Abstract Diabetic nephropathy (DN) is one of the major causes of end-stage renal disease. This study aimed to explore the internal relationship between metabolic processes and autoimmune responses in patients with DN via untargeted metabolomics and Olink proteomics. The serum of 10 patients who were diagnosed with DN and 10 healthy individuals via untargeted metabolomics and Olink proteomics. Animal models were used to validate the characterized genes. Correlation analysis of major differentially abundant metabolites and differentially expressed proteins revealed that SIRT2 might be a key hub linking energy metabolism and innate immune responses. KEGG enrichment analysis showed that HIF-1 signaling pathway and renal cell carcinoma pathway were co-enriched pathways in energy metabolism and inflammatory response. VEGFA plays a vital role in these two signaling pathways. The ability of SIRT2 to regulate VEGFA expression has been demonstrated. In vivo experiments revealed that SIRT2, VEGFA, and HIF-1α were highly expressed in the kidneys of mice with diabetic nephropathy. In conclusion, our study combines metabolomics and proteomics to provide valuable insights into the synergistic roles of metabolic disorders and inflammatory responses in DN. The data suggest that SIRT2 may be a key target affecting these processes. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-024-80492-1. Keywords: Diabetic nephropathy (DN), Serum, Untargeted metabolomics, Olink proteomics, Inflammation Subject terms: Bioinformatics, Immunology, Nephrology Introduction Diabetic nephropathy (DN) is a common complication of diabetes that is characterized by chronic kidney damage^[42]1,[43]2. According to statistics, by 2045, 780 million people will suffer from diabetes globally^[44]3, and more serious is the presence of DN in about 30–40% of diabetic patients^[45]4. DN has become one of the most common causes of end-stage renal disease (ESRD) in the world^[46]5. When diabetic nephropathy progresses to ESRD, patients need lifelong hemodialysis treatment or kidney transplantation, which places heavy burdens on both patients and their families. The pathological process of DN involves glomerular hyperfiltration, glomerular basement membrane thickening, and a progressive decline in the estimated glomerular filtration rate, which eventually leads to ESRD^[47]6. DN is significantly associated with the presence of hypertension and hyperglycemia, and control of blood pressure and blood glucose levels can reduce the incidence of DN^[48]6–[49]8. However, progress toward specific treatments for DN has been slow^[50]9,[51]10. Therefore, it is necessary to elucidate the pathogenesis of DN to identify effective targets for early intervention. In recent years, researchers have recognized that inflammation and metabolic disorders play central roles in the development of DN^[52]11–[53]14. Innate immunity and energy metabolism interact to promote the progression of DN^[54]11. Metabolic disorders lead to inflammation, which exacerbates the progression of DN. Cao et al., through the analysis of public transcriptome data, revealed that metabolic disorders and inflammatory responses play important roles in the development of DN. Further research has revealed that the VEGFB and IL-17 A signaling pathways play key roles in the pathological process of DN. By intervening in these signaling pathways, lipid accumulation and inflammation in the kidney can be significantly reduced, which helps delay the progression of the disease^[55]15. Studying the link between energy metabolism and innate immunity can provide new proposition into the pathogenesis and therapeutic targets of DN^[56]16, and identifying targets involved in both innate immunity and energy metabolism may be critical for the future treatment of DN. In recent years, metabolomics, proteomics, lipidomics, and many other omics techniques have been rapidly developed, and these developments have facilitated the combined use of multiomics techniques. Metabolomics, a high-throughput analytical technique capable of identifying the levels of small-molecule metabolites with molecular weights < 1500 Da, has been widely used in DN research^[57]13,[58]17. Using a combination of metabolomics analysis and machine learning techniques, He et al. found a close correlation between the incidence of DN and a history of cardiovascular disease. They also reported that the ratios of lactic acid, citric acid, and cholesterol esters to total lipids in medium-density lipoproteins were significant factors influencing the pathogenesis of DN^[59]17. Proteomics, a method used to screen for human disease markers, has also been widely used in DN-related research^[60]18,[61]19. Li et al. identified AKR1A1 as a biomarker for DN via combined transcriptome analysis and proteomic techniques^[62]18. Zhang et al., on the other hand, combined proteomics and Mendelian randomization to identify four proteins as potential therapeutic targets for DN^[63]19. Olink proteomics is a new high-throughput proteomics analysis technology that is based on the proximity extension assay (PEA) principle and provides an innovative high-sensitivity and high-specificity protein detection method^[64]20, whose reliability has already been demonstrated in several studies^[65]21,[66]22. However, this approach has not been applied to the serum of DN patients, and by combining proteomics with metabolomics, it is possible to identify potential mechanisms of disease progression at a deeper level^[67]11,[68]23. The aim of this study was to identify differentially abundant metabolites and differentially expressed inflammatory proteins between serum of DN patients and those of healthy individuals via untargeted metabolomics in combination with Olink proteomics and to recognize the potential mechanisms involved in the progression of DN through this joint analysis to provide an opportunity for early control of DN development. Results Clinical characteristics Ten DN patients and 10 healthy individuals were included in the cohort on the basis of the inclusion and exclusion criteria. Table [69]1 shows the baseline characteristics for the two groups. Analysis revealed no significant differences in baseline characteristics, such as sex, age, height or weight, between the two groups. Compared with the healthy individuals, the patients in the DN group had lower glomerular filtration rates(GFRs) and albumin(ALB) levels and higher Blood Urea Nitrogen(BUN) and Serum Creatinine(SCR) levels, which was related to the pathogenesis of DN, wherein renal impairment leads to a reduced glomerular filtration rate in patients. The Erythrocyte Sedimentation Rates(ESRs), White Blood Cells(WBC)counts, Neutrophil(NEUT) counts, and Eosinophil(EO) counts were elevated in the DN group, indicating that chronic inflammation existed in the patients with DN and was involved in the process of DN. Table 1. Baseline characteristics. GFR, glomerular filtration rate; SBP, systolic blood pressure; DBP, diastolic blood pressure; ALB, albumin; GLB, globulin; BUN, blood urea nitrogen; SCR, serum creatinine; ESR, erythrocyte sedimentation rate; TC, total cholesterol; TG, triglyceride; HDL.C, high density lipoprotein; LDL.C, low density lipoprotein; WBC, white blood cell; RBC, red blood cell; hb, hemoglobin; PLT, platelets; NEUT, neutrophils; LYM, lymphocyte; MONO, monocytes; EO, eosinophils; BASO, basophils; NEUTR, neutrophil ratio; LYMR, lymphocyte radio; MONOR, monocytes radio; EOSR, eosinophils radio; BASOR, basophils radio; PLR, platelet lymphocyte ratio; NLR, neutrophil lymphocyte ratio. n Overall Control DN P 20 10 10 GFR (mean (SD)) 68.52 (35.38) 95.44 (20.73) 41.61 (24.57) < 0.001 Age (mean (SD)) 55.65 (15.17) 60.00 (17.40) 51.30 (11.86) 0.208 Gender (%) 14/6 (70.0/30.0) 5/5 (50.0/50.0) 9/1 (90.0/10.0) 0.141 SBP (mean (SD)) 134.65 (15.55) 135.10 (13.78) 134.20 (17.89) 0.901 DBP (mean (SD)) 77.35 (11.31) 73.70 (8.87) 81.00 (12.73) 0.154 Height (mean (SD)) 167.15 (7.65) 165.10 (3.73) 169.20 (10.02) 0.241 Weight (mean (SD)) 68.90 (14.19) 67.60 (10.04) 70.20 (17.91) 0.694 BMI (mean (SD)) 24.59 (4.30) 24.82 (3.83) 24.36 (4.91) 0.819 Smoke (%) 8/12 (40.0/60.0) 5/5 (50.0/50.0) 3/7 (30.0/70.0) 0.648 Drunk (%) 15/5 (75.0/25.0) 8/2 (80.0/20.0) 7/3 (70.0/30.0) 1 ALB (mean (SD)) 36.58 (6.78) 39.67 (4.22) 33.49 (7.62) 0.038 GLB (mean (SD)) 28.91 (3.63) 28.73 (2.52) 29.08 (4.62) 0.836 BUN (median [IQR]) 7.55 [5.27, 10.08] 5.25 [4.75, 5.65] 10.85 [8.67, 19.66] < 0.001 SCR (median [IQR]) 92.00 [61.75, 135.25] 61.50 [57.75, 76.25] 138.50 [102.50, 334.87] 0.001 ESR (median [IQR]) 14.00 [6.75, 28.50] 8.00 [5.25, 14.50] 25.00 [11.50, 39.00] 0.041 TC (median [IQR]) 5.27 [4.42, 5.81] 5.46 [4.75, 5.68] 4.68 [4.30, 6.00] 0.496 TG (median [IQR]) 1.40 [1.04, 2.17] 1.23 [1.06, 1.50] 2.17 [1.15, 2.55] 0.112 HDL.C (mean (SD)) 1.30 (0.49) 1.37 (0.47) 1.24 (0.52) 0.546 LDL.C (mean (SD)) 2.40 (0.64) 2.63 (0.71) 2.17 (0.48) 0.103 WBC (mean (SD)) 6.39 (1.88) 5.46 (1.41) 7.33 (1.88) 0.022 RBC (mean (SD)) 4.62 (0.86) 4.90 (0.56) 4.34 (1.04) 0.151 Hb (median [IQR]) 146.00 [136.00, 155.25] 146.00 [140.50, 155.75] 147.00 [108.00, 153.50] 0.427 PLT (mean (SD)) 225.55 (35.22) 227.90 (30.37) 223.20 (41.04) 0.774 NEUT (mean (SD)) 4.11 (1.45) 3.47 (1.20) 4.75 (1.44) 0.045 LYM (mean (SD)) 1.90 (0.69) 1.83 (0.82) 1.97 (0.57) 0.665 MONO (mean (SD)) 0.47 (0.18) 0.45 (0.16) 0.49 (0.21) 0.656 EO (mean (SD)) 0.14 (0.08) 0.09 (0.04) 0.18 (0.09) 0.015 BASO (median [IQR]) 0.03 [0.03, 0.05] 0.03 [0.03, 0.04] 0.04 [0.01, 0.07] 0.356 NEUTR (mean (SD)) 61.20 (8.20) 59.15 (7.83) 63.26 (8.44) 0.274 LYMR (mean (SD)) 28.91 (7.04) 30.65 (7.35) 27.17 (6.62) 0.28 MONOR (mean (SD)) 7.22 (1.96) 7.90 (1.97) 6.54 (1.78) 0.123 EOSR (mean (SD)) 2.06 (1.01) 1.71 (0.77) 2.41 (1.14) 0.125 BASOR (mean (SD)) 0.62 (0.28) 0.61 (0.20) 0.62 (0.36) 0.939 PLR (median [IQR]) 123.22 [103.71, 149.42] 136.78 [100.78, 185.52] 118.84 [107.39, 123.35] 0.174 NLR (mean (SD)) 2.34 (0.89) 2.11 (0.84) 2.58 (0.92) 0.247 [70]Open in a new tab Untargeted metabolomics analysis of the serum Serum from 10 DN patients and 10 healthy individuals were analyzed via untargeted metabolomics by using an LC‒MS/MS system. Through metabolite identification, a total of 9605 substances were screened, of which lipids and lipid-like molecules and organic heterocyclic compounds accounted for a relatively large number of substances (Fig. [71]1A). Principal component analysis (PCA) is effective in determining trends in the overall distribution of the data as well as the degree of sample variation between groups. The score plots of the PCA reveal that all samples are within the 95% confidence level (Fig. [72]1B), which indicated the stability of the data. The orthogonal partial least squares discriminant analysis(OPLS-DA) results demonstrated intergroup variability as well as intragroup homogeneity (Fig. [73]1C), suggesting the stability of the machine and the accuracy of the LC‒MS/MS analysis. Furthermore, the Q2 and R2Y scores from the OPLS-DA permutation test surpassed the 0.5 threshold, suggesting the model’s robust ability to interpret and predict categorical data. Fig. 1. [74]Fig. 1 [75]Open in a new tab Screening of serum differential metabolites between Control group and DN group (A) Donut Plot of metabolite classification and proportion. (B) Score scatter plot of PCA model for group Control vs. DN. (C) Permutation plot test of OPLS-DA model for group Control vs. DN. PCA, principal component analysis; OPLS-DA, orthogonal partial least squares discriminant analysis. The raw data were screened for differentially abundant metabolites after filtering for deviations and lack of values, lack of value filling, and normalization. A volcano plot of metabolites revealed that a total of 3692 substances differed between the two groups, with 2308 metabolites upregulated and 1384 metabolites downregulated in the patients with DN compared to the healthy individuals(Fig. [76]2A). In addition, hierarchical clustering analysis of the differentially abundant metabolites was performed to compare the levels of the differentially metabolites between the two groups(Supplementary Fig. 1). Besides, we showed the top 10 most significantly up-regulated and down-regulated metabolites among the differential abundant metabolites(Fig. [77]2B). Enrichment analysis of KEGG pathways^[78]24–[79]26 showed that differentially abundant metabolites were enriched in 46 different pathways, mainly in the pathways of biosynthesis of amino acids, 2-oxocarboxylic acid metabolism, bile secretion, and carbon metabolism(Fig. [80]2C-D, Supplementary Table 1). The pathways were further topologically analyzed to screen for key pathways with the highest correlation and highest percentage of enrichment for differentially abundant metabolites. The results showed that the most significant pathways associated with the differentially abundant metabolites were alanine, aspartate and glutamate metabolism; the pentose phosphate pathway; the citrate cycle (TCA cycle); and phenylalanine metabolism, which may play key factors in the progression of DN. A total of 15 metabolites were enriched in these pathways (Table [81]2). Fig. 2. [82]Fig. 2 [83]Open in a new tab Difference analysis and the enrichment of metabolites between the two groups (A) Volcano plot for group Control vs. DN. (B) The top 10 most significantly up-regulated and down-regulated metabolites among the differential metabolites. (C) KEGG Enrichment for group Control vs. DN. (D) KEGG Classification for group Control vs. DN. Table 2. Major metabolic pathways and their hits metabolites. Pathway Hits metabolites Alanine, aspartate and glutamate metabolism N-Acetyl-L-aspartic acid; L-Asparagine; Pyruvic acid; Oxoglutaric acid; L-Glutamic acid; Fumaric acid Pentose phosphate pathway Deoxyribose; 2-Dehydro-3-deoxy-D-gluconate; Gluconic acid; Glyceric acid; Pyruvic acid Citrate cycle (TCA cycle) Oxoglutaric acid; Pyruvic acid; Fumaric acid Phenylalanine metabolism L-Phenylalanine; Phenylpyruvic acid; trans-2-Hydroxycinnamate; 4-Hydroxycinnamic acid; Pyruvic acid; Fumaric acid [84]Open in a new tab Olink proteomic analysis in the patients serum The levels of 92 proteins associated with inflammation in the serum of the DN patients and healthy individuals was measured via Olink proteomics based on PEA technology(Supplementary Table 2). First we show the total protein expression between the two groups(Fig. [85]3A). Hierarchical cluster analysis demonstrated the expression of 92 inflammatory proteins between the two groups(Fig. [86]3B). Statistical analysis revealed a total of 66 upregulated differentially expressed proteins, suggesting that the inflammatory response plays essential role in the development of DN(Fig. [87]3C Supplementary Table 3). Among these proteins, CD5, CD40, IL-8, SIRT2, and TNFRSF9 were the five inflammatory proteins with the most significant differences between the groups(Fig. [88]4A-E). Further correlation analysis with the GFRs of the patients revealed that the serum levels of the above proteins were significantly negatively correlated with the GFR(Fig. [89]4F-J). In addition, altered pathways were revealed by GO and KEGG analyses. The GO enrichment analysis indicated that the differentially expressed proteins were predominantly enriched in functions related to protein binding, immune response, and signal transduction pathways(Fig. [90]5A-B). The KEGG pathway enrichment analysis revealed that the differentially expressed proteins were significantly enriched in cytokine‒cytokine receptor interactions(Fig. [91]5C-D, Supplementary Table 4). The potential interactions of the 66 differentially expressed proteins were subsequently revealed through a protein interaction network, which revealed that the core inflammatory proteins were TNF-α and IL-6 (Fig. [92]3D). Fig. 3. [93]Fig. 3 [94]Open in a new tab Olink proteomics differential protein screening and differential protein interaction network (A) Box plots of total serum protein expression in the Control and DN groups. (B) Heatmap of 92 inflammatory proteins expression in Control and DN groups. (C) Volcano plot of differential proteins in Control and DN groups. (D) Control group and DN group differential protein interactions network. Figure 3B was performed using the OmicStudio tools at [95]https://www.omicstudio.cn/tool. Fig. 4. [96]Fig. 4 [97]Open in a new tab The five most significantly different proteins between Control group and DN group and their correlation with GFR were analyzed (A-E)Box plots of the five most significantly differential proteins. (F-J) Correlation of the five most significantly different proteins with GFR. GFR, glomerular filtration rate. Fig. 5. [98]Fig. 5 [99]Open in a new tab Enrichment analysis of differential proteins (A) Boxplot of GO enrichment analysis of differential proteins. (B) Bubble plot for GO enrichment analysis of differential proteins. (C) Boxplot of KEGG enrichment analysis of differential proteins. (D) Bubble plot for KEGG enrichment analysis of differential proteins. Correlation analysis between the differentially abundant metabolites and differentially expressed proteins We performed Pearson correlation analysis between the 15 differentially abundant metabolites enriched in major pathways and the 66 differentially expressed proteins to determine the potential associations of differentially abundant metabolites with differentially expressed proteins (Fig. [100]6A). Hierarchical clustering analysis revealed that glyceric acid and fumaric acid, which are lipid molecules, were correlated with most of the differentially expressed proteins, indicating that lipid metabolism may be associated with the development of inflammation. Among the five differentially expressed proteins that were most significantly upregulated, SIRT2 had the most significant positive correlation with the 15 metabolites. The metabolites that were most significantly correlated with SIRT2 were glyceric acid, fumaric acid, glutamic acid, L-phenylalanine, and 2-keto-3-deoxy-D-gluconic acid. Fig. 6. [101]Fig. 6 [102]Open in a new tab Combined metabolomics and proteomics analysis (A) Heatmap of correlation analysis of major differential metabolites with differential inflammatory proteins. (*P < 0.05,**P < 0.01,***P < 0.001) (B) Venn plot of proteomics and metabolomics KEGG enrichment pathway. (C) HIF-1 Signaling pathway. (D) Renal cell cancinoma. (A) was performed using the OmicShare tool, a free online platform for data analysis ([103]https://www.omicshare.com/tools, accessed on 8 June 2022). Combined metabolomics and Olink proteomics analysis The pathways identified by KEGG enrichment analysis of the metabolomic and proteomic data were intersected, and two co-enrichment pathways were found, namely, the HIF-1 signaling pathway and the renal cell carcinoma pathway(Fig. [104]6B). There were a total of 6 differentially expressed proteins and 4 differentially abundant metabolites enriched in these two pathways (Fig. [105]6C-D), among which the VEGFA protein was involved in both pathways, and the four differentially metabolites were malate, fumarate, alpha-ketoglutarate, and pyruvate. These metabolites are essential components of the TCA cycle and have a significant impact on mitochondrial oxidative phosphorylation. In addition, the roles of the TCA cycle and the HIF-1/VEGF signaling pathway in the progression of DN have been reported^[106]27–[107]29. Therefore, we hypothesized that a high-glucose environment could induce impaired TCA cycle activity in renal cells, which in turn induced the activation of HIF-1 to promote increased expression of VEGFA, and that the above events might promote the development of neovascularization and the onset of renal fibrosis, exacerbating renal damage and remodeling. SIRT2, VEGFA and HIF-1 were highly expressed in the kidneys of DN mice To further verify the results of the above screening, C57BL/6 mice were used as the control group, and db/db knockout mice were used to establish a DN group. Hematoxylin and eosin(HE) staining showed that the volume of glomeruli in DN group increased significantly, Masson staining further confirmed that the degree of renal interstitial fibrosis in DN group was significantly aggravated, and Periodic Acid-Schiff(PAS) staining showed that the thickness of glomerular basement membrane was significantly increased(Fig. [108]7A). The kidney tissues of the healthy and DN mice were collected for immunohistochemical(IHC) staining and Western blot analysis of the key proteins in the HIF-1 pathway, namely, HIF-1α and VEGFA. In addition, we performed IHC staining and Western blot analysis of SIRT2, a potential target linking metabolism and immunity. The results revealed that the rates of the positive SIRT2, HIF-1α and VEGFA expression in the kidney tissues of the DN mice were significantly greater than those in the kidney tissues of the healthy mice (Fig. [109]7B-C). Fig. 7. [110]Fig. 7 [111]Open in a new tab SIRT2, VEGFA and HIF-1α were highly expressed in the kidneys of DN mice (A) Pathological staining of kidney tissue. HE, hematoxylin and eosin; PAS, Periodic Acid-Schiff; Red arrows indicate enlarged glomeruli, Yellow arrows indicate deposited fibrous tissue; Blue arrows indicate the thickened basement membrane; Scar bar = 50 μm. (B) Immunohistochemical staining of kidneys from normal and diabetic nephropathy mice labeled with SIRT2, HIF-1α and VEGFA(*P < 0.05,**P < 0.01,***P < 0.001); Scar bar = 50 μm.(C) Western blot analysis of kidneys from normal and diabetic nephropathy mice labeled with SIRT2, HIF-1α and VEGFA(*P < 0.05,**P < 0.01,***P < 0.001). Discussion As the number of diabetic patients continues to increase, the incidence of DN is also increasing annually^[112]30, and DN has become the primary cause of ESRD^[113]5, which places a heavy burden on the world^[114]31. Research on specific drugs for reducing DN has been slow, and therefore, researchers have focused on the pathogenesis of DN in an attempt to find reliable targets to delay DN. There is a consensus that chronic inflammation and renal fibrosis are common processes in chronic kidney disease^[115]32. During the development of DN, injured renal tubular cells secrete cytokines to recruit inflammatory cells, such as neutrophils and macrophages, to infiltrate the kidney and exert anti-inflammatory effects. However, overactivation of inflammatory cells further results in the secretion of inflammatory mediators, which aggravates disease progression^[116]33,[117]34. In addition, infiltrating macrophages differentiate into fibroblasts, exacerbating the development of renal fibrosis^[118]35. Metabolic disturbances in DN patients also contribute to disease progression^[119]36. In recent years, the role of metabolic reprogramming in DN has received increasing attention from researchers^[120]27,[121]37. Shao et al. compared metabolite levels between canagliflozin (CANA)-treated and control groups via untargeted and targeted metabolomics in patients with DN and in animal models and reported that metabolic reprogramming and upregulation of the metabolic pathways of glycine, serine, and threonine occurred in the CANA group compared with the control group. These authors suggested that glycine, via the AMP-activated protein kinase (AMPK)/mammalian target of rapamycin (mTOR) pathway, ameliorated apoptosis of human proximal tubule cells and delayed the progression of DN^[122]38. The aim of our study was to use combined metabolomics and proteomics approaches to reveal DN-related molecular pathophysiological alterations, which may contribute to the discovery of the underlying mechanisms of DN and the selection of therapeutic targets. Our untargeted metabolomics results revealed that a total of 2308 metabolites were upregulated and 1384 metabolites were downregulated in the serum of DN patients compared with healthy individuals. KEGG pathway enrichment analysis and topological analysis revealed four major differential pathways, namely, alanine, aspartate and glutamate metabolism; the pentose phosphate pathway; the citrate cycle (TCA cycle); and phenylalanine metabolism. Consistent with our results, Qian et al. conducted a metabolomic analysis on serum from diabetic patients with or without DN and found that metabolites that were differentially abundant were mainly concentrated in the alanine, aspartate, and glutamate metabolic pathway^[123]39. A spatially resolved metabolomic analysis of renal tissues from rats revealed significantly higher levels of renal outer medullary glucose 6-phosphate and glyceraldehyde 3-phosphate in the DN group than in the control group, indicating increased flux of PPP metabolism in the outer medulla in the DN group^[124]40. TCA cycle, a key pathway of energy metabolism that occurs mainly in mitochondria, has been demonstrated in several metabolomic studies^[125]41. Kwon S et al. metabolically analyzed urine samples from patients with DN and reported that the levels of circulating TCA metabolites increased with the progression of the DN stage, demonstrating the involvement of the TCA cycle in the progression of DN^[126]42. Phenylalanine, an essential amino acid, is converted to tyrosine by hepatic phenylalanine hydroxylase, and phenylalanine metabolic pathways are significantly upregulated in the serum and fecal metabolomes of patients with DN and are strongly correlated with DN progression^[127]43. The evidence from these studies demonstrates that all four of the above metabolic pathways are associated with the progression of DN. Correlation analysis and hierarchical clustering of differentially abundant metabolites enriched in the above pathways and differentially expressed proteins revealed that the levels of glyceric acid and fumaric acid were significantly increased and were associated with most of the inflammatory proteins in the serum of DN patients. Glyceric acid is a three-carbon alcohol acid produced by the oxidation of glycerol, and the formation of glycerol 3-phosphate via phosphorylation of glycerol is involved in the process of glycolysis. Abnormal glycolysis may lead to kidney injury^[128]37. Furthermore, other studies have shown that intestinal fructose metabolism leads to increased glycerate production, which impairs the islet size and function, thereby inducing glucose intolerance and further aggravating the development of diabetes^[129]44. Thus, increased synthesis of glycolic acid may exacerbate abnormal glycolysis, further impairing renal function. Fumaric acid is a key substance in the TCA cycle and stimulates endoplasmic reticulum stress, matrix production, and HIF-1a and TGF-β production in the context of DN^[130]45,[131]46. Nevertheless, the inhibition of NOX4 can promote the synthesis of fumarate hydratase and thus reduce fumarate levels, which can attenuate the activation of HIF-1α and TGF-β and the process of renal fibrosis^[132]46. Olink proteomics revealed that a total of 66 inflammatory proteins were upregulated in the patients with DN compared with the controls, with five proteins that showed the most significant differences significantly correlated with the patient’s GFR. CD5, which was previously shown to act as a disease marker for DN^[133]47. CD40 is able to induce inflammation in endothelial cells in patients with diabetic retinopathy (DR) and may play the same role in the progression of DN, but further evidence is needed^[134]48. IL-8, a classical inflammatory factor, has been studied in several complications of diabetes^[135]49–[136]51. TNFRSF9, a member of the TNF receptor superfamily, is associated with T-cell activation, but the mechanisms of its involvement in DN have not been established. SIRT2, a member of the SIRT family, has been extensively studied, pointing out that SIRT2 plays a key role in the maintenance of blood glucose homeostasis, which it achieves through mechanisms such as enhancing the uptake of glucose by the liver, improving insulin sensitivity, and facilitating glycolysis, which leads to a protective role in diabetes^[137]52–[138]55. However, a new perspective was provided by Ren et al. who found that compound SL010110 activated p300 acetyltransferase, which in turn promoted the acetylation and degradation of PEPCK1 and inhibited the process of hepatic gluconeogenesis by inhibiting the activity of SIRT2, suggesting that SIRT2 may play a role in promoting hepatic gluconeogenesis and raising blood glucose levels^[139]56. In the field of kidney disease, the role of SIRT2 is equally complex and controversial. Several studies have shown that SIRT2 has a protective role in hypertensive nephropathy^[140]57,[141]58, but it promotes disease progression in acute kidney injury (AKI) and high-fat diet-induced diabetic nephropathy^[142]59,[143]60. In addition, there are different views on the regulatory role of SIRT2 on renal fibrosis. One study showed that SIRT2 inhibited the TGF-β signaling pathway and attenuated renal fibrosis by reducing the acetylation levels of SMAD2 and SMAD3^[144]61. In contrast, two other studies suggested that inhibition of SIRT2 suppressed the activation and proliferation of renal interstitial fibroblasts and attenuated renal interstitial fibrosis in a mouse model of unilateral urethral obstruction^[145]62,[146]63. These conflicting findings suggest that the role of SIRT2 in DN requires further in-depth studies to better understand its complex role in disease progression. According to the heatmap of the correlation analysis between inflammatory proteins and differentially abundant metabolites, SIRT2 was most strongly correlated with all differentially abundant metabolites, which suggests that it may be associated with metabolic alterations. In previous studies, SIRT2 was shown to play an important role in a variety of metabolic pathways, and in tuberculosis, it may drive macrophage polarization by regulating arginine and tryptophan metabolism^[147]64. In addition, a study on lung adenocarcinoma suggested that KLF8 might promote tumor progression by activating SIRT2 transcription, leading to G6PD deacetylation and enhancing G6PD activity, which in turn leads to the upregulation of the PPP pathway^[148]65. Although the regulatory relationship between SIRT2 and the TCA cycle is unclear, the elevated expression of SIRT2 in DN group suggests that SIRT2 may play a role in DN, and it is reasonable to suppose that the deacetylation activity of SIRT2 may affect key enzymes of the TCA cycle and thus regulate cellular energy metabolism. The pathways screened by the combined proteomics and metabolomics analyses were intersected, and two pathways were found to be enriched, namely, the HIF-1 signaling pathway and renal cell carcinoma pathway. Four metabolites, namely, malic acid, fumaric acid, alpha-ketoglutaric acid, and pyruvic acid, which are important in the TCA cycle, were enriched, and their abundances demonstrated that metabolic disturbances in the TCA cycle in DN patients led to metabolic reprogramming, which affects the function of kidney cells. The VEGFA protein was enriched in both pathways, and its contribution to DN disease progression by regulating neovascularization has been widely demonstrated^[149]66,[150]67. Web-based pharmacological studies have revealed that a variety of drugs may alleviate DN by affecting VEGFA expression^[151]68–[152]70. Furthermore, SIRT2, through its deacetylase activity, has been shown to activate the expression of VEGFA, which plays a crucial role in tumor angiogenesis^[153]71. Building on our previous research, we hypothesize that under hyperglycemic conditions, the upregulation of SIRT2 expression in DN patients leads to a disruption of the TCA cycle, thereby activating the HIF-1 signaling pathway and consequently increasing the expression of VEGFA. Upregulated VEGFA in turn leads to the development of neovascularization and renal fibrosis and exacerbates the impairment of renal function. IHC and Western blot revealed that the expression levels of SIRT2 and key proteins of HIF-1α signaling pathway were significantly greater in the kidney tissues of DN mice than in those of healthy mice (P < 0.05). These findings indicate that the SIRT2 and HIF-1α signaling pathway may be closely related to the progression of DN. Conclusion In summary, this study focused on circulating TCA cycle metabolites and on SIRT2 and VEGFA inflammatory proteins through metabolomics and Olink proteomics. We hypothesize that in a high-glucose environment, SIRT2 participates in the alteration of multiple metabolic pathways through its deacetylation activity, leading to metabolic reprogramming of renal cells. Concurrently, metabolic disorders of the TCA cycle may activate the HIF-1 signaling pathway, which in turn upregulates VEGFA to promote neovascularization and the onset of renal fibrosis, exacerbating renal damage and remodeling. Combined with the findings of previous studies, these findings suggest that SIRT2 and VEGFA may become key targets for the future treatment of DN. Limitations Our research has several limitations. Firstly, our sample size is small, which leads to our results not being generalisable to a wide range of people. Second, deeper analysis via techniques such as targeted metabolomics and single-cell sequencing technology should be conducted to further confirm our conjecture. Third, the regulatory relationship between SIRT2 and the TCA cycle and the mechanism by which the disruption of the TCA cycle affects the HIF-1 signaling pathway and thus upregulates VEGFA expression require further experimental exploration. Despite these limitations, these data suggest that SIRT2 is a target that links metabolism and inflammation in diabetic nephropathy. Methods Research program Blood was collected from patients with a definite DN diagnosis obtained via renal puncture biopsy; blood from healthy individuals was collected in an outpatient clinic. Blood was centrifuged, and serum was collected for untargeted metabolomics and Olink proteomics. The screened differentially expressed proteins and differentially abundant metabolites were further analyzed. Research population Five milliliters of blood were collected from patients with DN that was confirmed via renal puncture biopsy(n = 10) and from healthy individuals(n = 10). The Declaration of Helsinki’s guiding principles were followed for conducting this study on human subjects. The study was authorized by Shanxi Medical University’s Second Hospital Ethics Committee.((2024)YX No. 116) Informed consent was obtained from the patients with diabetic nephropathy and healthy individuals. The following were the inclusion criteria for patients with DN: (1) Age > 18 years; (2) DN confirmed by renal puncture biopsy. The following were the exclusion criteria for patients with DN: (1) Comorbid cancer; (2) Other comorbid diseases affecting inflammation, such as infections and autoimmune diseases; (3) Severe cardiopulmonary insufficiency; and (4) The inability to provide informed consent. The following were the inclusion criteria for patients with healthy individuals: (1) Age > 18 years; (2) No systemic diseases. The following were the exclusion criteria for patients with healthy individuals: 1. Diabetes or elevated blood glucose; 2.The presence of diseases that affect inflammation, such as infections or autoimmune diseases; and 3. The inability to provide informed consent. Sample collection and processing Five milliliters of blood were drawn into collection tubes containing EDTA, and serum was collected after centrifugation at 3000 r/min for 10 min and frozen at -80 °C for subsequent testing. Metabolite extraction Each serum sample (100 µL) was combined with 400 µL of an extraction mixture composed of methanol and acetonitrile in a 1:1 ratio (v/v). The mixture used for extraction contained deuterated internal standards. After 30 s of vortexing, the samples were sonicated for 10 min in a 4 °C water bath. The samples were incubated at -40 °C for one hour in order to precipitate proteins. After that, the samples were centrifuged for 15 min at 4 °C for 12,000 rpm (RCF = 13800×g, R = 8.6 cm). For analysis, the supernatant was moved to a fresh glass vial. LC‒MS/MS analysis The analysis of polar metabolites was conducted using a UHPLC system (Vanquish, Thermo Fisher Scientific) equipped with a Waters ACQUITY UPLC BEH amide column (2.1 mm × 50 mm, 1.7 μm) and coupled to an Orbitrap Exploris 120 mass spectrometer (Thermo). The mobile phase comprised of 25 mmol/L ammonium acetate and 25 mmol/L ammonia hydroxide in water (pH = 9.75) and acetonitrile. The autosampler was maintained at a temperature of 4 °C, and the injection volume was set to 2 µL. The Orbitrap Exploris 120 mass spectrometer was employed for its capability to acquire MS/MS spectra in information-dependent acquisition (IDA) mode, controlled by the acquisition software Xcalibur (Thermo). The acquisition software continuously assesses the full-scan MS spectrum in this mode. The following parameters were selected for the ESI source: 320 °C for the capillary temperature; 60,000 for the full MS resolution; 15,000 for the MS/MS resolution; SNCE 20/30/40 for the collision energy; 3.8 kV (positive) or -3.4 kV (negative) for the spray voltage. Metabolite identification The raw data were converted to the mzXML format via ProteoWizard and processed with an in-house program, which was developed by using R and XCMS for peak detection, extraction, alignment, and integration. The R package and BiotreeDB (V3.0) were used for metabolite identification^[154]72. Olink proteomics analysis Following the manufacturer’s instructions, the expression of 92 inflammation-related proteins in the DN and healthy control groups was examined via an Olink^® inflammation panel (Olink Proteomics AB, Uppsala, Sweden). Briefly, oligonucleotide-labeled antibody probes pair and bind to their target proteins. If the probes come into close proximity, the oligonucleotides hybridize pairwise. The addition of a DNA polymerase then triggers a proximity-dependent DNA polymerization event, creating a unique PCR target sequence. This DNA sequence is subsequently detected and quantified using a microfluidic real-time PCR instrument (Signature Q100, LC-Bio Technology Co., Ltd., Hangzhou, China). The resulting Ct data are then quality controlled and normalized via a set of internal and external controls. The final assay readout is presented as normalized protein expression (NPX) values, which are arbitrary units on a log2 scale, where a high value corresponds to increased protein expression^[155]73. Animal models All experiments followed the institutional guidelines and were approved by the Animal Ethics Committee of the Second Hospital of Shanxi Medical University (DW2023046). The environment was regulated to a consistent 12-hour alternating cycle of light and darkness, with the temperature stabilized at 23 °C. The lighting was programmed to cease at 7 pm. Eight-week-old male db/db knockout mice and C57BL/6 mice, both purchased from Nanjing Junke Bio, were used in this study. Pathological staining Samples of renal tissue were sectioned, the wax was removed, and the samples were then fixed by baking them after being hydrated. Subsequently, hematoxylin and eosin(HE), Masson and Periodic Acid-Schiff(PAS) staining were performed. The stained samples were then examined under a microscope and photographed. Immunohistochemical staining The tissue samples were preserved with 4% paraformaldehyde, embedded in paraffin and cut into sections of 5 μm thickness. After deparaffinization, rehydration, and antigen repair, the tissue sections were exposed to primary antibodies (against SIRT2, HIF-1α, and VEGFA) overnight at 4 °C. The primary antibodies used for immunohistochemical staining were anti-mouse SIRT2 (Abcam, ab211033), anti-mouse HIF-1α (Abcam, ab114977) and anti-mouse VEGFA (Abcam, ab51745). Western blotting analysis Firstly, mouse kidney tissue samples are thoroughly lysed and sonicated to extract proteins. Subsequently, the proteins are separated using an SDS-PAGE gel electrophoresis system, followed by incubation with specific antibodies and detection procedures. The primary antibodies used for western blotting were anti-mouse SIRT2 (Abcam, ab211033), anti-mouse HIF-1α (Abcam, ab179483) and anti-mouse VEGFA (Abcam, ab51745). Statistical analysis We conducted statistical analyses using R software, version 4.2, in conjunction with GraphPad Prism 8.0.The significance level was set at P < 0.05. For continuous variables, we employed the t-test to compare the mean ± standard deviation (SD) when the data were normally distributed; otherwise, the Mann-Whitney U test was applied for statistical comparisons across different groups when normality was not observed. Counted data were analyzed via a chi-square test. In addition, we performed Pearson correlation analyses for the major differentially abundant metabolites and differentially expressed proteins. Electronic supplementary material Below is the link to the electronic supplementary material. [156]Supplementary Material 1^ (17.1KB, docx) [157]Supplementary Material 2^ (16.4KB, docx) [158]Supplementary Material 3^ (14.5KB, docx) [159]Supplementary Material 4^ (23.3KB, docx) [160]Supplementary Material 5^ (3.2MB, docx) Author contributions R.J.Z, R.Z.C: Data curation, Formal analysis, Writing – original draft.H.W: Data curation, Formal analysis. J.S.C, K.Y.F, Y.H.Z, S.Y: Data curation. C.L.L, L.Z.L: Formal analysis. H.L.D: Conceptualization, Writing – review & editing, Supervision. Funding The Shanxi Provincial Government’s Regional Cooperation Program (Grant No. 202204041101038), the Leading Talent Team Building Program (Grant No. 202204051002010), the Translational Medicine Engineering Research Center for Vascular Diseases of Shanxi Province, China (Grant No. 2022017), the Construction and Demonstration of Molecular Diagnosis and Treatment Platform for Vascular Diseases in Shanxi Province, China (Grant No. SCP-2023-17), and the Central Government Guidance Fund for Local Projects, China (YDZJSX2021C026) all provided funding for this work. Data availability The corresponding author can provide the data supporting this article upon reasonable requires. Declarations Competing interests The authors declare no competing interests. Ethical Statement This retrospective study involving human participants was performed in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of the Second Hospital of Shanxi Medical University ((2024)YX No. 116). All animal experiments were carried out in accordance with the institutional guidelines and were approved by the animal ethics committee of the Second Hospital of Shanxi Medical University (DW2023046). Conflict of interest The authors declare that none of the work reported in this study could have been influenced by any known competing financial interests or personal relationships. Footnotes Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. These authors contributed equally: Ruijing Zhang and Runze Chang. References