM also produced a similar result: (OR = 1.171, 95% CI = 1.066–1.287, p = 0.001); however, the association estimate using MR-Egger was not statistically significant (OR = 1.139, 95% CI = 1.001–1.295, p = 0.063). OR of CD33dim HLA DR+ CD11b+ AC for DN risk was ~1.103 (95% CI = 1.052–1.157, p = 4.8 × 10⁻⁵, P_FDR = 0.012) using the IVW method. The association estimates calculated by WM and MR-Egger were not statistically significant: WM (OR = 1.070, 95% CI = 0.995–1.152, p = 0.069) and MR-Egger (OR = 1.079, 95% CI = 0.999–1.164, p = 0.064). OR of CD16− CD56 on HLA DR+ NK for DN risk was ~1.072 (95% CI = 1.032–1.112, p = 2.75 × 10⁻⁴, P_FDR = 0.049) using the IVW method. The MR-Egger method also yielded a similar result: (OR = 1.055, 95% CI = 1.006–1.107, p = 0.037); however, the association estimates using WM were not statistically significant (OR = 1.038, 95% CI = 0.982–1.096, p = 0.186). Regardless of the statistical significance of the association estimates using WM and MR-Egger as the complementary tests, the direction was consistent with the IVW analysis (Figure 2), further strengthening our results. Concerning causality, the p-value of Cochran’s Q > 0.05 indicated no heterogeneity in the results. Additionally, MR-Egger detected no evidence of pleiotropy because the p-value for the intercept was >0.05. Neither MR-PRESSSO nor leave-one-out plots detected outliers (Supplementary Figure 1). Furthermore, with pFDR< 0.2 as a screening criterion, one immunophenotype exhibiting a correlation with DN was HLA DR on CD33− HLA DR+, belonging to the myeloid cell panel and is at risk for DN. OR on DN risk was ~1.1023 (95% CI = 1.016–1.196, p = 0.019, P_FDR = 0.1223), and the direction of the results based on WM and MR-Egger methods was also consistent with IVW analysis (Figure 2). The forest plot and scatter plot of causal relationships between genetically predicted HLR-DR-associated immunophenotypes and the risk of DN are shown in Figures 3 and 4, and the details of sensitivity analyses are shown in Figure 5. Furthermore, detailed MR results for all 731 immunophenotypes are shown in Supplementary Table 3, whereas the scatter, forest, and funnel plots were presented in Supplementary Figures 2–4.

Figure 2

Figure 2. Positive MR results of 731 immunophenotypes on DN with FDR correction (pFDR< 0.2). MR, mendelian randomization; DN, diabetic nephropathy; FDR, false discovery rate.

Figure 3

Figure 3. Detailed forest plots with the estimated MR effect of each IV in IVW models. (A) HLA DR on CD14+ CD16− monocyte; (B) HLA DR on CD14+monocyte. MR, mendelian randomization; IV, instrumental variable; IVW, inverse variance weighting; HLA, human leukocyte antigen.

Figure 4

Figure 4. Scatter plots of causality. The slope of each line corresponding to the estimated MR effect in different models. (A) HLA DR on CD14+ CD16− monocyte; (B) HLA DR on CD14+monocyte. MR, mendelian randomization; HLA, human leukocyte antigen.

Figure 5

Figure 5. Leave one out of sensitivity tests. Calculate the MR results of the remaining IVs after removing the IVs one by one. (A) HLA DR on CD14+ CD16− monocyte; (B) HLA DR on CD14+monocyte. MR, mendelian randomization; IV, instrumental variable; HLA, human leukocyte antigen.

3.3 Reverse Mendelian randomization analysis

To further investigate the impact of DN on immune cell phenotypes, a reverse MR analysis was conducted by swapping the exposure and outcome. After performing FDR correction for multiple comparisons, it was observed that the onset of DN led to alterations in 152 immune cell phenotypes (Supplementary Table 4). Among these, the five most significant immune cell phenotype changes were as follows: DN was associated with a reduced percentage of CD14− CD16+ monocyte (OR 95% CI: 0.937 [0.912–0.962], p < 0.001). In contrast, DN significantly increased the expression of CD45 on lymphocytes (OR 95% CI: 1.143 [1.085–1.204], p < 0.001), CD8 on CD39+ CD8+ T cells (OR 95% CI: 1.093 [1.050–1.137], p < 0.001), CD8dim T cell absolute count (OR 95% CI: 1.069 [1.038–1.101], p<0.001), and effector memory CD8+ T cell absolute count (OR 95% CI: 1.074 [1.048–1.101], p < 0.001), as detailed in Figure 6.

Figure 6

Figure 6. Reverse Mendelian randomization effects of diabetic nephropathy on immune cell phenotypes (top five most significant).

3.4 Clinical validation of immunophenotypes in DN patients

Through MR analysis, four immunophenotypes, including HLA DR on CD14+ CD16− monocyte and HLA DR on CD14+ monocyte, were found to be causally associated with the development of DN. In order to further understand their expression in the blood of DN patients versus normal controls, we performed flow cytometry analysis. Flow cytometry was used to analyze the peripheral blood of 24 patients with DN and 24 normal controls. All patients with DN and normal controls met the inclusion and exclusion criteria. The clinical information of patients with DN and normal controls are shown in Table 1.

Table 1

Table 1. General clinical and pathological data of normal controls and diabetic nephropathy patients.

Correlation analysis showed that MFI of HLA DR on CD14+ monocyte was positively correlated with serum creatinine (Scr) level (r = 0.583, p = 0.003) and negatively correlated with estimate glomerular filtration rate (eGFR) level (r = -0.482, p = 0.017) (Figures 7A, B). MFI of HLA DR on CD14+ CD16− monocyte was positively correlated with Scr level (r = 0.461, p = 0.023) and negatively correlated with eGFR level (r = −0.429, p = 0.037) (Figures 7C, D). Correlation analysis showed that MFI of HLA DR on CD14+ and MFI of HLA DR on CD14+ CD16− monocyte had no correlation with blood urea nitrogen, urinary protein excretion, fasting blood glucose, and serum albumin in DN patients (Supplementary Figure 5). Correlation analysis showed that in the normal control group, the MFI of HLA DR on CD14+ and the MFI of HLA DR on CD14+ CD16− monocyte were not correlated with blood urea nitrogen, Scr, eGFR, fasting blood glucose, and serum albumin (Supplementary Figure 6).

Figure 7

Figure 7. Correlation analysis between MFI of HLA DR on CD14+ and CD14+ CD16− monocyte with serum creatinine and eGFR in patients with DN. (A) Correlation analysis between MFI of HLA DR on CD14+ monocyte and serum creatinine. (B) Correlation analysis between MFI of HLA DR on CD14+ monocyte and eGFR. (C) Correlation analysis between MFI of HLA DR on CD14+ CD16− monocyte and serum creatinine. (D) Correlation analysis between MFI of HLA DR on CD14+ CD16− monocyte and eGFR. MFI, median fluorescence intensities; HLA, human leukocyte antigen; eGFR, estimate glomerular filtration rate; DN, diabetic nephropathy.

We gated CD14+ monocyte and CD14+ CD16− monocyte according to the gating strategy in Figure 8A. The mean value of MFI of HLA DR on CD14+ monocyte in the normal control group was 27,266 ± 2,499, and that of MFI of HLA DR on CD14+ monocyte in the DN group was 30,482 ± 3159. As shown in Figure 8B, the MFI of HLA DR on CD14+ monocyte in the peripheral blood of DN patients was higher than that of the normal control group, and the difference was statistically significant (p <0.001). The mean value of the MFI of HLA DR on CD14+ CD16− monocyte in the normal control group was 13,541 ± 1,487, and that of MFI of HLA DR on CD14+ CD16− monocyte in the DN group was 14,629 ± 1,285. As shown in Figure 8C, the MFI of HLA DR on CD14+ CD16− monocyte in the peripheral blood of DN patients was higher than that of the normal control group, and the difference was statistically significant (p <0.05). The average CD14+ monocyte count in the normal control group was 4,841 ± 1,694, and that in the DN group was 5,025 ± 1,787. As shown in Figure 8D, there was no statistical difference between the two groups (p = 0.679). The average count of CD14+ CD16− monocyte in the normal control group was 4,686 ± 1656, and that in the DN group was 4,727 ± 1704. As shown in Figure 8E, there was no statistical difference between the two groups (p = 0.934). We further divided 24 DN patients into two groups, grade II and grades III-IV, based on the degree of glomerular pathological lesions in renal puncture tissues, with 12 patients in each group, to further explore the MFI of HLA DR on monocyte in different pathological grades of DN patients. The mean value of the MFI of HLA DR on CD14+ monocyte in the DN glomerular pathological grade II group was 30,183 ± 3,013, and that of the MFI of HLA DR on CD14+ monocyte in the DN glomerular pathological grade III-IV group was 30,782 ± 3,405. As shown in Supplementary Figure 7A, there was no statistical difference between the two groups (p = 0.653). The mean value of the MFI of HLA DR on CD14+ CD16− monocyte in the DN glomerular pathological grade II group was 14,635 ± 1,105, and that of the MFI of HLA DR on CD14+ CD16− monocyte in the DN glomerular pathological grade III-IV group was 14,623 ± 1,494. As shown in Supplementary Figure 7B, there was no statistical difference between the two groups (p = 0.983).

Figure 8

Figure 8. Immunophenotypes in peripheral blood monocyte cells between DN patients and normal controls. (A) Gating strategy of CD14+ monocyte and CD14+ CD16− monocyte. (B) MFI of HLA DR on CD14+ monocyte. (C) MFI of HLA DR on CD14+ CD16− monocyte. (D) CD14+ monocyte absolute count. (E) CD14+ CD16− monocyte absolute count. DN, diabetic nephropathy; MFI, median fluorescence intensities; HLA, human leukocyte antigen; NC, normal controls. The values are expressed as mean ± standard deviation. NC group, n=24; DN group, n=24. *, p <0.05 vs. NC group; **, p <0.001 vs. NC group.

4 Discussion

DN is the major cause of chronic kidney disease that leads to renal fibrosis worldwide (1). The pathogenesis of DN is complex (6, 31–33). Currently, emerging evidence highlights that oxidative stress and inflammation play a central role in DN and underscores the critical contribution of inflammation and immune dysregulation to DN progression (34–37). Although current treatment strategies have reduced the incidence of DN and slowed its progression (38, 39), new therapies are still needed for the prevention and treatment of DN (40, 41).

Many studies have indicated a role for the immune system and cells in DN occurrence and development (42, 43); however, the specific causal relationships involving diverse immunophenotypes and DN development have remained unclear. The role of immune disorders in the occurrence of DN has attracted more and more attention. Recently, some scholars have used MR to conduct the causal relationship between immunophenotype and DN at the gene level (44–47). Different from previous studies, we not only used publicly available genetic datasets to establish the causal relationship between immunophenotypes and DN but also verified the causal immunophenotypes of patients using flow cytometry and conducted correlation analyses on the clinical laboratory indicators of patients. Of the various immune traits analyzed (MFI, RC, AC, and MP), four immunophenotypes (HLA DR on CD14+ CD16− monocyte, HLA DR on CD14+ monocyte, CD16− CD56 on HLA DR+ NK, and CD33dim HLA DR+ CD11b+ Activated Cells) showed significant causal effects on DN (P_FDR < 0.05). Particularly, we conducted clinical validation for two HLA-DR-related monocyte immunophenotypes. The MFI of HLA-DR on CD14+ CD16− and CD14+ monocyte was higher in DN patients compared with normal controls, respectively. However, we did not find the difference of MFI of HLA DR on CD14+ and CD14+ CD16− monocyte among different grades of renal pathology in DN patients. Correlation analysis showed that MFI of HLA DR on CD14+ and HLA DR on CD14+ CD16− monocyte had a positive correlation with Scr and negative correlation with eGFR in DN patients, but had no correlation with blood urea nitrogen, urinary protein excretion, fasting blood glucose, and serum albumin. In addition, to explore the effect of DN on immune cell phenotype, this study additionally performed reverse MR analysis by exchanging exposure and outcome. The onset of DN led to alterations in many immune cell phenotypes. Among them, the five most significant immune cell phenotype changes were the percentage of CD14− CD16+ monocyte, the expression of CD45 on lymphocytes and CD8 on CD39+ CD8+ T cells, CD8dim T cell absolute count, and effector memory CD8+ T-cell absolute count. This suggests that the occurrence of DN also leads to the disorder of the immune system. However, the reverse MR analysis did not reveal any significant effect of DN on HLA DR on CD14+ monocyte and CD14+ CD16− monocyte, further indicating the directionality of the causal relationship between HLA DR expression on monocytes and DN.

CD14 is a marker for mature monocytes and an important receptor for lipopolysaccharide in the immune system. HLA-DR is a universally recognized marker of immune status. HLA DR on CD14+ monocyte is associated with several diseases, including sepsis and coronary atherosclerosis (48, 49). Studies have shown that CD14+ CD16+ monocyte levels are elevated in patients with DN (50), and the ratio of monocyte to high-density lipoprotein cholesterol was elevated in patients with DN (51). However, studies on the correlation between HLA-DR on monocytes and DN have not been established. In the present study, we found that HLA DR on CD14+ monocyte and HLA DR on CD14+ CD16− monocyte are associated with harmful effects on DN. Monocytes may contribute to the establishment and resolution of local inflammatory reactions and participate in the innate immune surveillance of the organism (52). Monocyte heterogeneity has long been recognized; however, in recent years, three functional subsets of human monocytes, classical (CD14++CD16−), intermediate (CD14++CD16+), and nonclassical (CD14+CD16+) monocytes, have been identified (53). Classical monocytes synthesize monocyte chemoattractant protein-1 (MCP-1) and express C–C motif chemokine receptor 2 (54). MCP-1 influences the whole cascade of monocyte-associated inflammation and has a key role in mediating the migration of monocytes and macrophages into kidney tissue (55). MCP-1/C–C motif chemokine receptor 2 is associated with inflammation and morphological changes, such as tubular atrophy and glomerular injury (56). Even in the early DN stages, an increase in MCP-1 urinary excretion can be detected, highlighting its relevance (57). Critically, the role of monocytes extends beyond chemokine production. The functional competence of these cells, as indicated by the expression of HLA-DR, serves as a pivotal upstream regulator in the DN inflammatory cascade. As a key molecule for antigen presentation, the level of HLA-DR expression directly determines the monocyte’s ability to orchestrate adaptive immune responses. In addition, non-classical monocytes are closely associated with DN development, primarily through Toll-like receptors (TLRs) and nuclear factor-kappa B (NF-κB) interaction (58). TLRs are a unique germinal line of pattern recognition receptors that participate in the recognition and activation of innate immunity, leading to a cascade in the presence of inflammation and the release of proinflammatory cytokines (59). Emerging evidence indicates a pivotal role for TLR2 and TLR4 in the perpetuation of inflammation in DN (60, 61). TLR2 may be the predominant long-term mediator of NF-κB activation in transducing inflammation during DN. TLR4 sends signals through its downstream partner myeloid differentiation primary response protein 88, activating NF-κB and resulting in the production of cytokines and reactive oxygen species (62). All of the above have emphasized the significant role of monocytes in the upstream of the DN inflammatory cascade reaction. This supports our findings that immune activation of CD14+ monocyte, particularly CD14+ CD16− monocyte, may exert harmful effects on DN patients.

DN is characterized by albuminuria, loss of renal function, renal fibrosis, and infiltration of macrophages originating from peripheral monocytes inside kidneys. The infiltration of monocytes/macrophages and the macrophage–myofibroblast transformation in the glomerulus during DN are significant in renal dysfunction and glomerulosclerosis (63, 64). In DN, damaged intrinsic renal cells recruit monocytes/macrophages to the tissue damage area to defend against and clear cell damage; however, the polarization of macrophages aggravates the inflammatory response, extracellular matrix secretion, fibrosis, and necroptosis of intrinsic kidney cells (65). Tissue-infiltrating CD14+ monocytes/macrophages are regarded as the producers of the extracellular matrix (66). The fluorescence intensity of CD14+ CD16+ and the expression of TLR4 in patients with uremia complicated with type 2 diabetes mellitus were both higher than those in the normal control group; this immune dysfunction may be related to the activation of the TLR4/NF-κB and signal transducers and activators of transcription 5 signaling pathways (58). The expression level of TLR4 and the proportion of CD14+ CD16+ monocyte in the peripheral blood of patients with early-stage DN are elevated (50). Furthermore, the high expression of HLA-DR on CD14− CD16− immune cells may indicate a lower risk of diabetic microvascular complications, such as diabetic retinopathy and DN (47). Our research found that the MFI of HLA DR on CD14+ monocyte and CD14+ CD16− monocyte were associated with the risk of DN, which was consistent with the previous research results (47, 67).

Our two-sample MR analysis drew upon data from extensive GWAS cohorts, encompassing approximately 185,000 subjects, to ensure robust statistical validity. The conclusions of this study are grounded in genetic instrumental variables, with causal inferences derived from multiple MR methodologies. The findings are resilient, showing no significant distortion from horizontal pleiotropy or other external influences. However, there were still some limitations to our MR study. Despite thorough sensitivity analyses, assessments of horizontal pleiotropy remain somewhat incomplete. Additionally, the absence of individual participant data precluded further stratified analysis. The study’s reliance on a European-centric database also limits the generalizability of the findings to remaining ethnic groups. A broader threshold for evaluating results was employed, which, while potentially increasing the rate of false positives, also facilitated a more comprehensive exploration of the strong associations involving immunophenotypes as well as DN. We collected peripheral blood samples from healthy controls and clinical DN patients and performed flow cytometry analysis on the positive results of HLA DR on monocytes screened by MR. This is where our research has an advantage over previous similar studies. At present, the research results on the expression of immune cell phenotypes in DN are not consistent. This may be related to the different stages of DN, the severity of the disease, whether there are complications, the selection of clinical samples and the size of the sample, etc. Our research still lacks studies on the mechanism and pathophysiology of HLA DR on monocyte in DN. Therefore, when necessary, further in vitro cell experiments, animal experiments, clinical studies with larger sample sizes, and the combined application of databases are also required to conduct more in-depth exploration of the role of HLA DR on monocytes in DN.

5 Conclusions

By integrating systematic MR with clinical correlation analyses, this study demonstrates that elevated HLA DR expression on CD14+ and CD14+ CD16− monocyte is associated with DN and is significantly associated with worsened renal function. These findings establish that activated monocytes may serve as a key driver of DN pathogenesis and HLA DR may serve as a translatable biomarker for prognostic and therapeutic development.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

Ethics statement

The studies involving humans were approved by the Ethics Committee of the First Hospital of Jilin University (Approval number: 2022-417). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

YW: Formal Analysis, Writing – original draft, Methodology, Investigation, Data curation. SF: Formal Analysis, Writing – original draft, Data curation, Conceptualization, Investigation, Methodology. SS: Writing – review & editing, Methodology. ZX: Funding acquisition, Writing – review & editing.

Funding

The author(s) declare financial support was received for the research and/or publication of this article. This study was funded by the National Natural Science Foundation of China (Grant No. 82570845), Health Science and Technology Capacity Improvement Project of Jilin Provincial (Grant No. 2022LC097), and Wu Jieping Medical Foundation (Grant No. 320.6750.2024-03-48).

Acknowledgments

We sincerely thank participants of the studies for their valuable contributions. We sincerely thank the researchers and participants of the GWASs for the collection of the large-scale data resources.

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.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

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 tary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2025.1733139/full#supplementary-material

Supplementary Figure 1 | Leave one out analyses of all MR analyses of 731 immunophenotypes on DN. MR, mendelian randomization; DN, diabetic nephropathy.

Supplementary Figure 2 | Scatter plots of all MR analyses of 731 immunophenotypes on DN. MR, mendelian randomization; DN, diabetic nephropathy.

Supplementary Figure 3 | Forest plots of all MR analyses of 731 immunophenotypes on DN. MR, mendelian randomization; DN, diabetic nephropathy.

Supplementary Figure 4 | Funnel plots of all MR analyses of 731 immunophenotypes on DN. MR, mendelian randomization; DN, diabetic nephropathy.

Supplementary Figure 5 | Correlation analysis between MFI of HLA DR on CD14+ and CD14+ CD16− monocyte with blood urea nitrogen, urinary protein excretion, fasting blood glucose and serum albumin in patients with DN. MFI, median fluorescence intensities; HLA, human leukocyte antigen; DN, diabetic nephropathy.

Supplementary Figure 6 | Correlation analysis between MFI of HLA-DR on CD14+ and CD14+ CD16- monocyte with clinical indicators in normal controls. MFI, median fluorescence intensities; HLA, human leukocyte antigen; eGFR, estimate glomerular filtration rate.

Supplementary Figure 7 | MFI of HLA DR on monocyte of different grades in renal pathology of DN patients. MFI, median fluorescence intensities; HLA, human leukocyte antigen; DN, diabetic nephropathy.

Supplementary Table 1 | The specific immune signatures corresponding to each immunophenotype.

Supplementary Table 2 | The instrumental variables used to genetically predict each immune trait.

Supplementary Table 3 | IVW results of the causal effect of immune cells on Diabetic nephropathy.

Supplementary Table 4 | Results of the causal effect of Diabetic nephropathy on immune cells.

Abbreviations

DN, Diabetic nephropathy; HLA, Human leukocyte antigen; MR, Mendelian randomization; MFI, Median fluorescence intensities; FDR, False discovery rate; NK cells, Natural killer cells; IV, Instrumental variable; AC, Absolute counts; MP, Morphological parameters; RC, Relative counts; GWAS, Genome-wide association study; SNPs, Single-nucleotide polymorphisms; PBMC, Peripheral blood monocyte cells; PBS, Phosphate-buffered saline; IVW, Inverse variance weighting; WM, Weighted median; MR-Egger, Mendelian randomization–Egger; MR-PRESSO, MR pleiotropy residual sum and outlier; OR, Odds ratio; CI, Confidence interval; Scr, Serum creatinine; eGFR, Estimate glomerular filtration rate; MCP-1, Monocyte chemoattractant protein-1; TLR, Toll-like receptors; NF-κB, Nuclear factor-kappa B.

References

1. Gupta S, Dominguez M, and Golestaneh L. Diabetic kidney disease: an update. Med Clin North Am. (2023) 107:689–705. doi: 10.1016/j.mcna.2023.03.004

PubMed Abstract | Crossref Full Text | Google Scholar

2. Folkerts K, Petruski-Ivleva N, Kelly A, Fried L, Blankenburg M, Gay A, et al. Annual health care resource utilization and cost among type 2 diabetes patients with newly recognized chronic kidney disease within a large U.S. administrative claims database. J Manag Care Spec Pharm. (2020) 26:1506–16. doi: 10.18553/jmcp.2020.26.12.1506

PubMed Abstract | Crossref Full Text | Google Scholar

3. Neuen BL, Yeung EK, Rangaswami J, and Vaduganathan M. Combination therapy as a new standard of care in diabetic and non-diabetic chronic kidney disease. Nephrol Dial Transplant. (2025) 40:i59–69. doi: 10.1093/ndt/gfae258

PubMed Abstract | Crossref Full Text | Google Scholar

4. Correa-Rotter R, Maple-Brown LJ, Sahay R, Tuttle KR, and Ulasi II. New and emerging therapies for diabetic kidney disease. Nat Rev Nephrol. (2024) 20:156–60. doi: 10.1038/s41581-023-00782-1

PubMed Abstract | Crossref Full Text | Google Scholar

5. Kumar M, Dev S, Khalid MU, Siddenthi SM, Noman M, John C, et al. The bidirectional link between diabetes and kidney disease: mechanisms and management. Cureus. (2023) 15:e45615. doi: 10.7759/cureus.45615

PubMed Abstract | Crossref Full Text | Google Scholar

6. Liu D, Chen X, He W, Lu M, Li Q, Zhang S, et al. Update on the pathogenesis, diagnosis, and treatment of diabetic tubulopathy. Integr Med Nephrol Androl. (2024) 11:e23–00029. doi: 10.1097/IMNA-D-23-00029

Crossref Full Text | Google Scholar

7. Nellaiappan K, Preeti K, Khatri DK, and Singh SB. Diabetic complications: an update on pathobiology and therapeutic strategies. Curr Diabetes Rev. (2022) 18:e030821192146. doi: 10.2174/1573399817666210309104203

PubMed Abstract | Crossref Full Text | Google Scholar

8. Wang M, Yao F, Chen N, Wu T, Yan J, Du L, et al. The advance of single cell transcriptome to study kidney immune cells in diabetic kidney disease. BMC Nephrol. (2024) 25:412. doi: 10.1186/s12882-024-03853-y

PubMed Abstract | Crossref Full Text | Google Scholar

9. 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

PubMed Abstract | Crossref Full Text | Google Scholar

10. Lin DW, Yang TM, Ho C, Shih YH, Lin CL, and Hsu YC. Targeting macrophages: Therapeutic approaches in diabetic kidney disease. Int J Mol Sci. (2024) 25:4350. doi: 10.3390/ijms25084350

PubMed Abstract | Crossref Full Text | Google Scholar

11. Chung JY-F, Zhang Y-Y, Ji ZZ-Y, Tang T, Chen J-Y, Tang SC-W, et al. Immunodynamics of macrophages in renal fibrosis. Integr Med Nephrol Androl. (2023) 10:e00001. doi: 10.1097/imna-d-23-00001

Crossref Full Text | Google Scholar

12. Chen G, Chen H, Ren S, Xia M, Zhu J, Liu Y, et al. Aberrant DNA methylation of mTOR pathway genes promotes inflammatory activation of immune cells in diabetic kidney disease. Kidney Int. (2019) 96:409–20. doi: 10.1016/j.kint.2019.02.020

PubMed Abstract | Crossref Full Text | Google Scholar

13. Nishad R, Mukhi D, Kethavath S, Raviraj S, Paturi ASV, Motrapu M, et al. Podocyte derived TNF-α mediates monocyte differentiation and contributes to glomerular injury. FASEB J. (2022) 36:e22622. doi: 10.1096/fj.202200923R

PubMed Abstract | Crossref Full Text | Google Scholar

14. Cervantes J, Kramer F, Bornfeldt K, and Kanter JE. 39-OR: circulating foamy CD36hi ly6Clo monocytes in mouse models of diabetic dyslipidemia. Diabetes. (2023) 72:39–OR. doi: 10.2337/db23-39-OR

Crossref Full Text | Google Scholar

15. Cristancho C and Hemond CC. Serum albumin modifies the effect of peripheral blood monocytes on severity of diabetic nephropathy in an adult population. Can J Diabetes. (2022) 46:69–74. doi: 10.1016/j.jcjd.2021.06.001

PubMed Abstract | Crossref Full Text | Google Scholar

16. Wu HP, Chuang LP, Liu PH, Chu CM, Yu CC, Lin SW, et al. Decreased monocyte HLA-DR expression in patients with sepsis and acute kidney injury. Med (Kaunas). (2022) 58:1198. doi: 10.3390/medicina58091198

PubMed Abstract | Crossref Full Text | Google Scholar

17. Ye Y, Dai L, Gu H, Yang L, Xu Z, and Li Z. The causal relationship between immune cells and diabetic retinopathy: a Mendelian randomization study. Front Immunol. (2024) 15:1381002. doi: 10.3389/fimmu.2024.1381002

PubMed Abstract | Crossref Full Text | Google Scholar

18. Wang SY, Yu Y, Ge XL, and Pan S. Causal role of immune cells in diabetic nephropathy: a bidirectional Mendelian randomization study. Front Endocrinol (Lausanne). (2024) 15:1357642. doi: 10.3389/fendo.2024.1357642

PubMed Abstract | Crossref Full Text | Google Scholar

19. Fu S, Zhang L, Ma F, Xue S, Sun T, and Xu Z. Effects of selenium on chronic kidney disease: A mendelian randomization study. Nutrients. (2022) 14:4458. doi: 10.3390/nu14214458

PubMed Abstract | Crossref Full Text | Google Scholar

20. Zeitoun T and El-Sohemy A. Using mendelian randomization to study the role of iron in health and disease. Int J Mol Sci. (2023) 24:13458. doi: 10.3390/ijms241713458

PubMed Abstract | Crossref Full Text | Google Scholar

21. Orrù V, Steri M, Sidore C, Marongiu M, Serra V, Olla S, et al. Complex genetic signatures in immune cells underlie autoimmunity and inform therapy. Nat Genet. (2020) 52:1036–45. doi: 10.1038/s41588-020-0684-4

PubMed Abstract | Crossref Full Text | Google Scholar

22. Sidore C, Busonero F, Maschio A, Porcu E, Naitza S, Zoledziewska M, et al. Genome sequencing elucidates Sardinian genetic architecture and augments association analyses for lipid and blood inflammatory markers. Nat Genet. (2015) 47:1272–81. doi: 10.1038/ng.3368

PubMed Abstract | Crossref Full Text | Google Scholar

23. Kurki MI, Karjalainen J, Palta P, Sipilä TP, Kristiansson K, Donner KM, et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature. (2023) 613:508–18. doi: 10.1038/s41586-022-05473-8

PubMed Abstract | Crossref Full Text | Google Scholar

24. Burgess S, Davies NM, and Thompson SG. Bias due to participant overlap in two-sample Mendelian randomization. Genet Epidemiol. (2016) 40:597–608. doi: 10.1002/gepi.21998

PubMed Abstract | Crossref Full Text | Google Scholar

25. Wang C, Zhu D, Zhang D, Zuo X, Yao L, Liu T, et al. Causal role of immune cells in schizophrenia: Mendelian randomization (MR) study. BMC Psychiatry. (2023) 23:590. doi: 10.1186/s12888-023-05081-4

PubMed Abstract | Crossref Full Text | Google Scholar

26. Burgess S and Thompson SG. Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol. (2011) 40:755–64. doi: 10.1093/ije/dyr036

PubMed Abstract | Crossref Full Text | Google Scholar

27. Yavorska OO and Burgess S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int J Epidemiol. (2017) 46:1734–9. doi: 10.1093/ije/dyx034

PubMed Abstract | Crossref Full Text | Google Scholar

28. Burgess S and Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol. (2017) 32:377–89. doi: 10.1007/s10654-017-0255-x

PubMed Abstract | Crossref Full Text | Google Scholar

29. Xu JJ, Zhang XB, Tong WT, Ying T, and Liu KQ. Phenome-wide Mendelian randomization study evaluating the association of circulating vitamin D with complex diseases. Front Nutr. (2023) 10:1108477. doi: 10.3389/fnut.2023.1108477

PubMed Abstract | Crossref Full Text | Google Scholar

30. Liu Q, Kang J, Yin Y, Zhang J, Lian Q, and Zhou F. Association between multiple sclerosis and cancer risk: An extensive review/meta and Mendelian randomization analyses. Mult Scler Relat Disord. (2023) 76:104798. doi: 10.1016/j.msard.2023.104798

PubMed Abstract | Crossref Full Text | Google Scholar

31. De Gregorio V, Barua M, and Lennon R. Collagen formation, function and role in kidney disease. Nat Rev Nephrol. (2025) 21:200–15. doi: 10.1038/s41581-024-00902-5

PubMed Abstract | Crossref Full Text | Google Scholar

32. Wang Y, Liu T, Wu Y, Wang L, Ding S, Hou B, et al. Lipid homeostasis in diabetic kidney disease. Int J Biol Sci. (2024) 20:3710–24. doi: 10.7150/ijbs.95216

PubMed Abstract | Crossref Full Text | Google Scholar

33. Li S and Susztak K. Mitochondrial dysfunction has a central role in diabetic kidney disease. Nat Rev Nephrol. (2025) 21:77–8. doi: 10.1038/s41581-024-00919-w

PubMed Abstract | Crossref Full Text | Google Scholar

34. Su S, Ma Z, Wu H, Xu Z, and Yi H. Oxidative stress as a culprit in diabetic kidney disease. Life Sci. (2023) 322:121661. doi: 10.1016/j.lfs.2023.121661

PubMed Abstract | Crossref Full Text | Google Scholar

35. Ebaid H, Bashandy SAE, Abdel-Mageed AM, Al-Tamimi J, Hassan I, and Alhazza IM. Folic acid and melatonin mitigate diabetic nephropathy in rats via inhibition of oxidative stress. Nutr Metab (Lond). (2020) 17:6. doi: 10.1186/s12986-019-0419-7

PubMed Abstract | Crossref Full Text | Google Scholar

36. Wang B, Dai Z, Gao Q, Liu Y, Gu G, and Zheng H. Spop ameliorates diabetic nephropathy through restraining NLRP3 inflammasome. Biochem Biophys Res Commun. (2022) 594:131–8. doi: 10.1016/j.bbrc.2021.12.068

PubMed Abstract | Crossref Full Text | Google Scholar

37. Zhou T, Fang YL, Tian TT, and Wang GX. Pathological mechanism of immune disorders in diabetic kidney disease and intervention strategies. World J Diabetes. (2024) 15:1111–21. doi: 10.4239/wjd.v15.i6.1111

PubMed Abstract | Crossref Full Text | Google Scholar

38. Martinez Leon V, Hilburg R, and Susztak K. Mechanisms of diabetic kidney disease and established and emerging treatments. Nat Rev Endocrinol. (2025). doi: 10.1038/s41574-025-01171-3

PubMed Abstract | Crossref Full Text | Google Scholar

39. Barrera-Chimal J, Lima-Posada I, Bakris GL, and Jaisser F. Mineralocorticoid receptor antagonists in diabetic kidney disease - mechanistic and therapeutic effects. Nat Rev Nephrol. (2022) 18:56–70. doi: 10.1038/s41581-021-00490-8

PubMed Abstract | Crossref Full Text | Google Scholar

40. DeFronzo RA, Reeves WB, and Awad AS. Pathophysiology of diabetic kidney disease: impact of SGLT2 inhibitors. Nat Rev Nephrol. (2021) 17:319–34. doi: 10.1038/s41581-021-00393-8

PubMed Abstract | Crossref Full Text | Google Scholar

41. Qiao YY, Ji JL, Hou WL, Qu GT, Li SW, Li XY, et al. tRF3-IleAAT reduced extracellular matrix synthesis in diabetic kidney disease mice by targeting ZNF281 and inhibiting ferroptosis. Acta Pharmacol Sin. (2024) 45:1032–43. doi: 10.1038/s41401-024-01228-5

PubMed Abstract | Crossref Full Text | Google Scholar

42. Fu S, Cheng Y, Wang X, Huang J, Su S, Wu H, et al. Identification of diagnostic gene biomarkers and immune infiltration in patients with diabetic kidney disease using machine learning strategies and bioinformatic analysis. Front Med (Lausanne). (2022) 9:918657. doi: 10.3389/fmed.2022.918657

PubMed Abstract | Crossref Full Text | Google Scholar

43. Hu X, Chen S, Ye S, Chen W, and 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

PubMed Abstract | Crossref Full Text | Google Scholar

44. Zhou Y, Zhang H, Yan H, Huang C, and Liu Y. RETRACTED: Mendelian randomization based on immune cells in diabetic nephropathy. Front Endocrinol (Lausanne). (2024) 15:1460652. doi: 10.3389/fendo.2024.1460652

PubMed Abstract | Crossref Full Text | Google Scholar

45. Xue H, Yuan B, Ma L, Kang M, Chen J, and Fang X. Causal role of 731 immune cells in diabetic nephropathy: a bi-directional two-sample Mendelian randomization study. Int Urol Nephrol. (2024) 57:635–41. doi: 10.1007/s11255-024-04206-2

PubMed Abstract | Crossref Full Text | Google Scholar

46. Shen N, Lu S, Kong Z, Gao Y, Hu J, Si S, et al. The causal role between circulating immune cells and diabetic nephropathy: a bidirectional Mendelian randomization with mediating insights. Diabetol Metab Syndrome. (2024) 16:164. doi: 10.1186/s13098-024-01386-w

PubMed Abstract | Crossref Full Text | Google Scholar

47. Li X, Zhang L, Yan C, Zeng H, Chen G, and Qiu J. Relationship between immune cells and diabetic nephropathy: a Mendelian randomization study. Acta Diabetol. (2024) 61:1251–8. doi: 10.1007/s00592-024-02293-2

PubMed Abstract | Crossref Full Text | Google Scholar

48. Leijte GP, Rimmelé T, Kox M, Bruse N, Monard C, Gossez M, et al. Monocytic HLA-DR expression kinetics in septic shock patients with different pathogens, sites of infection and adverse outcomes. Crit Care. (2020) 24:110. doi: 10.1186/s13054-020-2830-x

PubMed Abstract | Crossref Full Text | Google Scholar

49. Huber-Lang M, Barratt-Due A, Pischke SE, Sandanger Ø, Nilsson PH, Nunn MA, et al. Double blockade of CD14 and complement C5 abolishes the cytokine storm and improves morbidity and survival in polymicrobial sepsis in mice. J Immunol. (2014) 192:5324–31. doi: 10.4049/jimmunol.1400341

PubMed Abstract | Crossref Full Text | Google Scholar

50. Chen Y, Men K, Meng C-m, Ma J, and Guo J-c. Changes in TLR-4 expression level and CD14+CD16+ monocyte ratio in the peripheral blood of patients with early diabetic nephropathies. ScienceAsia. (2020) 46:206–12. doi: 10.2306/scienceasia1513-1874.2020.022

Crossref Full Text | Google Scholar

51. Karatas. A, Turkmen. E, Erdem. E, Dugeroglu. H, and Kaya. Y. Monocyte to high-density lipoprotein cholesterol ratio in patients with diabetes mellitus and diabetic nephropathy. Biomarkers Med. (2018) 12:593–9. doi: 10.2217/bmm-2018-0048

PubMed Abstract | Crossref Full Text | Google Scholar

52. Yona S and Jung S. Monocytes: subsets, origins, fates and functions. Curr Opin Hematol. (2010) 17:53–9. doi: 10.1097/MOH.0b013e3283324f80

PubMed Abstract | Crossref Full Text | Google Scholar

53. Heine GH, Ortiz A, Massy ZA, Lindholm B, Wiecek A, Martínez-Castelao A, et al. Monocyte subpopulations and cardiovascular risk in chronic kidney disease. Nat Rev Nephrol. (2012) 8:362–9. doi: 10.1038/nrneph.2012.41

PubMed Abstract | Crossref Full Text | Google Scholar

54. Galkina E and Ley K. Leukocyte recruitment and vascular injury in diabetic nephropathy. J Am Soc Nephrol. (2006) 17:368–77. doi: 10.1681/asn.2005080859

PubMed Abstract | Crossref Full Text | Google Scholar

55. Panzer U, Steinmetz OM, Stahl RA, and Wolf G. Kidney diseases and chemokines. Curr Drug Targets. (2006) 7:65–80. doi: 10.2174/138945006775270213

PubMed Abstract | Crossref Full Text | Google Scholar

56. Urushihara M, Ohashi N, Miyata K, Satou R, Acres OW, and Kobori H. Addition of angiotensin II type 1 receptor blocker to CCR2 antagonist markedly attenuates crescentic glomerulonephritis. Hypertension. (2011) 57:586–93. doi: 10.1161/hypertensionaha.110.165704

PubMed Abstract | Crossref Full Text | Google Scholar

57. Khan NU, Lin J, Liu X, Li H, Lu W, Zhong Z, et al. Insights into predicting diabetic nephropathy using urinary biomarkers. Biochim Biophys Acta Proteins Proteom. (2020) 1868:140475. doi: 10.1016/j.bbapap.2020.140475

PubMed Abstract | Crossref Full Text | Google Scholar

58. Yang M, Gan H, Shen Q, Tang W, Du X, and Chen D. Proinflammatory CD14+CD16+ monocytes are associated with microinflammation in patients with type 2 diabetes mellitus and diabetic nephropathy uremia. Inflammation. (2012) 35:388–96. doi: 10.1007/s10753-011-9374-9

PubMed Abstract | Crossref Full Text | Google Scholar

59. Wada J and Makino H. Innate immunity in diabetes and diabetic nephropathy. Nat Rev Nephrol. (2016) 12:13–26. doi: 10.1038/nrneph.2015.175

PubMed Abstract | Crossref Full Text | Google Scholar

60. Mudaliar H, Pollock C, and Panchapakesan U. Role of Toll-like receptors in diabetic nephropathy. Clin Sci (Lond). (2014) 126:685–94. doi: 10.1042/cs20130267

PubMed Abstract | Crossref Full Text | Google Scholar

61. Mudaliar H, Pollock C, Komala MG, Chadban S, Wu H, and Panchapakesan U. The role of Toll-like receptor proteins (TLR) 2 and 4 in mediating inflammation in proximal tubules. Am J Physiol Renal Physiol. (2013) 305:F143–54. doi: 10.1152/ajprenal.00398.2012

PubMed Abstract | Crossref Full Text | Google Scholar

62. Mudaliar H, Pollock C, Komala MG, Chadban S, Wu H, and Panchapakesan U. The role of Toll-like receptor proteins (TLR) 2 and 4 in mediating inflammation in proximal tubules. Am J Physiology-Renal Physiol. (2013) 305:F143–F54. doi: 10.1152/ajprenal.00398.2012

PubMed Abstract | Crossref Full Text | Google Scholar

63. Torres Á, Muñoz K, Nahuelpán Y, R. Saez A-P, Mendoza P, Jara C, et al. Intraglomerular monocyte/macrophage infiltration and macrophage–myofibroblast transition during diabetic nephropathy is regulated by the A2B adenosine receptor. Cells. (2020) 9:1051. doi: 10.3390/cells9041051

PubMed Abstract | Crossref Full Text | Google Scholar

64. Cai Y, Feng Z, Jia Q, Guo J, Zhang P, Zhao Q, et al. Cordyceps cicadae ameliorates renal hypertensive injury and fibrosis through the regulation of SIRT1-mediated autophagy. Front Pharmacol. (2022) 12:801094. doi: 10.3389/fphar.2021.801094

PubMed Abstract | Crossref Full Text | Google Scholar

65. Li X, Zhuge Z, Carvalho LRRA, Braga VA, Lucena RB, Li S, et al. Inorganic nitrate and nitrite ameliorate kidney fibrosis by restoring lipid metabolism via dual regulation of AMP-activated protein kinase and the AKT-PGC1α pathway. Redox Biol. (2022) 51:102266. doi: 10.1016/j.redox.2022.102266

PubMed Abstract | Crossref Full Text | Google Scholar

66. Rudnik M, Hukara A, Kocherova I, Jordan S, Schniering J, Milleret V, et al. Elevated fibronectin levels in profibrotic CD14(+) monocytes and CD14(+) macrophages in systemic sclerosis. Front Immunol. (2021) 12:642891. doi: 10.3389/fimmu.2021.642891

PubMed Abstract | Crossref Full Text | Google Scholar

67. Zhou M, Zhang S, Bai X, Cai Y, Zhang Z, Zhang P, et al. Acteoside delays the fibrosis process of diabetic nephropathy by anti-oxidation and regulating the autophagy-lysosome pathway. Eur J Pharmacol. (2024) 978:176715. doi: 10.1016/j.ejphar.2024.176715

PubMed Abstract | Crossref Full Text | Google Scholar