Correlation Between Ets-1 and the Progression of Diabetic Kidney Disease

Correlation Between Ets-1 and the Progression of Diabetic Kidney Disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Correlation Between Ets-1 and the Progression of Diabetic Kidney Disease Xiaochun Zhou, Bingru Wang, Ziyi LI, Tingxin Wan, Yuke Kong, Tianxi liu, and 15 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8378039/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background To investigate the correlation between Ets-1 and the progression of diabetic kidney disease (DKD). Methods A total of 115 patients with biopsy-proven DKD were followed for three years. Based on renal function after follow-up, they were categorized into a progress group (PG, 57.4%, 66/115) and a relatively stable group (RSG, 42.6%, 49/115). Ets-1 expression in renal tissue was analyzed, along with its associations with clinical, biochemical, and pathological parameters, and its predictive value for DKD progression. Results Ets-1 expression differed significantly between PG and RSG (p < 0.01). Its expression strongly correlated with clinical indicators of kidney injury (e.g., proteinuria, serum creatinine, blood pressure) and systemic inflammation (CRP) (all p < 0.01), and inversely with estimated eGFR (p 0.7, p < 0.01). Binary logistic regression confirmed that Ets-1 expression, UAER, 24-hour UTP, and serum creatinine were independent risk factors for DKD progression. ROC analysis demonstrated high predictive value of Ets-1 for DKD progression (AUC = 0.875), comparable to that of UAER (AUC = 0.874). Conclusion Ets-1 is closely associated with DKD progression and may serve as a potential predictor for disease advancement in clinical practice. Diabetic kidney disease DKD Progression risk Ets-1 Prognosi Figures Figure 1 Figure 2 Figure 3 1 Background Diabetic kidney disease (DKD) is a common microvascular complication of diabetes, affecting approximately 25–40% of patients with type 2 diabetes, leading to renal impairment and chronic kidney disease. Currently, DKD has become a leading cause of renal replacement therapy (RRT)[ 1 , 2 ]. However, the course of DKD exhibits significant heterogeneity, as not all patients progress to end-stage renal disease (ESRD)[ 3 ]. Commonly used clinical indicators, such as estimated Glomerular Filtration Rate (eGFR) and serum creatinine, reflect current renal function but are insufficient for predicting the progression of renal decline and disease prognosis. The urinary albumin excretion rate (UAER) is considered a biomarker for predicting DKD progression, yet its predictive value remains limited and is susceptible to various confounding factors[ 4 – 6 ]. While renal pathology can provide more precise information on kidney injury—including glomerulosclerosis, vascular lesions, and tubulointerstitial fibrosis—it lacks specific markers for predicting disease progression. Therefore, identifying novel biomarkers capable of earlier and more accurate prediction of DKD progression is an urgent need for achieving personalized treatment and improving patient outcomes. The primary pathological features of DKD include extracellular matrix (ECM) protein deposition, mesangial expansion, basement membrane thickening, tubular atrophy, interstitial fibrosis, and vascular sclerosis. The core of this process is an imbalance in ECM metabolism. The transcription factor Ets-1 (E26 avian erythroblastosis virus transcription factor-1) has been identified as a key regulator of ECM metabolism. It profoundly influences the fibrotic process by modulating the expression of a series of target genes (e.g., MMP-1, MMP-3, MMP-9, TIMP-1)[ 7 , 8 ]. Preliminary studies suggest that Ets-1 plays a significant role in various models of renal fibrosis. In mesangioproliferative glomerulonephritis models, Ets-1 expression in mesangial cells increases over time and is closely associated with mesangial cell activation. In a rat crescentic glomerulonephritis model, Ets-1 was overexpressed and may be involved in the development and progression of the disease [ 9 , 10 ]. In Dahl salt-sensitive (SS) rats, activation of the renin-angiotensin system increased Ets-1 expression, and blocking Ets-1 attenuated the severity of renal injury, suggesting Ets-1 as a potential therapeutic target for hypertension and kidney injury [ 11 ].Our preliminary analysis also indicated significantly upregulated Ets-1 expression in the renal tissue of DKD patients. However, the expression pattern of Ets-1 in human DKD tissues and whether its expression level can serve as a biomarker for predicting the clinical progression of DKD remain unclear. Based on this evidence, we hypothesize that the expression level of Ets-1 in renal tissue is closely associated with the severity and progression risk of DKD, and may serve as a novel biomarker for predicting DKD progression. To test this hypothesis, this study conducted a 3-year follow-up of 115 patients with biopsy-proven DKD, aiming to elucidate the relationship between renal Ets-1 expression and disease progression, and to explore its potential utility in predicting the progression of diabetic kidney disease. 2. Study Subjects and Methods achieved HbA1c levels between 6–8%, 96% achieved the target blood pressure (< 130/80 mmHg), and 93% achieved the target lipid levels (for cholestero 2.1 Study Subjects A total of 115 patients with DKD, confirmed by renal biopsy, were enrolled from four centers: The Second Hospital of Lanzhou University, Wuwei People's Hospital, Linxia People's Hospital, and The First Hospital of Lanzhou University, between January 2019 and February 2022. All patients had complete clinical and biochemical data, as well as renal pathological data. The study was approved by the Medical Ethics Committee of The Second Hospital of Lanzhou University, and written informed consent was obtained from all patients. All 115 patients were followed up for 3 years, starting from the date of renal biopsy. All patients received lifestyle modification counseling. The primary glucose-lowering regimen included insulin and SGLT2 inhibitors. Blood pressure was managed with ACE inhibitors (ACEIs) as the primary therapy, supplemented with other antihypertensive agents as needed. Statins were used for lipid management. During follow-up, 100% of patientsl, triglycerides, low-density lipoprotein, and very-low-density lipoprotein)[ 12 ]. 2.2 Research Methods 2.2.1 Collection of Clinical and Biochemical Parameters Demographic and clinical information at the time of renal biopsy was recorded for all patients, including sex, age, height, weight, blood pressure, diabetes duration, body mass index (BMI), hemoglobin, C-reactive protein(CRP), fasting blood glucose (FBG), glycated hemoglobin (HbA1c), urinary albumin excretion rate (UAER), 24-hour urinary protein excretion (24hUPE), serum creatinine (Scr), serum uric acid (SUA), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C). The estimated glomerular filtration rate (eGFR) was calculated using the CKD-EPI creatinine equation [ 13 ],Carotid plaque presence was assessed using carotid color Doppler ultrasound. The aforementioned clinical and biochemical parameters were collected at 1, 2, and 3 years during the follow-up period. Renal tissue specimens were fixed, paraffin-embedded, and sectioned for hematoxylin and eosin (H&E), Masson's trichrome, and periodic acid-Schiff (PAS) staining. Reserved renal tissue sections were used for Ets-1 immunohistochemistry and immunofluorescence staining. Control renal tissues were obtained from non-tumorous normal renal tissue adjacent to tumors in 15 subjects (11 males, 4 females, mean age 49 years [range 41–62 years]), including 7 cases of simple renal cyst, 6 cases of renal cell carcinoma, and 2 cases of angiomyolipoma. None of these control subjects had a history of diabetes or hypertension. 2.2.2 Diagnostic and Exclusion Criteria for DKD Subjects were included in the study if they met the following criteria: DKD diagnosis was based on (Kidney Disease: Improving Global Outcomes) KDIGO guidelines [ 14 ]. Patients had a history of diabetes, with UAER ≥ 30 mg/24h (≥ 20 µg/min) on at least two occasions within 3–6 months, or typical retinal changes persisting for more than 3 months, and renal biopsy findings consistent with DKD pathology. Exclusion criteria included type 1 diabetes, hereditary kidney diseases, other primary or secondary kidney diseases, acute or chronic infections, receipt of renal replacement therapy, cancer diagnosis, use of immunosuppressive agents, or pregnancy/lactation. 2.2.3 Clinical Staging of Diabetic Kidney Disease [14 ] Stage I: Increased glomerular filtration rate, enlarged kidney size, absence of albuminuria, and no pathological histological damage. Increased renal blood flow, and glomerular capillary perfusion pressure. Stage II: Normoalbuminuric stage. UAER is normal. Glomerular basement membrane thickening and increased mesangial matrix are present. eGFR is above normal. Stage III: Early Diabetes kidney disease. UAER persistently between 20–200 µg/min or 30–300 mg/24h. Glomerular basement membrane thickening and increased mesangial matrix are more pronounced, featuring nodular (Kimmelstiel-Wilson) and diffuse glomerular lesions, as well as arteriolar hyalinosis. Stage IV: Clinical or overt Diabetes kidney disease. UAER persistently > 200 µg/min or urinary protein > 0.5 g/24h, accompanied by elevated blood pressure and edema. Nodular and diffuse glomerular lesions and arteriolar hyalinosis are evident, glomerular obsolescence becomes more marked, and GFR begins to decline. Stage V: End-stage renal failure. Widespread glomerular obsolescence, elevated serum creatinine and blood urea nitrogen, accompanied by severe hypertension, hypoproteinemia, and edema. Disease progression criterion: At the end of the follow-up period, patients with eGFR ≤ 60 ml/min/1.73m² were classified as having disease progression, while those with eGFR > 60 ml/min/1.73m² were classified as having relatively stable disease. 2.2.4 Renal Histopathological Examination H&E, Masson's Trichrome, and PAS Staining: Renal biopsy tissues were fixed in 10% neutral buffered formalin for 24 hours, paraffin-embedded, and sectioned at 3 µm thickness. Sections were baked at 60°C for 2 hours, deparaffinized in xylene, rehydrated through a graded ethanol series, and subjected to conventional H&E, Masson's trichrome, and PAS staining. Control renal tissues were from non-tumorous normal renal tissue adjacent to tumors. Renal Pathology and Pathological Scoring: Diabetes kidney disease pathological classification was based on the criteria proposed by Tervaet et al[ 15 ]. Diabetes kidney disease pathology is divided into 4 classes: Class I: Glomerular basement membrane thickening observed by electron microscopy (EM) (> 395 nm in women, > 430 nm in men), without meeting any criteria for Class II, III, or IV glomerular lesions. Class II: 2a: Mild mesangial expansion (expanded mesangial area less than the mean area of capillary lumina) in more than 25% of the observed mesangial regions. 2b: Severe mesangial expansion (expanded mesangial area greater than the mean area of capillary lumina) in more than 25% of the total observed mesangial regions. Class III: Nodular sclerosis (Kimmelstiel-Wilson lesion) with 50% of glomeruli. Scoring Methods: Mesangial Lesion Score (0–3): 0: Normal or mild mesangial expansion; 1: Mesangial expansion less than the area of one capillary lumen; 2: Mesangial expansion equal to the area of one capillary lumen; 3: Mesangial expansion exceeding the area of one capillary lumen. Nodular Lesion Score (0–1): 0: No nodules; 1: One or more nodules present in the biopsy specimen (regardless of nodule size). Interstitial Fibrosis and Tubular Atrophy (IFTA) Score: 0: No IFTA; 1 (Mild): 50% of total area. Interstitial Inflammation Score: 0: No inflammation; 1 (Mild): Inflammation only in areas of IFTA; 2 (Severe): Inflammation present in areas without IFTA. Arteriolar Lesion Score: 0: None seen; 1 (Mild): Hyalinosis present in at least one arteriole; 2 (Severe): Hyalinosis present in more than two arterioles. Arteriosclerosis Score: 0: No intimal thickening; 1 (Mild): Intimal thickness less than medial thickness; 2 (Severe): Intimal thickness greater than medial thickness. The presence of other lesions was also evaluated, as appropriate, such as extracapillary hypercellularit, periglomerularr lesions, etc. All pathological slides were independently scored by three renal pathologists, and the average score was calculated. 2.2.5 Ets-1 and α-SMA Immunohistochemical and Immunofluorescence Staining Ets-1 and α-SMA Immunohistochemical Staining: Renal tissue sections were prepared as described for renal pathology staining. 3 µm sections were baked at 60°C for 2 hours, deparaffinized, and rehydrated. Antigen retrieval was performed using citrate buffer (pH 6.0) at 95°C for 20 minutes, followed by natural cooling. Endogenous peroxidase activity was blocked with 3% hydrogen peroxide at room temperature for 25 minutes. Sections were blocked with 5% normal goat serum for 30 minutes. Primary antibodies, rabbit anti-human Ets-1 (1:200, ab220361, Abcam, Cambridge, UK) and rabbit anti-human α-SMA (1:200, 14395-1-AP, Proteintech, Wuhan, China), were applied and incubated overnight at 4°C. Subsequently, Alexa Fluor 488-conjugated goat anti-rabbit IgG (H + L) (ab150077, Abcam) was applied and incubated at 37°C for 1 hour. DAB was used for chromogenic development, followed by hematoxylin counterstaining and mounting with neutral balsam. All stained areas of interest (AOIs) in glomeruli, tubulointerstitium, and vessels on the sections were photographed. The integrated optical density (IOD) of these regions was measured using Image-Pro Plus 7.0 (Media Cybernetics, Inc., Silver Spring, MD, USA). The area of the selected effective statistical region was measured, and the mean density (IOD/area) was calculated. The mean density was used as the expression level of Ets-1 and α-SMA for statistical analysis. Ets-1 and α-SMA Immunofluorescence Staining: Section preparation was as described previously. Sections were placed in EDTA (pH 8.0) antigen retrieval buffer and heated (95°C) at 705-800W for 10 minutes, then cooled at room temperature for 30 minutes. Washed in 0.01 mol/L PBS for 10 minutes. Sections were incubated with 0.4% pepsin (pH 2.0) in a humidified chamber at 37°C for 10 minutes, followed by washing. Sections were blocked with irrelevant animal serum blocking solution and incubated at room temperature for 20 minutes. Primary antibodies, rabbit anti-human Ets-1 (ab220361, Abcam, Cambridge, UK) 1:200 and rabbit anti-human α-SMA (1:200, 14395-1-AP, Proteintech, Wuhan, China), were applied and incubated overnight at 4°C. Elab Fluor® 594-conjugated goat anti-rabbit IgG (H + L) (EAB-1060, Elabscience Biotechnology) was applied and incubated at room temperature for 30–60 minutes. Sections were mounted with glycerol and observed under a fluorescence microscope for imaging. This was used for the localization of Ets-1 and α-SMA. Immunohistochemical staining analysis was performed by three renal pathologists. Ets-1 immunohistochemical and immunofluorescence staining were performed on the renal biopsy tissues. 2.3 Statistical Analysis Statistical analyses were performed using IBM SPSS Statistics version 26 software. All hypothesis tests were two-sided, with the significance level set at α = 0.05. Continuous variables conforming to a normal distribution are presented as mean ± standard deviation (Mean ± SD), and comparisons between groups were performed using the independent samples t-test. Variables not conforming to a normal distribution are presented as median (interquartile range) (Median (IQR)), and comparisons between groups were performed using the Mann-Whitney U test. Categorical variables are presented as frequency (percentage) (n (%)), and comparisons between groups were performed using the Chi-square test (χ²); if the proportion of cells with an expected count 20% or if any cell had an expected count < 1, Fisher's exact test was used. Bivariate correlation analysis was performed using Spearman's rank correlation analysis. Before constructing multivariate regression models, all candidate independent variables were assessed for multicollinearity by calculating the variance inflation factor (VIF). A VIF > 10 was used as the criterion for significant collinearity. If collinearity was present, highly correlated variables were either excluded or combined using methods like principal component analysis to ensure that the final model had all VIFs < 5. Binary logistic regression models were used to analyze the independent association between variables and disease progression. Based on the predicted probabilities from the final logistic regression model, a receiver operating characteristic (ROC) curve was plotted, and the area under the curve (AUC) with its 95% confidence interval (CI) was calculated. The optimal probability cut-off value was determined by maximizing the Youden index, and the sensitivity and specificity at this cut-off value are reported. 3. Results 3.1 Characteristics of the DKD Patient Cohort and Three-Year Follow-Up Outcomes The study enrolled 115 patients diagnosed with DKD by renal biopsy between December 2019 and February 2022 from five medical centers in Gansu Province. Among them, 73% were male and 27% were female. The majority were of Han ethnicity (89%), while the remaining 11% included Hui, Tibetan, and other ethnic groups. The median age was 50 years (range: 46–58 years), and the median body mass index (BMI) was 25.66 kg/m². A 3-year follow-up was conducted from the date of biopsy. Based on predefined disease progression criteria, 57.4% (66/115) of the patients, who had an eGFR ≤ 60 ml/min at the end of the follow-up, were classified into the progress group (PG). The remaining 42.6% (49/115), with an eGFR > 60 ml/min, were classified into the relatively stable group (RSG). No significant differences were observed between the two groups in terms of sex (p = 0.447), ethnicity (p = 0.748), age (p = 0.412), BMI (p = 0.302), or duration of diabetes (p = 0.525). (see Table 1 ). Table 1 Characteristics of a Cohort of Diabetes kidney disease Patients and Three-Year Follow-up Outcomes Stage Baseline Follow-up endpoint Overall n = 115 1 Progressive n = 66 1 Relatively Stable n = 49 1 Overall n = 115 1 Progressive n = 66 1 Relatively Stable n = 49 1 G1 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) G2 11(9.5%) 3(4.5%) 8(16.3%) 7(6.1%) 0(0) 7(14.3%) G3a 66(57.4%) 34(51.5%) 32(65.3%) 48(41.7%) 10(15.2%) 38(77.6%) G3b 38(33.1%) 31(46.9%) 7(14.34%) 44(38.3%) 40(60.6%) 4(8.2%) G4 0(0) 0(0) 0(0) 16(13.9%) 16(24.2%) 0(0) G5 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 3.2 Patients in the Disease Progression Group Showed More Severe Baseline Clinical Indicators and Vascular Complications Comparison of baseline characteristics revealed that although there were no significant differences in age, sex, or BMI between the progression group and the stable group, the progression group exhibited more significant disease activity. This included higher blood pressure levels (SBP, p < 0.001; DBP, p = 0.007), more severe renal injury (UAER, p < 0.001; 24-UPE, p < 0.001), and poorer renal function indicators (Scr, p < 0.001; SUA, p < 0.001); lower eGFR:(p < 0.001).(Table 2 ). Additionally, the progression group had more severe dyslipidemia, with higher total cholesterol (p = 0.012), LDL ( p < 0.001), and VLDL ( p < 0.001), while HDL was significantly lower ( p = 0.018). The inflammatory state was also more prominent in the progression group, with C-reactive protein levels significantly higher than in the stable group ( p < 0.001). Furthermore, the progression group had a higher proportion of hypertension (p < 0.001) and a greater prevalence of carotid plaques (p < 0.001). (Table 2 ). Table 2 Characteristics of baseline clinical parameters for patients in the disease progression group and the relatively stable group Characteristic Overall n = 115 1 Progressive n = 66 1 Relatively Stable n = 49 1 p 2 Sex 0.447 Male 84(73%) 50(76%) 34(69%) Female 31(27%) 16(24%) 15(31%) Ethnicity 0.748 Han Chinese 101(89%) 57(88%) 44(90%) Non-Han Chinese 13(11%) 8(12%) 5(10%) Age(years) 50.00(46.00,58.00) 51.00(46.75,59.25) 49.00(45.50,55.50) 0.412 BMI(kg/m²) 25.66(23.10,27.46) 25.28(21.90,28.09) 25.69(23.47,27.34) 0.302 SBP(mmHg) 140.00(133.00,150.00) 144.50(136.00,159.25) 135.00(127.50,140.00) < 0.001 DBP (mmHg) 89.00 (80.00, 94.00) 90.50(82.00,98.00) 86.00(77.00,92.00) 0.007 Diabetes duration (years) 7.00(2.00,11.00) 7.50(2.75,13.25) 6.00(2.00,10.00) 0.525 Hb(g/L) 134.00(121.00,148.00) 131.50(120.00,138.50) 141.00(132.50,151.50) < 0.001 SCR (µmol/L) 73.00(67.00,88.00) 80.00(76.00,94.75) 64.00(56.15,78.00) < 0.001 eGFR(mL/min/1.73m²) 89.00(79.05,107.32) 81.57(66.31,102.96) 97.70(94.25,109.30) < 0.001 UA (µmol/L) 373.00(309.00,435.00) 398.00(334.50,451.00) 359.00(285.00,400.50) 0.007 ALB(g/L) 35.00(37.00,39.00) 36.35(34.58,40.10) 37.40(36.40,39.60) 0.030 GLB(g/L) 34.30(32.10,35.60) 31.40 (31.48,35.60) 34.60 (33.00,35.60) 0.21 CHOL(mmol/L) 4.89(4.09,5.99) 5.15(4.20,6.25) 4.63(3.67,5.21) 0.012 TG(mmol/L) 1.85(1.42,2.89) 1.88(1.41,3.06) 1.80(1.43,2.55) 0.442 LDL(mmol/L) 2.14(1.46,2.81) 2.57(1.88,3.40) 1.57(1.34,2.34) < 0.001 VLDL(mmol/L) 2.12(1.15,3.25) 3.08(2.03,3.41) 1.25(1.05,1.36) < 0.001 HDL(mmol/L) 1.20(0.99,1.40) 1.10(0.90,1.37) 1.26(1.08,1.47) 0.018 CRP(mg/L) 5.00(1.41,10.00) 9.35(5.58,12.00) 1.49(0.71,2.94) < 0.001 UAER(mg/24h) 599.70(508.30,712.70) 682.40(596.35,851.08) 505.80(484.30,61.05) < 0.001 24hUPE(g/24h) 0.60(0.44,1.70) 1.25(0.54,2.01) 0.48(0.38,0.60) < 0.001 Hypertension comorbidity 0.002 Yes 68(59%) 47(71%) 21(43%) No 47(41%) 19(29%) 28(57%) Carotid plaques < 0.001 Yes 55(48%) 50(76%) 5(10%) No 60(52%) 16(24%) 44(90%) 1 n (%); Median (Q1, Q3) 2 Pearson's Chi-squared test; Fisher's exact test; Mann-Whitney U test 3.3 Pathological Characteristics of Renal Tissue in the Relatively Stable and Progressive Groups Consistent with clinical indicators, renal pathological damage was more severe in the progression group. The glomerular lesion score, interstitial lesion score, vascular score, and total renal pathology score were all significantly higher in the progression group compared to the stable group (all p < 0.001). (Table 3 ). Table 3 Pathological characteristics of renal tissue in patients with relatively stable and progressive groups Characteristic Overall n = 115 1 Progressive n = 66 1 Relatively Stable n = 49 1 p 2 Total Renal Pathology Score 5.00(3.00,11.00) 11.00(6.75,11.00) 3.00(2.50,4.00) < 0.001 Glomerular Injury Score 3.00(2.00,4.00) 4.00(2.00,6.00) 2.00(1.00,3.00) < 0.001 Tubulointerstitial Injury Score 2.00(1.00,4.00) 3.00(2.00,4.00) 1.00(0.00,1.00) < 0.001 Vascular Injury Score 1.00(0.00,3.00) 2.00(1.00,3.00) 0.00(0.00,0.50) < 0.001 1 Median (Q1, Q3) 2 Mann-Whitney U test 3.4 Ets-1 is Specifically Highly Expressed in Renal Tissue of Diabetic Kidney Disease Immunohistochemical and immunofluorescence analyses revealed low Ets-1 expression in normal glomeruli, tubules, and vasculature. In contrast, renal tissues from patients with DKD — including tubular, glomerular, and vascular regions — exhibited significantly elevated Ets-1 expression. Concurrently, α-SMA, a key marker of fibrosis, also showed a synchronized upregulation (all p < 0.001), suggesting a potential role of Ets-1 in the renal fibrotic process. Comparative analysis between the relatively stable and progression groups demonstrated markedly higher Ets-1 expression in renal tissues and vasculature of the progression group. Similarly, α-SMA expression was significantly elevated in these patients, further supporting the association between Ets-1 and disease progression. Table 4 and Fig. 1. Table 4 Ets-1 and α-SMA expression in renal tissue of DKD patients Characteristic(Mean density) Case Group (n = 1151 ) Control Group (n = 151 ) p 2 Ets-1 Expression in Renal Tissue 281.39(201.45,341.79) 74.40(69.40,83.10) < 0.001 Ets-1 Expression in Renal Vasculature 85.70(69.30,104.60) 39.90(29.70,48.90) < 0.001 α-SMA Expression in Renal Tissue 260.60(203.40,317.50) 98.60(89.70,126.50) < 0.001 α-SMA Expression in Vascular Walls 81.90(68.90,101.70) 38.90(31.90,49.40) < 0.001 1 Median (Q1, Q3) 2 Mann-Whitney U test Due to the skewed distribution of the data, we used the median (interquartile range) for statistical description and the Wilcoxon rank-sum test for intergroup comparisons. The average optical density of total Ets-1 in Diabetes kidney disease renal tissues was significantly upregulated. See Figs. 1 and 2 . Among clinical parameters, significant differences were observed between the progression group and the stable group in hypertension, hyperlipidemia, intimal thickening, and plaque formation. Therefore, we particularly noted the levels of vascular Ets-1 average optical density. The results demonstrated that vascular Ets-1 expression in Diabetes kidney disease renal tissues was significantly upregulated, and vascular α-SMA expression also showed an increasing trend (all p < 0.001). Figure 1. The Expression of Ets-1 and ɑ-SMA in DKD renal tissue Ctrl: Renal tissue adjacent to the tumor; D: DKD patients; G: Progress Group patients; R: Relatively stable group patients. 3.5 Disease Progression is Associated with More Severe Renal Pathological Damage and High Ets-1 Expression Pathological evaluation of glomerular, tubulointerstitial, and vascular lesions confirmed that patients in the progression group exhibited more severe damage in glomerular, tubulointerstitial, and vascular compartments, with significantly higher total pathology scores (all p < 0.001). Consistent with these findings, expression levels of Ets-1 and its potential downstream factor α-SMA were markedly upregulated in both renal tissue and vasculature of the progression group (all p < 0.001).(Table 5 , Fig. 2 ). Table 5 Comparison of Renal Ets-1 Expression Between Relatively Stable and Progressive Groups Characteristic (Mean density) Overall n = 115 1 Progressive n = 66 1 Relatively Stable n = 49 1 p 2 Ets-1 Expression in Renal Tissue 281.39 (201.45,341.79) 321.82 (283.18,511.58) 206.76 (168.19,246.97) < 0.001 Ets-1 Expression in Renal Vasculature 85.70 (69.30,104.60) 100.20 (85.20,148.48) 70.50 (58.65,80.20) < 0.001 α-SMA Expression in Renal Tissue 260.60 (203.40,317.50) 310.60 (257.88,420.60) 210.50 (170.15,253.60) < 0.001 α-SMA Expression in Vascular Walls 81.90 (68.90,101.70) 100.20 (78.55,120.53) 69.30 (57.85,79.90) < 0.001 1 Median (Q1, Q3) 2 Mann-Whitney U test Examples of patients in the progressive and relatively stable groups: A PG patient, male, 56 years old, during renal biopsy, blood pressure: 160/96mmHg, SCR; 102umol/L,eGFR: 62ml/min, After a 3-year follow-up, blood pressure was controlled, with a creatinine level of 193umol/L and eGFR of 46ml/min. Pathological examination showed that most glomerular capillary loops were still open, and there was moderate to severe proliferation of the mesangial matrix; thickening of several glomerular capsule walls, extracapsular fibrosis, thickening of glomerular basement membrane, multifocal atrophy (atrophy area 10–15%), and severe hyalinization of several small artery walls. Thickening of the walls of individual small arteries. A RSG patient, 54 years old, during renal biopsy, blood pressure: 134/80 mmHg and SCR; 73 umol/L, eGFR: 87 ml/min, After a 3-year follow-up, blood pressure was controlled, with a creatinine level of 102 umol/L and eGFR of 70 ml/min. Pathological examination showed that the glomerular capillary loops were still open, and there was focal segmental proliferation in the mesangial area; Vacuolar and granular degeneration of renal tubular epithelial cells, focal atrophy of renal interstitium (atrophy area of about 5–10%), and severe hyalinization of several small artery walls. 3.6 Expression of Ets-1 in Renal Tissue Shows Strong Positive Correlation with Renal Injury and Fibrosis Indicators Spearman correlation analysis revealed that the expression intensity of Ets-1 in renal tissue was significantly positively correlated with multiple clinical renal injury indicators—such as proteinuria, serum creatinine, and blood pressure—as well as systemic inflammatory markers (CRP) (all p < 0.001), while exhibiting a negative correlation with eGFR. More importantly, Ets-1 demonstrated strong positive correlations with all renal pathological scores—including glomerular, interstitial, and vascular lesion scores, as well as total pathology score—and with the expression of α-SMA (all r > 0.7, p < 0.01). (Tables 6 , 7 ). Table 6 Comparison of Renal ETS-1 Expression Between Relatively Stable and Progressive Groups Characteristic r p Characteristic r p Characteristic r p Sex 0.166 0.077 Hb -0.359** <0.001 SCR 0.336** <0.001 Age -0.15 0.111 ALB -0.15 0.109 eGFR -0.213* 0.022 Ethnicity -0.098 0.297 GLB -0.079 0.404 UA 0.338** <0.001 BMI -0.068 0.473 CHOL 0.201* 0.031 CRP 0.672** < 0.001 SBP 0.419** <0.001 TG 0.133 0.155 UAER 0.537** < 0.001 DBP 0.451** < 0.001 LDL 0.322** <0.001 24h UTP 0.476** <0.001 Diabetes duration 0.012 0.901 VLDL 0.608** <0.001 Hypertension comorbidity 0.285** 0.002 HDL -0.241** 0.01 Carotid plaques 0.696** <0.001 * At the 0.05 level (double tailed), the correlation is significant; **At the 0.01 level (double tailed), the correlation is significant Table 7 Correlation Analysis of Renal Ets-1 Expression and Renal Pathological Scores Characteristic r p Total Renal Pathology Score 0.762** <0.001 Glomerular Injury Score 0.841** <0.001 Tubulointerstitial Injury Score 0.806** <0.001 Arteriolar Injury Score 0.849** <0.001 α-SMA Expression in Renal Tissue 0.786** <0.001 Ets-1 Expression in Renal Vasculature 0.850** <0.001 α-SMA Expression in Vascular Walls 0.711** <0.001 * At the 0.05 level (double tailed), the correlation is significant; **At the 0.01 level (double tailed), the correlation is significant. 3.7 Multicollinearity Diagnostics of Clinical Indicators Multicollinearity was assessed using the variance inflation factor (VIF) to evaluate the degree of collinearity among variables. Severe multicollinearity may lead to inflated variance of regression coefficients. Independent variables with a tolerance between 0.1 and 1 and VIF < 5 were retained. Under these conditions, no significant evidence of multicollinearity was detected among Ets-1, duration of (Type 2 Diabetes Mellitus) T2DM, age, BMI, SBP, DBP, UAER, LDL, TG, TC, HDL, and CRP. However, severe multicollinearity (VIF > 10) was observed among renal histopathological scores, Scr, eGFR, mean optical density of α-SMA, mean optical density of renal Ets-1, and mean optical density of vascular α-SMA (12.211). This likely indicates that these variables collectively reflect a core pathophysiological process—presumably renal fibrosis or vascular injury. To mitigate multicollinearity in the logistic regression model, the following variables were excluded: renal histopathological score, vascular lesion score, tubulointerstitial lesion score, glomerular lesion score, eGFR, and mean optical density of vascular α-SMA. (Table 8 ). Table 8 Multivariate ordinal logistic regression analysis Characteristic VIF值 Characteristic VIF Sex 8.283 SCR 33.374 Age 3.959 eGFR 27.797 Ethnicity 1.5 UA 1.888 BMI 1.661 CHOL 2.337 SBP 2.096 TG 1.83 DBP 2.436 LDL 2.472 Diabetes duration 1.613 VLDL 4.295 Hb 2.447 HDL 1.923 ALB 2.348 CRP 5.389 GLB 1.896 UAER 3.381 Hypertension comorbidity 1.89 24h UTP 4.531 Carotid plaques 3.475 Ets-1 Expression in Renal Tissue 13.426 Total Renal Pathology Score 363.477 Ets-1 Expression in Renal Vasculature 7.568 Glomerular Injury Score 43.179 α-SMA Expression in Renal Tissue 18.918 Tubulointerstitial Injury Score 52.579 α-SMA Expression in Vascular Walls 12.211 Arteriolar Hyalinosis Score 68.286 3.8 Ets-1 is an Independent Risk Factor for Predicting the Progression of Diabetic Kidney Disease After adjusting for the effects of multicollinearity, binary logistic regression analysis was performed. Using disease progression (progress = 1, stable = 0) as the dependent variable and statistically significant variables from the univariate analysis as independent variables, a binary logistic regression model was constructed. Variable selection was conducted using the backward stepwise method. The analysis confirmed that renal Ets-1 expression level, UAER, 24hUPE, and Scr were independent risk factors for predicting DKD progression (all p < 0.05). ROC curve analysis further demonstrated that Ets-1 had high predictive efficacy for disease progression (AUC = 0.875), with a value comparable to the traditional UAER indicator (AUC = 0.874). (Tables 9 , 10 ; Fig. 3 ). Table 9 Analysis of Independent Predictors for Disease Progression Predictor β SE Wald Z p OR 95% CI for OR Lower limit Upper Limit UAER 0.027 0.009 9.976 0.002 1.028 1.01 1.045 24hUPE 2.397 1.137 4.444 0.035 10.994 1.183 102.127 SCR 0.076 0.037 4.213 0.04 1.079 1.003 1.161 Ets-1 Expression in Renal Tissue 0.014 0.007 4.169 0.041 1.014 1.001 1.028 CHOL 0.785 0.419 3.511 0.061 2.192 0.965 4.983 Hb -0.062 0.034 3.258 0.071 0.94 0.878 1.005 LDL 0.959 0.602 2.539 0.111 2.608 0.802 8.483 Table 10 Evaluation of the predictive efficacy of Ets-1 expression level in renal tissue for disease progression Predictor AUC 95% CI Sensitivity(%) Specificity(%) Youden's Index UAER 0.874 0.811–0.936 75.8 85.7 0.615 Ets-1 Expression in Renal Tissue 0.875 0.81–0.939 84.8 81.6 0.664 4. Discussion This study conducted a 3-year follow-up of 115 patients with biopsy-confirmed DKD from four centers. Based on predefined disease progression criteria, 57.4% (66/115) of the patients were classified into the progression group (PG) at the end of follow-up, while 42.6% (49/115) were classified as relatively stable (RSG). Analysis of baseline characteristics revealed that patients in the PG exhibited more marked systemic disease activity, including higher blood pressure levels, poorer renal function indicators (elevated Scr and UAER, lower eGFR), more severe dyslipidemia and inflammatory status, as well as a higher prevalence of hypertension and carotid plaque. Renal pathological assessment demonstrated significantly elevated Ets-1 expression in the renal tissues of DKD patients—including glomerular, tubulointerstitial, and vascular compartments—compared to normal renal tissue. Concurrently, α-SMA, a marker of fibrosis, also showed a synchronous upregulation trend. Spearman correlation analysis indicated that the intensity of renal Ets-1 expression was significantly positively correlated with multiple clinical indicators of kidney injury (such as proteinuria, serum creatinine, and blood pressure) and systemic inflammation (CRP), while it was negatively correlated with eGFR. More importantly, Ets-1 exhibited strong positive correlations with all renal pathological scores (including glomerular, interstitial, and vascular lesion scores, as well as the total score) and with α-SMA expression. Our results suggest that Ets-may 1 be closely associated with the progression of diabetic glomerular and interstitial fibrosis, as well as vascular injury. Ets-1 belongs to the Ets transcription factor family, which shares a conserved DNA-binding domain that specifically recognizes the core GGA(A/T) DNA sequence. It plays key roles in cell differentiation, proliferation, and apoptosis, and regulates various biological processes such as angiogenesis and development. Ets-1 is essential for normal kidney development and the maintenance of glomerular integrity [ 7 , 8 , 16 ]. Studies have shown that advanced chronic kidney disease (CKD) is often characterized by abnormal accumulation of extracellular matrix within the glomeruli. Matrix metalloproteinases (MMPs) play a critical role in ECM remodeling in various glomerular diseases. As a transcription factor, Ets-1 regulates the expression of multiple matrix proteases, including MMP-1, MMP-3, and MMP-9, and may influence ECM deposition by modulating the MMP-2/TIMP-2 system balance[ 7 – 9 ]. Ets-1 also contributes to renal fibrosis by activating downstream targets such as plasminogen activator inhibitor-1 (PAI-1) and connective tissue growth factor (CTGF), thereby regulating angiotensin II (Ang II)-induced fibroblast activation rat mesangial cells, Ang II induces Ets-1 expression, which directly activates the fibronectin promoter and promotes fibronectin production. Conversely, Ets-1 knockdown significantly reduces fibronectin expression in mesangial cells[ 19 ]. Ets-1 is highly expressed in the kidney across various renal injury models [ 8 , 9 ]. In Ets-1⁻/⁻ mice, Ang II infusion led to markedly reduced ROS (Reactive Oxygen Species, ROS) production in the thoracic aorta. siRNA-mediated knockdown of Ets-1 effectively suppressed ROS generation and p47phox expression. Since p47phox is a major source of ROS in the kidney and vascular system and promotes renal inflammation, a positive feedback relationship appears to exist between Ets-1 and ROS[ 20 ]. In Dahl salt-sensitive (SS) rats, activation of the renin–angiotensin system increases Ets-1 expression and upregulates fibronectin, contributing to the development of end-organ injury in this model. Blocking Ets-1 alleviates the severity of renal damage in these animals[ 11 ]. These findings collectively highlight the central role of Ets-1 in renal injury and tissue and vascular remodeling. In any form of CKD, dysregulation of extracellular matrix (ECM) remodeling, tubular epithelial-mesenchymal transition, activation of mesangial cells and fibroblasts, inflammatory cell infiltration, and apoptosis are critical processes driving disease progression[ 21 ]. Diabetes kidney disease is characterized by glomerulosclerosis and tubulointerstitial fibrosis. Our renal pathological assessment confirmed that patients in the progression group exhibited more severe glomerular, tubulointerstitial, and vascular lesions, along with significantly higher total pathology scores. Compared with normal renal tissues, Ets-1 expression was significantly elevated in the renal tissues of DKD patients—localized to glomerular, tubulointerstitial, and vascular compartments. More importantly, Ets-1 demonstrated strong positive correlations with all renal pathological scores, including those for glomerular, interstitial, and vascular lesions, as well as the total score. Concurrently, α-SMA—a marker of epithelial-mesenchymal transition (EMT)—also showed a synchronous upregulation. These findings suggest that Ets-may 1 promote the progression of Diabetes kidney disease by initiating the expression of fibrotic and inflammatory genes, facilitating the transdifferentiation of mesangial cells and tubular epithelial cells, and thereby accelerating glomerular and interstitial fibrosis. We thus propose that under hyperglycemic conditions, sustained activation of Ets-1 is closely associated with the progression of diabetic kidney disease. In our study, the progression group exhibited a higher incidence of hypertension, elevated blood pressure, dyslipidemia, and plaque formation. Therefore, we paid particular attention to the vascular expression of Ets-1. Results revealed significantly upregulated Ets-1 expression in the vascular walls of renal tissues—including interstitial vessels and glomerular capillary walls—in the progression group. Expression of its downstream factor α-SMA was also markedly increased. These results indicate that Ets-may 1 play a critical role in diabetic renal vascular pathology. Owing to its broad expression profile and potent transcriptional regulatory capacity, Ets-1 is instrumental in modulating cell proliferation, differentiation, apoptosis, and angiogenesis. Activation of the renin-angiotensin system (RAS) is a key mechanism in the pathogenesis of diabetes kidney disease. Studies have shown that when mouse thoracic aortas are systemically perfused with angiotensin II, Ets-1 is rapidly activated in vascular smooth muscle cells and endothelial cells. Ets1-knockout mice exhibited significantly attenuated arterial wall thickening, perivascular fibrosis, and cardiac hypertrophy [ 22 , 23 ]. In a rat carotid artery balloon injury model, Ets-1 mRNA expression increased rapidly (within 2 hours) in the damaged artery, with pronounced nuclear expression within 24 hours post-injury. Blocking Ets-1 effectively suppressed neointima formation in this model. Moreover, Ets-1 can promote inflammation by regulating proinflammatory cytokines and adhesion molecules such as IL-6, MCP-1, P-selectin, and E-selectin. Inhibiting Ets-1 reduced neointimal formation in mouse carotid arteries and downregulated expression of NOX2, NOX4, E-selectin, and MCP-1[ 24 ]. These studies suggest that Ets-1 is involved not only in glomerulosclerosis and interstitial fibrosis, but also in diabetic vascular remodeling and plaque formation. In recent years, the role of inflammation in DKD progression has garnered increasing attention[ 25 ]. Our study found that C-reactive protein (CRP) levels were significantly higher in the progression group than in the relatively stable group, and Ets-1 expression was positively correlated with CRP levels, indicating a close relationship between Ets-1 and the inflammatory state in diabetes kidney disease. Numerous studies have demonstrated that Ets-1 directly initiates fibrosis-related genes—promoting tissue fibrosis—and regulates the expression of various genes, including growth factors, chemokines, and adhesion molecules. For example, high glucose upregulates protein tyrosine phosphatase 1B (PTP1B) expression via Ets-1, enhancing monocyte/endothelial adhesion and vascular cell adhesion molecule-1 (VCAM-1) expression in human umbilical vein endothelial cells, thereby contributing to endothelial inflammation and the pathogenesis of cardiovascular complications in diabetes[ 26 ]. In patients with inflammatory bowel disease, Ets-1 is highly expressed and promotes Th1-driven mucosal inflammation through cold-inducible RNA-binding protein (CIRBP)[ 27 ]. Additionally, Ets-1 initiates PDGF-A expression at both transcriptional and mRNA levels in primary rat aortic smooth muscle cells induced by (Platelet-Derived Growth Factor Subunit A) PDGF-A[ 28 ]. These findings suggest that Ets-1 contributes to disease progression by sustaining a chronic inflammatory state in DKD. To elucidate the clinical value of Ets-1 in the progression of DKD, we conducted a three-year follow-up of 115 patients with DKD to evaluate the predictive value of baseline Ets-1 expression. After adjusting for multicollinearity, binary logistic regression confirmed that renal Ets-1 expression level, UAER, 24UPE and Scr were independent risk factors for predicting DKD progression. ROC curve analysis further demonstrated that Ets-1 exhibited high predictive efficacy for disease progression (AUC = 0.875), comparable to that of the traditional UAER marker (AUC = 0.874). UAER is recognized as an early diagnostic indicator for DKD and a biomarker for evaluating disease progression and vascular endothelial injury[ 29 – 31 ]. In both diabetic and non-diabetic kidney diseases, 24UPE is closely associated with disease progression. Persistent proteinuria is an independent risk factor for renal function deterioration and increased cardiovascular risk [ 32 , 33 ],However, the predictive value of 24UPE and albumin excretion remains limited due to susceptibility to various confounding factors[ 4 – 6 ],To date, renal pathology also lacks specific markers for predicting disease progression[ 34 ]. As an early-response transcription factor, Ets-1 has been proposed as a biomarker for progression in certain diseases [ 35 , 36 ]. In esophageal cancer, overexpression of MMP-7 is significantly associated with poor prognosis, and Ets-1 mRNA levels correlate positively with MMP-7 expression, suggesting Ets-1 as a potential marker for advanced esophageal cancer[ 37 ]. Therefore, we propose that Ets-may 1 serve as a novel clinical biomarker for predicting DKD progression. Although this study suggests the potential of Ets-1 in predicting DKD progression, several challenges remain for its use as a diagnostic biomarker. First, the study was retrospective in design, and the enrolled patients consisted predominantly of those with stage II and III pathological changes, with a lack of early-stage DKD patients. Statistical multicollinearity was also present. Second, due to the fact that renal biopsy is not a mandatory procedure for DKD diagnosis, the sample size was relatively small. Furthermore, although patients were enrolled from multiple centers, all were within the same province, which may introduce selection bias. These limitations may affect the generalizability of the results and hinder a comprehensive understanding of the systemic expression pattern of Ets-1 in DKD. Finally, no multicenter clinical validation was performed due to practical constraints. Abbreviations Abbreviation full name DKD Diabetic kidney disease Ets-1 E-Twenty-Six-1 α-SMA Alpha-Smooth Muscle Actin ESRD end-stage renal disease eGFR estimated Glomerular Filtration Rate UAER urinary albumin excretion rate RRT replacement therapy PG progress group RSG relatively stable group CRP C-reactive Protein UAER Urinary Albumin Excretion Rate 24-hour-UTP 24-hour Urinary Total Protein ROC Receiver Operating Characteristic AUC Area Under the (ROC) CurveArea Under the (ROC) Curve RRT renal replacement therapy ECM extracellular matrix MMP-1 Matrix Metallopeptidase 1 MMP-3 Matrix Metallopeptidase 3 MMP-9 Matrix Metallopeptidase 9 TIMP-1 TIMP Metallopeptidase Inhibitor 1 SS salt-sensitive ACEIs ACE inhibitors SGLT2 Sodium-Glucose Cotransporter 2 BMI body mass index FBG fasting blood glucose HbA1c glycated hemoglobin Scr serum creatinine SUA serum uric acid TC total cholesterol TG triglycerides HDL-C high-density lipoprotein cholesterol LDL-C low-density lipoprotein cholesterol H&E hematoxylin and eosin PAS periodic acid-Schiff KDIGO Kidney Disease: Improving Global Outcomes EM electron microscopy IFTA Interstitial Fibrosis and Tubular Atrophy AOIs areas of interest IOD integrated optical density VIF variance inflation factor EDTA E thylenediaminetetraacetic A cid PBS P hosphate- B uffered S aline CI confidence interval T2DM Type 2 Diabetes Mellitus CKD chronic kidney disease MMPs Matrix metalloproteinases PAI-1 plasminogen activator inhibitor-1 CTGF onnective tissue growth factor Ang II angiotensin II EMT epithelial-mesenchymal transition RAS renin-angiotensin system CRP C-reactive protein PTP 1B protein tyrosine phosphatase 1B VCAM-1 vascular cell adhesion molecule-1 CIRBP cold-inducible RNA-binding protein PDGF-A Platelet-Derived Growth Factor Subunit A Declarations Availability of data and materials All data generated or analyzed during this study are included in this article. Further enquiries can be directed to the corresponding author. Competing interests The authors declare that they have no competing interests. Ethics approval and consent to participate This study received approval from the Ethics Committee of The Second Affiliated Hospital of Lanzhou University ( 2024A-1048 ), all methods were carried out in accordance with relevant guidelines and regulations. All participants provided written informed consent before their inclusion in this study. Funding This research received support from various funding sources, the Gansu Provincial Administration of Traditional Chinese Medicine (GZKZ-2024-24), Gansu Provincial Science and International cooperation project of Technology Department (24YFWA012), And including the Talent Innovation and Entrepreneurship Project of Lanzhou City, Gansu Province (Grant No. 2021-RC-94), Additionally, our experiments were supported by the Clinical Medical Research Center of Gansu Province (Grants: 21JR7RA436). Declaration of generative AI use No AI-assisted tools were used in the writing of this work. Data availability All data generated or analyzed during this study are included in this article. Further enquiries can be directed to the corresponding author. Conflict of interest The authors declare that they have no competing interests. Author contributions XCZ performed the experiments and manuscript writing. TXW, YKK, TXL,,and XQW are responsible for patient inclusion and follow-up management, while BRW, ZYL, XZM, ZRL analyzed data and prepared figures/tables. JQW, TXW, WL, YYW, YXN, YW, WJZ, WKZ, XXT, RRD, ZDT, YJL and FTT are responsible for reviewing renal pathology scores. FTT conceptualizes and designs this study, with FTT participating in manuscript review. All authors participated in the writing of the article and approved the final draft. 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09:33:41","extension":"html","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":224936,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8378039/v1/6e62c65090550e00c94b59d7.html"},{"id":99220014,"identity":"3c5e2392-281a-41dc-b16a-ef404f2462c7","added_by":"auto","created_at":"2025-12-30 09:33:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1523362,"visible":true,"origin":"","legend":"\u003cp\u003eThe Expression of Ets-1 and ɑ-SMA in DKD renal tissue\u003c/p\u003e\n\u003cp\u003eCtrl: Renal tissue adjacent to the tumor; D: DKD patients; G: Progress Group patients; R: Relatively stable group patients.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8378039/v1/7169f8a281f8dd0975aaeb3f.png"},{"id":99317149,"identity":"f7f10eab-dd8b-46c3-9208-20094376cada","added_by":"auto","created_at":"2025-12-31 16:29:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1874215,"visible":true,"origin":"","legend":"\u003cp\u003ePathological characteristics of renal tissue in DKD with relatively stable and progressive groups\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8378039/v1/f4f57f78244a203a7f1909a1.png"},{"id":99317968,"identity":"0623de62-11b7-42a2-a5ee-0c121bbe0db3","added_by":"auto","created_at":"2025-12-31 16:31:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1104449,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curves to determine risk of disease progression\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8378039/v1/78655ee21ac334514bc2ca1d.png"},{"id":101297197,"identity":"ab4e8fda-d2bf-40c2-84a4-80394c1f41fd","added_by":"auto","created_at":"2026-01-28 09:25:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5700880,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8378039/v1/54c2a914-2b9c-406a-90b7-dcfdec70c837.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Correlation Between Ets-1 and the Progression of Diabetic Kidney Disease","fulltext":[{"header":"1 Background","content":"\u003cp\u003eDiabetic kidney disease (DKD) is a common microvascular complication of diabetes, affecting approximately 25\u0026ndash;40% of patients with type 2 diabetes, leading to renal impairment and chronic kidney disease. Currently, DKD has become a leading cause of renal replacement therapy (RRT)[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, the course of DKD exhibits significant heterogeneity, as not all patients progress to end-stage renal disease (ESRD)[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Commonly used clinical indicators, such as estimated Glomerular Filtration Rate (eGFR) and serum creatinine, reflect current renal function but are insufficient for predicting the progression of renal decline and disease prognosis. The urinary albumin excretion rate (UAER) is considered a biomarker for predicting DKD progression, yet its predictive value remains limited and is susceptible to various confounding factors[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. While renal pathology can provide more precise information on kidney injury\u0026mdash;including glomerulosclerosis, vascular lesions, and tubulointerstitial fibrosis\u0026mdash;it lacks specific markers for predicting disease progression. Therefore, identifying novel biomarkers capable of earlier and more accurate prediction of DKD progression is an urgent need for achieving personalized treatment and improving patient outcomes.\u003c/p\u003e \u003cp\u003eThe primary pathological features of DKD include extracellular matrix (ECM) protein deposition, mesangial expansion, basement membrane thickening, tubular atrophy, interstitial fibrosis, and vascular sclerosis. The core of this process is an imbalance in ECM metabolism. The transcription factor Ets-1 (E26 avian erythroblastosis virus transcription factor-1) has been identified as a key regulator of ECM metabolism. It profoundly influences the fibrotic process by modulating the expression of a series of target genes (e.g., MMP-1, MMP-3, MMP-9, TIMP-1)[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePreliminary studies suggest that Ets-1 plays a significant role in various models of renal fibrosis. In mesangioproliferative glomerulonephritis models, Ets-1 expression in mesangial cells increases over time and is closely associated with mesangial cell activation. In a rat crescentic glomerulonephritis model, Ets-1 was overexpressed and may be involved in the development and progression of the disease [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In Dahl salt-sensitive (SS) rats, activation of the renin-angiotensin system increased Ets-1 expression, and blocking Ets-1 attenuated the severity of renal injury, suggesting Ets-1 as a potential therapeutic target for hypertension and kidney injury [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].Our preliminary analysis also indicated significantly upregulated Ets-1 expression in the renal tissue of DKD patients. However, the expression pattern of Ets-1 in human DKD tissues and whether its expression level can serve as a biomarker for predicting the clinical progression of DKD remain unclear.\u003c/p\u003e \u003cp\u003eBased on this evidence, we hypothesize that the expression level of Ets-1 in renal tissue is closely associated with the severity and progression risk of DKD, and may serve as a novel biomarker for predicting DKD progression. To test this hypothesis, this study conducted a 3-year follow-up of 115 patients with biopsy-proven DKD, aiming to elucidate the relationship between renal Ets-1 expression and disease progression, and to explore its potential utility in predicting the progression of diabetic kidney disease.\u003c/p\u003e"},{"header":"2. Study Subjects and Methods","content":"\u003cp\u003eachieved HbA1c levels between 6\u0026ndash;8%, 96% achieved the target blood pressure (\u0026lt;\u0026thinsp;130/80 mmHg), and 93% achieved the target lipid levels (for cholestero\u003cb\u003e2.1 Study Subjects\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA total of 115 patients with DKD, confirmed by renal biopsy, were enrolled from four centers: The Second Hospital of Lanzhou University, Wuwei People's Hospital, Linxia People's Hospital, and The First Hospital of Lanzhou University, between January 2019 and February 2022. All patients had complete clinical and biochemical data, as well as renal pathological data. The study was approved by the Medical Ethics Committee of The Second Hospital of Lanzhou University, and written informed consent was obtained from all patients.\u003c/p\u003e \u003cp\u003eAll 115 patients were followed up for 3 years, starting from the date of renal biopsy. All patients received lifestyle modification counseling. The primary glucose-lowering regimen included insulin and SGLT2 inhibitors. Blood pressure was managed with ACE inhibitors (ACEIs) as the primary therapy, supplemented with other antihypertensive agents as needed. Statins were used for lipid management. During follow-up, 100% of patientsl, triglycerides, low-density lipoprotein, and very-low-density lipoprotein)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Research Methods\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Collection of Clinical and Biochemical Parameters\u003c/h2\u003e \u003cp\u003eDemographic and clinical information at the time of renal biopsy was recorded for all patients, including sex, age, height, weight, blood pressure, diabetes duration, body mass index (BMI), hemoglobin, C-reactive protein(CRP), fasting blood glucose (FBG), glycated hemoglobin (HbA1c), urinary albumin excretion rate (UAER), 24-hour urinary protein excretion (24hUPE), serum creatinine (Scr), serum uric acid (SUA), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C). The estimated glomerular filtration rate (eGFR) was calculated using the CKD-EPI creatinine equation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e],Carotid plaque presence was assessed using carotid color Doppler ultrasound. The aforementioned clinical and biochemical parameters were collected at 1, 2, and 3 years during the follow-up period.\u003c/p\u003e \u003cp\u003eRenal tissue specimens were fixed, paraffin-embedded, and sectioned for hematoxylin and eosin (H\u0026amp;E), Masson's trichrome, and periodic acid-Schiff (PAS) staining. Reserved renal tissue sections were used for Ets-1 immunohistochemistry and immunofluorescence staining. Control renal tissues were obtained from non-tumorous normal renal tissue adjacent to tumors in 15 subjects (11 males, 4 females, mean age 49 years [range 41\u0026ndash;62 years]), including 7 cases of simple renal cyst, 6 cases of renal cell carcinoma, and 2 cases of angiomyolipoma. None of these control subjects had a history of diabetes or hypertension.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Diagnostic and Exclusion Criteria for DKD\u003c/h2\u003e \u003cp\u003eSubjects were included in the study if they met the following criteria: DKD diagnosis was based on (Kidney Disease: Improving Global Outcomes) KDIGO guidelines [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePatients had a history of diabetes, with UAER\u0026thinsp;\u0026ge;\u0026thinsp;30 mg/24h (\u0026ge;\u0026thinsp;20 \u0026micro;g/min) on at least two occasions within 3\u0026ndash;6 months, or typical retinal changes persisting for more than 3 months, and renal biopsy findings consistent with DKD pathology. Exclusion criteria included type 1 diabetes, hereditary kidney diseases, other primary or secondary kidney diseases, acute or chronic infections, receipt of renal replacement therapy, cancer diagnosis, use of immunosuppressive agents, or pregnancy/lactation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Clinical Staging of Diabetic Kidney Disease [14 ]\u003c/h2\u003e \u003cp\u003eStage I: Increased glomerular filtration rate, enlarged kidney size, absence of albuminuria, and no pathological histological damage. Increased renal blood flow, and glomerular capillary perfusion pressure.\u003c/p\u003e \u003cp\u003eStage II: Normoalbuminuric stage. UAER is normal. Glomerular basement membrane thickening and increased mesangial matrix are present. eGFR is above normal.\u003c/p\u003e \u003cp\u003eStage III: Early Diabetes kidney disease. UAER persistently between 20\u0026ndash;200 \u0026micro;g/min or 30\u0026ndash;300 mg/24h. Glomerular basement membrane thickening and increased mesangial matrix are more pronounced, featuring nodular (Kimmelstiel-Wilson) and diffuse glomerular lesions, as well as arteriolar hyalinosis.\u003c/p\u003e \u003cp\u003eStage IV: Clinical or overt Diabetes kidney disease. UAER persistently\u0026thinsp;\u0026gt;\u0026thinsp;200 \u0026micro;g/min or urinary protein\u0026thinsp;\u0026gt;\u0026thinsp;0.5 g/24h, accompanied by elevated blood pressure and edema. Nodular and diffuse glomerular lesions and arteriolar hyalinosis are evident, glomerular obsolescence becomes more marked, and GFR begins to decline.\u003c/p\u003e \u003cp\u003eStage V: End-stage renal failure. Widespread glomerular obsolescence, elevated serum creatinine and blood urea nitrogen, accompanied by severe hypertension, hypoproteinemia, and edema.\u003c/p\u003e \u003cp\u003eDisease progression criterion: At the end of the follow-up period, patients with eGFR\u0026thinsp;\u0026le;\u0026thinsp;60 ml/min/1.73m\u0026sup2; were classified as having disease progression, while those with eGFR\u0026thinsp;\u0026gt;\u0026thinsp;60 ml/min/1.73m\u0026sup2; were classified as having relatively stable disease.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 Renal Histopathological Examination\u003c/h2\u003e \u003cp\u003eH\u0026amp;E, Masson's Trichrome, and PAS Staining: Renal biopsy tissues were fixed in 10% neutral buffered formalin for 24 hours, paraffin-embedded, and sectioned at 3 \u0026micro;m thickness. Sections were baked at 60\u0026deg;C for 2 hours, deparaffinized in xylene, rehydrated through a graded ethanol series, and subjected to conventional H\u0026amp;E, Masson's trichrome, and PAS staining. Control renal tissues were from non-tumorous normal renal tissue adjacent to tumors.\u003c/p\u003e \u003cp\u003eRenal Pathology and Pathological Scoring: Diabetes kidney disease pathological classification was based on the criteria proposed by Tervaet et al[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Diabetes kidney disease pathology is divided into 4 classes:\u003c/p\u003e \u003cp\u003eClass I: Glomerular basement membrane thickening observed by electron microscopy (EM) (\u0026gt;\u0026thinsp;395 nm in women, \u0026gt;\u0026thinsp;430 nm in men), without meeting any criteria for Class II, III, or IV glomerular lesions.\u003c/p\u003e \u003cp\u003eClass II: 2a: Mild mesangial expansion (expanded mesangial area less than the mean area of capillary lumina) in more than 25% of the observed mesangial regions. 2b: Severe mesangial expansion (expanded mesangial area greater than the mean area of capillary lumina) in more than 25% of the total observed mesangial regions.\u003c/p\u003e \u003cp\u003eClass III: Nodular sclerosis (Kimmelstiel-Wilson lesion) with \u0026lt;\u0026thinsp;50% global glomerulosclerosis.\u003c/p\u003e \u003cp\u003eClass IV: Global glomerulosclerosis affecting\u0026thinsp;\u0026gt;\u0026thinsp;50% of glomeruli.\u003c/p\u003e \u003cp\u003eScoring Methods:\u003c/p\u003e \u003cp\u003eMesangial Lesion Score (0\u0026ndash;3): 0: Normal or mild mesangial expansion; 1: Mesangial expansion less than the area of one capillary lumen; 2: Mesangial expansion equal to the area of one capillary lumen; 3: Mesangial expansion exceeding the area of one capillary lumen.\u003c/p\u003e \u003cp\u003eNodular Lesion Score (0\u0026ndash;1): 0: No nodules; 1: One or more nodules present in the biopsy specimen (regardless of nodule size).\u003c/p\u003e \u003cp\u003eInterstitial Fibrosis and Tubular Atrophy (IFTA) Score: 0: No IFTA; 1 (Mild): \u0026lt;25% of total area; 2 (Moderate): 25%-50% of total area; 3 (Severe): \u0026gt;50% of total area.\u003c/p\u003e \u003cp\u003eInterstitial Inflammation Score: 0: No inflammation; 1 (Mild): Inflammation only in areas of IFTA; 2 (Severe): Inflammation present in areas without IFTA.\u003c/p\u003e \u003cp\u003eArteriolar Lesion Score: 0: None seen; 1 (Mild): Hyalinosis present in at least one arteriole; 2 (Severe): Hyalinosis present in more than two arterioles.\u003c/p\u003e \u003cp\u003eArteriosclerosis Score: 0: No intimal thickening; 1 (Mild): Intimal thickness less than medial thickness; 2 (Severe): Intimal thickness greater than medial thickness.\u003c/p\u003e \u003cp\u003eThe presence of other lesions was also evaluated, as appropriate, such as extracapillary hypercellularit, periglomerularr lesions, etc.\u003c/p\u003e \u003cp\u003eAll pathological slides were independently scored by three renal pathologists, and the average score was calculated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.5 Ets-1 and α-SMA Immunohistochemical and Immunofluorescence Staining\u003c/h2\u003e \u003cp\u003eEts-1 and α-SMA Immunohistochemical Staining: Renal tissue sections were prepared as described for renal pathology staining. 3 \u0026micro;m sections were baked at 60\u0026deg;C for 2 hours, deparaffinized, and rehydrated. Antigen retrieval was performed using citrate buffer (pH 6.0) at 95\u0026deg;C for 20 minutes, followed by natural cooling. Endogenous peroxidase activity was blocked with 3% hydrogen peroxide at room temperature for 25 minutes. Sections were blocked with 5% normal goat serum for 30 minutes. Primary antibodies, rabbit anti-human Ets-1 (1:200, ab220361, Abcam, Cambridge, UK) and rabbit anti-human α-SMA (1:200, 14395-1-AP, Proteintech, Wuhan, China), were applied and incubated overnight at 4\u0026deg;C. Subsequently, Alexa Fluor 488-conjugated goat anti-rabbit IgG (H\u0026thinsp;+\u0026thinsp;L) (ab150077, Abcam) was applied and incubated at 37\u0026deg;C for 1 hour. DAB was used for chromogenic development, followed by hematoxylin counterstaining and mounting with neutral balsam. All stained areas of interest (AOIs) in glomeruli, tubulointerstitium, and vessels on the sections were photographed. The integrated optical density (IOD) of these regions was measured using Image-Pro Plus 7.0 (Media Cybernetics, Inc., Silver Spring, MD, USA). The area of the selected effective statistical region was measured, and the mean density (IOD/area) was calculated. The mean density was used as the expression level of Ets-1 and α-SMA for statistical analysis.\u003c/p\u003e \u003cp\u003eEts-1 and α-SMA Immunofluorescence Staining: Section preparation was as described previously. Sections were placed in EDTA (pH 8.0) antigen retrieval buffer and heated (95\u0026deg;C) at 705-800W for 10 minutes, then cooled at room temperature for 30 minutes. Washed in 0.01 mol/L PBS for 10 minutes. Sections were incubated with 0.4% pepsin (pH 2.0) in a humidified chamber at 37\u0026deg;C for 10 minutes, followed by washing. Sections were blocked with irrelevant animal serum blocking solution and incubated at room temperature for 20 minutes. Primary antibodies, rabbit anti-human Ets-1 (ab220361, Abcam, Cambridge, UK) 1:200 and rabbit anti-human α-SMA (1:200, 14395-1-AP, Proteintech, Wuhan, China), were applied and incubated overnight at 4\u0026deg;C. Elab Fluor\u0026reg; 594-conjugated goat anti-rabbit IgG (H\u0026thinsp;+\u0026thinsp;L) (EAB-1060, Elabscience Biotechnology) was applied and incubated at room temperature for 30\u0026ndash;60 minutes. Sections were mounted with glycerol and observed under a fluorescence microscope for imaging. This was used for the localization of Ets-1 and α-SMA.\u003c/p\u003e \u003cp\u003eImmunohistochemical staining analysis was performed by three renal pathologists. Ets-1 immunohistochemical and immunofluorescence staining were performed on the renal biopsy tissues.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using IBM SPSS Statistics version 26 software. All hypothesis tests were two-sided, with the significance level set at α\u0026thinsp;=\u0026thinsp;0.05. Continuous variables conforming to a normal distribution are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD), and comparisons between groups were performed using the independent samples t-test. Variables not conforming to a normal distribution are presented as median (interquartile range) (Median (IQR)), and comparisons between groups were performed using the Mann-Whitney U test. Categorical variables are presented as frequency (percentage) (n (%)), and comparisons between groups were performed using the Chi-square test (χ\u0026sup2;); if the proportion of cells with an expected count\u0026thinsp;\u0026lt;\u0026thinsp;5 was \u0026gt;\u0026thinsp;20% or if any cell had an expected count\u0026thinsp;\u0026lt;\u0026thinsp;1, Fisher's exact test was used. Bivariate correlation analysis was performed using Spearman's rank correlation analysis.\u003c/p\u003e \u003cp\u003eBefore constructing multivariate regression models, all candidate independent variables were assessed for multicollinearity by calculating the variance inflation factor (VIF). A VIF\u0026thinsp;\u0026gt;\u0026thinsp;10 was used as the criterion for significant collinearity. If collinearity was present, highly correlated variables were either excluded or combined using methods like principal component analysis to ensure that the final model had all VIFs\u0026thinsp;\u0026lt;\u0026thinsp;5. Binary logistic regression models were used to analyze the independent association between variables and disease progression. Based on the predicted probabilities from the final logistic regression model, a receiver operating characteristic (ROC) curve was plotted, and the area under the curve (AUC) with its 95% confidence interval (CI) was calculated. The optimal probability cut-off value was determined by maximizing the Youden index, and the sensitivity and specificity at this cut-off value are reported.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Characteristics of the DKD Patient Cohort and Three-Year Follow-Up Outcomes\u003c/h2\u003e \u003cp\u003eThe study enrolled 115 patients diagnosed with DKD by renal biopsy between December 2019 and February 2022 from five medical centers in Gansu Province. Among them, 73% were male and 27% were female. The majority were of Han ethnicity (89%), while the remaining 11% included Hui, Tibetan, and other ethnic groups. The median age was 50 years (range: 46\u0026ndash;58 years), and the median body mass index (BMI) was 25.66 kg/m\u0026sup2;.\u003c/p\u003e \u003cp\u003eA 3-year follow-up was conducted from the date of biopsy. Based on predefined disease progression criteria, 57.4% (66/115) of the patients, who had an eGFR\u0026thinsp;\u0026le;\u0026thinsp;60 ml/min at the end of the follow-up, were classified into the progress group (PG). The remaining 42.6% (49/115), with an eGFR\u0026thinsp;\u0026gt;\u0026thinsp;60 ml/min, were classified into the relatively stable group (RSG). No significant differences were observed between the two groups in terms of sex (p\u0026thinsp;=\u0026thinsp;0.447), ethnicity (p\u0026thinsp;=\u0026thinsp;0.748), age (p\u0026thinsp;=\u0026thinsp;0.412), BMI (p\u0026thinsp;=\u0026thinsp;0.302), or duration of diabetes (p\u0026thinsp;=\u0026thinsp;0.525). (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of a Cohort of Diabetes kidney disease Patients and Three-Year Follow-up Outcomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eFollow-up endpoint\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall n\u0026thinsp;=\u0026thinsp;115\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProgressive n\u0026thinsp;=\u0026thinsp;66\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRelatively Stable n\u0026thinsp;=\u0026thinsp;49\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOverall n\u0026thinsp;=\u0026thinsp;115\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eProgressive n\u0026thinsp;=\u0026thinsp;66\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRelatively Stable n\u0026thinsp;=\u0026thinsp;49\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eG1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eG2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(9.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8(16.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7(6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7(14.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eG3a\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66(57.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34(51.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32(65.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48(41.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10(15.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38(77.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eG3b\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38(33.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31(46.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(14.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44(38.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40(60.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4(8.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eG4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16(13.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16(24.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eG5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Patients in the Disease Progression Group Showed More Severe Baseline Clinical Indicators and Vascular Complications\u003c/h2\u003e \u003cp\u003eComparison of baseline characteristics revealed that although there were no significant differences in age, sex, or BMI between the progression group and the stable group, the progression group exhibited more significant disease activity. This included higher blood pressure levels (SBP, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; DBP, p\u0026thinsp;=\u0026thinsp;0.007), more severe renal injury (UAER, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; 24-UPE, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and poorer renal function indicators (Scr, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; SUA, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); lower eGFR:(p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, the progression group had more severe dyslipidemia, with higher total cholesterol (p\u0026thinsp;=\u0026thinsp;0.012), LDL ( p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and VLDL ( p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while HDL was significantly lower ( p\u0026thinsp;=\u0026thinsp;0.018). The inflammatory state was also more prominent in the progression group, with C-reactive protein levels significantly higher than in the stable group ( p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, the progression group had a higher proportion of hypertension (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a greater prevalence of carotid plaques (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of baseline clinical parameters for patients in the disease progression group and the relatively stable group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall n\u0026thinsp;=\u0026thinsp;115\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProgressive n\u0026thinsp;=\u0026thinsp;66\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRelatively Stable n\u0026thinsp;=\u0026thinsp;49\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.447\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84(73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50(76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34(69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31(27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15(31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHan Chinese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101(89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57(88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44(90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Han Chinese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13(11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.00(46.00,58.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.00(46.75,59.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.00(45.50,55.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI(kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.66(23.10,27.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.28(21.90,28.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.69(23.47,27.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP(mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140.00(133.00,150.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144.50(136.00,159.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135.00(127.50,140.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89.00 (80.00, 94.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.50(82.00,98.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.00(77.00,92.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes duration (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.00(2.00,11.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.50(2.75,13.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.00(2.00,10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb(g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e134.00(121.00,148.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131.50(120.00,138.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e141.00(132.50,151.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCR (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73.00(67.00,88.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.00(76.00,94.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.00(56.15,78.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR(mL/min/1.73m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89.00(79.05,107.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.57(66.31,102.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97.70(94.25,109.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUA (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e373.00(309.00,435.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e398.00(334.50,451.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e359.00(285.00,400.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB(g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.00(37.00,39.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.35(34.58,40.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.40(36.40,39.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLB(g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.30(32.10,35.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.40 (31.48,35.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.60 (33.00,35.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHOL(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.89(4.09,5.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.15(4.20,6.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.63(3.67,5.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.85(1.42,2.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.88(1.41,3.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.80(1.43,2.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.442\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.14(1.46,2.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.57(1.88,3.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.57(1.34,2.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVLDL(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.12(1.15,3.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.08(2.03,3.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.25(1.05,1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.20(0.99,1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.10(0.90,1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.26(1.08,1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP(mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.00(1.41,10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.35(5.58,12.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.49(0.71,2.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUAER(mg/24h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e599.70(508.30,712.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e682.40(596.35,851.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e505.80(484.30,61.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24hUPE(g/24h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.60(0.44,1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.25(0.54,2.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48(0.38,0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension comorbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68(59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47(71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21(43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47(41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19(29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28(57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarotid plaques\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55(48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50(76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60(52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44(90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003en (%); Median (Q1, Q3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e2\u003c/sup\u003ePearson's Chi-squared test; Fisher's exact test; Mann-Whitney U test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Pathological Characteristics of Renal Tissue in the Relatively Stable and Progressive Groups\u003c/h2\u003e \u003cp\u003eConsistent with clinical indicators, renal pathological damage was more severe in the progression group. The glomerular lesion score, interstitial lesion score, vascular score, and total renal pathology score were all significantly higher in the progression group compared to the stable group (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePathological characteristics of renal tissue in patients with relatively stable and progressive groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall n\u0026thinsp;=\u0026thinsp;115\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProgressive n\u0026thinsp;=\u0026thinsp;66\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRelatively Stable n\u0026thinsp;=\u0026thinsp;49\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Renal Pathology Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.00(3.00,11.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.00(6.75,11.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.00(2.50,4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlomerular Injury Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.00(2.00,4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.00(2.00,6.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.00(1.00,3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTubulointerstitial Injury Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.00(1.00,4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.00(2.00,4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00(0.00,1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVascular Injury Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00(0.00,3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00(1.00,3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00(0.00,0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003eMedian (Q1, Q3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e2\u003c/sup\u003eMann-Whitney U test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Ets-1 is Specifically Highly Expressed in Renal Tissue of Diabetic Kidney Disease\u003c/h2\u003e \u003cp\u003eImmunohistochemical and immunofluorescence analyses revealed low Ets-1 expression in normal glomeruli, tubules, and vasculature. In contrast, renal tissues from patients with DKD \u0026mdash; including tubular, glomerular, and vascular regions \u0026mdash; exhibited significantly elevated Ets-1 expression. Concurrently, α-SMA, a key marker of fibrosis, also showed a synchronized upregulation (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting a potential role of Ets-1 in the renal fibrotic process.\u003c/p\u003e \u003cp\u003eComparative analysis between the relatively stable and progression groups demonstrated markedly higher Ets-1 expression in renal tissues and vasculature of the progression group. Similarly, α-SMA expression was significantly elevated in these patients, further supporting the association between Ets-1 and disease progression. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEts-1 and α-SMA expression in renal tissue of DKD patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic(Mean density)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase Group (n\u0026thinsp;=\u0026thinsp;1151\u003csup\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl Group (n\u0026thinsp;=\u0026thinsp;151\u003csup\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEts-1 Expression in Renal Tissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e281.39(201.45,341.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.40(69.40,83.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEts-1 Expression in Renal Vasculature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.70(69.30,104.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.90(29.70,48.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eα-SMA Expression in Renal Tissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e260.60(203.40,317.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.60(89.70,126.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eα-SMA Expression in Vascular Walls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81.90(68.90,101.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.90(31.90,49.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003eMedian (Q1, Q3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e2\u003c/sup\u003eMann-Whitney U test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDue to the skewed distribution of the data, we used the median (interquartile range) for statistical description and the Wilcoxon rank-sum test for intergroup comparisons. The average optical density of total Ets-1 in Diabetes kidney disease renal tissues was significantly upregulated. See Figs.\u0026nbsp;1 and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Among clinical parameters, significant differences were observed between the progression group and the stable group in hypertension, hyperlipidemia, intimal thickening, and plaque formation. Therefore, we particularly noted the levels of vascular Ets-1 average optical density. The results demonstrated that vascular Ets-1 expression in Diabetes kidney disease renal tissues was significantly upregulated, and vascular α-SMA expression also showed an increasing trend (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eFigure 1. The Expression of Ets-1 and ɑ-SMA in DKD renal tissue\u003c/p\u003e \u003cp\u003eCtrl: Renal tissue adjacent to the tumor; D: DKD patients; G: Progress Group patients; R: Relatively stable group patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Disease Progression is Associated with More Severe Renal Pathological Damage and High Ets-1 Expression\u003c/h2\u003e \u003cp\u003ePathological evaluation of glomerular, tubulointerstitial, and vascular lesions confirmed that patients in the progression group exhibited more severe damage in glomerular, tubulointerstitial, and vascular compartments, with significantly higher total pathology scores (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Consistent with these findings, expression levels of Ets-1 and its potential downstream factor α-SMA were markedly upregulated in both renal tissue and vasculature of the progression group (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).(Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Renal Ets-1 Expression Between Relatively Stable and Progressive Groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic (Mean density)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall n\u0026thinsp;=\u0026thinsp;115\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProgressive n\u0026thinsp;=\u0026thinsp;66\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRelatively Stable n\u0026thinsp;=\u0026thinsp;49\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEts-1 Expression in Renal Tissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e281.39\u003c/p\u003e \u003cp\u003e(201.45,341.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e321.82\u003c/p\u003e \u003cp\u003e(283.18,511.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e206.76\u003c/p\u003e \u003cp\u003e(168.19,246.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEts-1 Expression in Renal Vasculature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.70\u003c/p\u003e \u003cp\u003e(69.30,104.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.20\u003c/p\u003e \u003cp\u003e(85.20,148.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.50\u003c/p\u003e \u003cp\u003e(58.65,80.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eα-SMA Expression in Renal Tissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e260.60\u003c/p\u003e \u003cp\u003e(203.40,317.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e310.60\u003c/p\u003e \u003cp\u003e(257.88,420.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e210.50\u003c/p\u003e \u003cp\u003e(170.15,253.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eα-SMA Expression in Vascular Walls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81.90\u003c/p\u003e \u003cp\u003e(68.90,101.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.20\u003c/p\u003e \u003cp\u003e(78.55,120.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.30\u003c/p\u003e \u003cp\u003e(57.85,79.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003eMedian (Q1, Q3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e2\u003c/sup\u003eMann-Whitney U test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eExamples of patients in the progressive and relatively stable groups:\u003c/p\u003e \u003cp\u003eA PG patient, male, 56 years old, during renal biopsy, blood pressure: 160/96mmHg, SCR; 102umol/L,eGFR: 62ml/min, After a 3-year follow-up, blood pressure was controlled, with a creatinine level of 193umol/L and eGFR of 46ml/min. Pathological examination showed that most glomerular capillary loops were still open, and there was moderate to severe proliferation of the mesangial matrix; thickening of several glomerular capsule walls, extracapsular fibrosis, thickening of glomerular basement membrane, multifocal atrophy (atrophy area 10\u0026ndash;15%), and severe hyalinization of several small artery walls. Thickening of the walls of individual small arteries.\u003c/p\u003e \u003cp\u003eA RSG patient, 54 years old, during renal biopsy, blood pressure: 134/80 mmHg and SCR; 73 umol/L, eGFR: 87 ml/min, After a 3-year follow-up, blood pressure was controlled, with a creatinine level of 102 umol/L and eGFR of 70 ml/min. Pathological examination showed that the glomerular capillary loops were still open, and there was focal segmental proliferation in the mesangial area; Vacuolar and granular degeneration of renal tubular epithelial cells, focal atrophy of renal interstitium (atrophy area of about 5\u0026ndash;10%), and severe hyalinization of several small artery walls.\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.6 Expression of Ets-1 in Renal Tissue Shows Strong Positive Correlation with Renal Injury and Fibrosis Indicators\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSpearman correlation analysis revealed that the expression intensity of Ets-1 in renal tissue was significantly positively correlated with multiple clinical renal injury indicators\u0026mdash;such as proteinuria, serum creatinine, and blood pressure\u0026mdash;as well as systemic inflammatory markers (CRP) (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while exhibiting a negative correlation with eGFR.\u003c/p\u003e \u003cp\u003eMore importantly, Ets-1 demonstrated strong positive correlations with all renal pathological scores\u0026mdash;including glomerular, interstitial, and vascular lesion scores, as well as total pathology score\u0026mdash;and with the expression of α-SMA (all r\u0026thinsp;\u0026gt;\u0026thinsp;0.7, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). (Tables\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Renal ETS-1 Expression Between Relatively Stable and Progressive Groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.359**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.336**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eALB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eeGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.213*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.338**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCHOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.201*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.672**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.419**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUAER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.537**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.451**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.322**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24h UTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.476**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.608**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHypertension comorbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.285**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.241**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCarotid plaques\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.696**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e* At the 0.05 level (double tailed), the correlation is significant; **At the 0.01 level (double tailed), the correlation is significant\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation Analysis of Renal Ets-1 Expression and Renal Pathological Scores\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Renal Pathology Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.762**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlomerular Injury Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.841**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTubulointerstitial Injury Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.806**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArteriolar Injury Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.849**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eα-SMA Expression in Renal Tissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.786**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEts-1 Expression in Renal Vasculature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.850**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eα-SMA Expression in Vascular Walls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.711**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e* At the 0.05 level (double tailed), the correlation is significant; **At the 0.01 level (double tailed), the correlation is significant.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Multicollinearity Diagnostics of Clinical Indicators\u003c/h2\u003e \u003cp\u003eMulticollinearity was assessed using the variance inflation factor (VIF) to evaluate the degree of collinearity among variables. Severe multicollinearity may lead to inflated variance of regression coefficients. Independent variables with a tolerance between 0.1 and 1 and VIF\u0026thinsp;\u0026lt;\u0026thinsp;5 were retained. Under these conditions, no significant evidence of multicollinearity was detected among Ets-1, duration of (Type 2 Diabetes Mellitus) T2DM, age, BMI, SBP, DBP, UAER, LDL, TG, TC, HDL, and CRP.\u003c/p\u003e \u003cp\u003eHowever, severe multicollinearity (VIF\u0026thinsp;\u0026gt;\u0026thinsp;10) was observed among renal histopathological scores, Scr, eGFR, mean optical density of α-SMA, mean optical density of renal Ets-1, and mean optical density of vascular α-SMA (12.211). This likely indicates that these variables collectively reflect a core pathophysiological process\u0026mdash;presumably renal fibrosis or vascular injury. To mitigate multicollinearity in the logistic regression model, the following variables were excluded: renal histopathological score, vascular lesion score, tubulointerstitial lesion score, glomerular lesion score, eGFR, and mean optical density of vascular α-SMA. (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate ordinal logistic regression analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVIF值\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.374\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eeGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.797\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.888\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCHOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.337\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.472\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.295\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.923\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.389\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUAER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.381\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension comorbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24h UTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.531\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarotid plaques\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEts-1 Expression in Renal Tissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.426\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Renal Pathology Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e363.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEts-1 Expression in Renal Vasculature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.568\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlomerular Injury Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eα-SMA Expression in Renal Tissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.918\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTubulointerstitial Injury Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eα-SMA Expression in Vascular Walls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArteriolar Hyalinosis Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Ets-1 is an Independent Risk Factor for Predicting the Progression of Diabetic Kidney Disease\u003c/h2\u003e \u003cp\u003eAfter adjusting for the effects of multicollinearity, binary logistic regression analysis was performed. Using disease progression (progress\u0026thinsp;=\u0026thinsp;1, stable\u0026thinsp;=\u0026thinsp;0) as the dependent variable and statistically significant variables from the univariate analysis as independent variables, a binary logistic regression model was constructed. Variable selection was conducted using the backward stepwise method. The analysis confirmed that renal Ets-1 expression level, UAER, 24hUPE, and Scr were independent risk factors for predicting DKD progression (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). ROC curve analysis further demonstrated that Ets-1 had high predictive efficacy for disease progression (AUC\u0026thinsp;=\u0026thinsp;0.875), with a value comparable to the traditional UAER indicator (AUC\u0026thinsp;=\u0026thinsp;0.874). (Tables\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, \u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of Independent Predictors for Disease Progression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWald Z\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e95% CI for OR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLower limit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUpper Limit\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUAER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24hUPE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e102.127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEts-1 Expression in Renal Tissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.983\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.483\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEvaluation of the predictive efficacy of Ets-1 expression level in renal tissue for disease progression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSensitivity(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecificity(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYouden's Index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUAER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.811\u0026ndash;0.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.615\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEts-1 Expression in Renal Tissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.81\u0026ndash;0.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study conducted a 3-year follow-up of 115 patients with biopsy-confirmed DKD from four centers. Based on predefined disease progression criteria, 57.4% (66/115) of the patients were classified into the progression group (PG) at the end of follow-up, while 42.6% (49/115) were classified as relatively stable (RSG). Analysis of baseline characteristics revealed that patients in the PG exhibited more marked systemic disease activity, including higher blood pressure levels, poorer renal function indicators (elevated Scr and UAER, lower eGFR), more severe dyslipidemia and inflammatory status, as well as a higher prevalence of hypertension and carotid plaque.\u003c/p\u003e \u003cp\u003eRenal pathological assessment demonstrated significantly elevated Ets-1 expression in the renal tissues of DKD patients\u0026mdash;including glomerular, tubulointerstitial, and vascular compartments\u0026mdash;compared to normal renal tissue. Concurrently, α-SMA, a marker of fibrosis, also showed a synchronous upregulation trend. Spearman correlation analysis indicated that the intensity of renal Ets-1 expression was significantly positively correlated with multiple clinical indicators of kidney injury (such as proteinuria, serum creatinine, and blood pressure) and systemic inflammation (CRP), while it was negatively correlated with eGFR. More importantly, Ets-1 exhibited strong positive correlations with all renal pathological scores (including glomerular, interstitial, and vascular lesion scores, as well as the total score) and with α-SMA expression. Our results suggest that Ets-may 1 be closely associated with the progression of diabetic glomerular and interstitial fibrosis, as well as vascular injury.\u003c/p\u003e \u003cp\u003eEts-1 belongs to the Ets transcription factor family, which shares a conserved DNA-binding domain that specifically recognizes the core GGA(A/T) DNA sequence. It plays key roles in cell differentiation, proliferation, and apoptosis, and regulates various biological processes such as angiogenesis and development. Ets-1 is essential for normal kidney development and the maintenance of glomerular integrity [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Studies have shown that advanced chronic kidney disease (CKD) is often characterized by abnormal accumulation of extracellular matrix within the glomeruli. Matrix metalloproteinases (MMPs) play a critical role in ECM remodeling in various glomerular diseases. As a transcription factor, Ets-1 regulates the expression of multiple matrix proteases, including MMP-1, MMP-3, and MMP-9, and may influence ECM deposition by modulating the MMP-2/TIMP-2 system balance[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Ets-1 also contributes to renal fibrosis by activating downstream targets such as plasminogen activator inhibitor-1 (PAI-1) and connective tissue growth factor (CTGF), thereby regulating angiotensin II (Ang II)-induced fibroblast activation rat mesangial cells, Ang II induces Ets-1 expression, which directly activates the fibronectin promoter and promotes fibronectin production. Conversely, Ets-1 knockdown significantly reduces fibronectin expression in mesangial cells[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Ets-1 is highly expressed in the kidney across various renal injury models [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In Ets-1⁻/⁻ mice, Ang II infusion led to markedly reduced ROS (Reactive Oxygen Species, ROS) production in the thoracic aorta. siRNA-mediated knockdown of Ets-1 effectively suppressed ROS generation and p47phox expression. Since p47phox is a major source of ROS in the kidney and vascular system and promotes renal inflammation, a positive feedback relationship appears to exist between Ets-1 and ROS[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In Dahl salt-sensitive (SS) rats, activation of the renin\u0026ndash;angiotensin system increases Ets-1 expression and upregulates fibronectin, contributing to the development of end-organ injury in this model. Blocking Ets-1 alleviates the severity of renal damage in these animals[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These findings collectively highlight the central role of Ets-1 in renal injury and tissue and vascular remodeling.\u003c/p\u003e \u003cp\u003eIn any form of CKD, dysregulation of extracellular matrix (ECM) remodeling, tubular epithelial-mesenchymal transition, activation of mesangial cells and fibroblasts, inflammatory cell infiltration, and apoptosis are critical processes driving disease progression[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Diabetes kidney disease is characterized by glomerulosclerosis and tubulointerstitial fibrosis. Our renal pathological assessment confirmed that patients in the progression group exhibited more severe glomerular, tubulointerstitial, and vascular lesions, along with significantly higher total pathology scores.\u003c/p\u003e \u003cp\u003eCompared with normal renal tissues, Ets-1 expression was significantly elevated in the renal tissues of DKD patients\u0026mdash;localized to glomerular, tubulointerstitial, and vascular compartments. More importantly, Ets-1 demonstrated strong positive correlations with all renal pathological scores, including those for glomerular, interstitial, and vascular lesions, as well as the total score. Concurrently, α-SMA\u0026mdash;a marker of epithelial-mesenchymal transition (EMT)\u0026mdash;also showed a synchronous upregulation.\u003c/p\u003e \u003cp\u003eThese findings suggest that Ets-may 1 promote the progression of Diabetes kidney disease by initiating the expression of fibrotic and inflammatory genes, facilitating the transdifferentiation of mesangial cells and tubular epithelial cells, and thereby accelerating glomerular and interstitial fibrosis. We thus propose that under hyperglycemic conditions, sustained activation of Ets-1 is closely associated with the progression of diabetic kidney disease.\u003c/p\u003e \u003cp\u003eIn our study, the progression group exhibited a higher incidence of hypertension, elevated blood pressure, dyslipidemia, and plaque formation. Therefore, we paid particular attention to the vascular expression of Ets-1. Results revealed significantly upregulated Ets-1 expression in the vascular walls of renal tissues\u0026mdash;including interstitial vessels and glomerular capillary walls\u0026mdash;in the progression group. Expression of its downstream factor α-SMA was also markedly increased. These results indicate that Ets-may 1 play a critical role in diabetic renal vascular pathology.\u003c/p\u003e \u003cp\u003eOwing to its broad expression profile and potent transcriptional regulatory capacity, Ets-1 is instrumental in modulating cell proliferation, differentiation, apoptosis, and angiogenesis. Activation of the renin-angiotensin system (RAS) is a key mechanism in the pathogenesis of diabetes kidney disease. Studies have shown that when mouse thoracic aortas are systemically perfused with angiotensin II, Ets-1 is rapidly activated in vascular smooth muscle cells and endothelial cells. Ets1-knockout mice exhibited significantly attenuated arterial wall thickening, perivascular fibrosis, and cardiac hypertrophy [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In a rat carotid artery balloon injury model, Ets-1 mRNA expression increased rapidly (within 2 hours) in the damaged artery, with pronounced nuclear expression within 24 hours post-injury. Blocking Ets-1 effectively suppressed neointima formation in this model. Moreover, Ets-1 can promote inflammation by regulating proinflammatory cytokines and adhesion molecules such as IL-6, MCP-1, P-selectin, and E-selectin. Inhibiting Ets-1 reduced neointimal formation in mouse carotid arteries and downregulated expression of NOX2, NOX4, E-selectin, and MCP-1[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. These studies suggest that Ets-1 is involved not only in glomerulosclerosis and interstitial fibrosis, but also in diabetic vascular remodeling and plaque formation.\u003c/p\u003e \u003cp\u003eIn recent years, the role of inflammation in DKD progression has garnered increasing attention[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Our study found that C-reactive protein (CRP) levels were significantly higher in the progression group than in the relatively stable group, and Ets-1 expression was positively correlated with CRP levels, indicating a close relationship between Ets-1 and the inflammatory state in diabetes kidney disease. Numerous studies have demonstrated that Ets-1 directly initiates fibrosis-related genes\u0026mdash;promoting tissue fibrosis\u0026mdash;and regulates the expression of various genes, including growth factors, chemokines, and adhesion molecules. For example, high glucose upregulates protein tyrosine phosphatase 1B (PTP1B) expression via Ets-1, enhancing monocyte/endothelial adhesion and vascular cell adhesion molecule-1 (VCAM-1) expression in human umbilical vein endothelial cells, thereby contributing to endothelial inflammation and the pathogenesis of cardiovascular complications in diabetes[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In patients with inflammatory bowel disease, Ets-1 is highly expressed and promotes Th1-driven mucosal inflammation through cold-inducible RNA-binding protein (CIRBP)[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Additionally, Ets-1 initiates PDGF-A expression at both transcriptional and mRNA levels in primary rat aortic smooth muscle cells induced by (Platelet-Derived Growth Factor Subunit A) PDGF-A[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These findings suggest that Ets-1 contributes to disease progression by sustaining a chronic inflammatory state in DKD.\u003c/p\u003e \u003cp\u003eTo elucidate the clinical value of Ets-1 in the progression of DKD, we conducted a three-year follow-up of 115 patients with DKD to evaluate the predictive value of baseline Ets-1 expression. After adjusting for multicollinearity, binary logistic regression confirmed that renal Ets-1 expression level, UAER, 24UPE and Scr were independent risk factors for predicting DKD progression. ROC curve analysis further demonstrated that Ets-1 exhibited high predictive efficacy for disease progression (AUC\u0026thinsp;=\u0026thinsp;0.875), comparable to that of the traditional UAER marker (AUC\u0026thinsp;=\u0026thinsp;0.874). UAER is recognized as an early diagnostic indicator for DKD and a biomarker for evaluating disease progression and vascular endothelial injury[\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In both diabetic and non-diabetic kidney diseases, 24UPE is closely associated with disease progression. Persistent proteinuria is an independent risk factor for renal function deterioration and increased cardiovascular risk [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e],However, the predictive value of 24UPE and albumin excretion remains limited due to susceptibility to various confounding factors[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e],To date, renal pathology also lacks specific markers for predicting disease progression[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. As an early-response transcription factor, Ets-1 has been proposed as a biomarker for progression in certain diseases [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In esophageal cancer, overexpression of MMP-7 is significantly associated with poor prognosis, and Ets-1 mRNA levels correlate positively with MMP-7 expression, suggesting Ets-1 as a potential marker for advanced esophageal cancer[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Therefore, we propose that Ets-may 1 serve as a novel clinical biomarker for predicting DKD progression.\u003c/p\u003e \u003cp\u003eAlthough this study suggests the potential of Ets-1 in predicting DKD progression, several challenges remain for its use as a diagnostic biomarker. First, the study was retrospective in design, and the enrolled patients consisted predominantly of those with stage II and III pathological changes, with a lack of early-stage DKD patients. Statistical multicollinearity was also present. Second, due to the fact that renal biopsy is not a mandatory procedure for DKD diagnosis, the sample size was relatively small. Furthermore, although patients were enrolled from multiple centers, all were within the same province, which may introduce selection bias. These limitations may affect the generalizability of the results and hinder a comprehensive understanding of the systemic expression pattern of Ets-1 in DKD. Finally, no multicenter clinical validation was performed due to practical constraints.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003efull name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eDKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eDiabetic kidney disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eEts-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eE-Twenty-Six-1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026alpha;-SMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlpha-Smooth Muscle Actin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eESRD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eend-stage renal disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eeGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eestimated Glomerular Filtration Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eUAER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eurinary albumin excretion rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eRRT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003ereplacement therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003ePG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eprogress group\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eRSG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003erelatively stable group\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eC-reactive Protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eUAER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eUrinary Albumin Excretion Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e24-hour-UTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e24-hour Urinary Total Protein\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReceiver Operating Characteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea Under the (ROC) CurveArea Under the (ROC) Curve\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eRRT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003erenal replacement therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eECM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eextracellular matrix\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eMMP-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eMatrix Metallopeptidase 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eMMP-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eMatrix Metallopeptidase\u0026nbsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eMMP-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eMatrix Metallopeptidase\u0026nbsp;9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eTIMP-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eTIMP Metallopeptidase Inhibitor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003esalt-sensitive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eACEIs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eACE inhibitors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eSGLT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSodium-Glucose Cotransporter 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003ebody mass index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eFBG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003efasting blood glucose\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eHbA1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eglycated hemoglobin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eScr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eserum creatinine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eSUA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eserum uric acid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003etotal cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003etriglycerides\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eHDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003ehigh-density lipoprotein cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eLDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003elow-density lipoprotein cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eH\u0026amp;E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003ehematoxylin and eosin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003ePAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eperiodic acid-Schiff\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eKDIGO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKidney Disease: Improving Global Outcomes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eelectron microscopy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eIFTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eInterstitial Fibrosis and Tubular Atrophy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eAOIs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eareas of interest\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eIOD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eintegrated optical density\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eVIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003evariance inflation factor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eEDTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003ethylenediaminetetraacetic \u003cstrong\u003eA\u003c/strong\u003ecid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003ePBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003ehosphate-\u003cstrong\u003eB\u003c/strong\u003euffered \u003cstrong\u003eS\u003c/strong\u003ealine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003econfidence interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eT2DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType 2 Diabetes Mellitus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eCKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003echronic kidney disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eMMPs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eMatrix metalloproteinases\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003ePAI-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eplasminogen activator inhibitor-1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eCTGF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eonnective tissue growth factor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eAng II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eangiotensin II\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eEMT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eepithelial-mesenchymal transition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eRAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003erenin-angiotensin system\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eC-reactive protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003ePTP 1B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eprotein tyrosine phosphatase 1B\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eVCAM-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003evascular cell adhesion molecule-1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eCIRBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003ecold-inducible RNA-binding protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003ePDGF-A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlatelet-Derived Growth Factor Subunit A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this article. Further enquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis study received approval from the Ethics Committee of The Second Affiliated Hospital of Lanzhou University ( 2024A-1048 ), all methods were carried out in accordance with relevant guidelines and regulations. All participants provided written informed consent before their inclusion in this study.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research received support from various funding sources, the Gansu Provincial Administration of Traditional Chinese Medicine (GZKZ-2024-24), Gansu Provincial Science and International cooperation project of Technology Department (24YFWA012), And including the Talent Innovation and Entrepreneurship Project of Lanzhou City, Gansu Province (Grant No. 2021-RC-94), Additionally, our experiments were supported by the Clinical Medical Research Center of Gansu Province (Grants: 21JR7RA436).\u003c/p\u003e\n\u003cp\u003eDeclaration of generative AI use\u003c/p\u003e\n\u003cp\u003eNo AI-assisted tools were used in the writing of this work.\u003c/p\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this article. Further enquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003eConflict of interest\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eXCZ performed the experiments and manuscript writing. TXW, YKK, TXL,,and XQW are responsible for patient inclusion and follow-up management, while BRW, ZYL, XZM, ZRL analyzed data and prepared figures/tables. JQW, TXW, WL, YYW, YXN, YW, WJZ, WKZ, XXT, RRD, ZDT, YJL and FTT are responsible for reviewing renal pathology scores. FTT conceptualizes and designs this study, with FTT participating in manuscript review. All authors participated in the writing of the article and approved the final draft.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eWe thank Prof. Futian Tang (Department of Cardiovascular Disease, Lanzhou University Second Hospital No. 82, Cuiyingmen Lanzhou 730030 Gansu, China) for his helpful suggestions regarding the experiments and his excellent technical assistance.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMa RCW. Epidemiology of diabetes and diabetic complications in China. Diabetologia. 2018;61:1249\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupta S, Dominguez M, Golestaneh L. Diabetic Kidney Disease: An Update. Med Clin North Am. 2023;107:689\u0026ndash;705.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavidson KW, Barry MJ, Mangione CM, et al. Screening for Prediabetes and Type 2 Diabetes: US Preventive Services Task Force Recommendation Statement. JAMA. 2021;326:736\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerhane AM, Weil EJ, Knowler WC, Nelson RG, Hanson RL. Albuminuria and estimated glomerular filtration rate as predictors of diabetic end-stage renal disease and death. Clin J Am Soc Nephrol. 2011;6:2444\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCole JB, Florez JC. Genetics of diabetes mellitus and diabetes complications. Nat Rev Nephrol. 2020;16:377\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnders HJ, Huber TB, Isermann B, Schiffer M. CKD in diabetes: diabetic kidney disease versus nondiabetic kidney disease. Nat Rev Nephrol. 2018;14:361\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMizui M, Isaka Y, Takabatake Y, et al. Transcription factor Ets-1 is essential for mesangial matrix remodeling. Kidney Int. 2006;70:298\u0026ndash;305.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOkuducu AF, Zils U, Michaelis SA, Michaelides S, von Deimling A. Ets-1 is up-regulated together with its target gene products matrix metalloproteinase-2 and matrix metalloproteinase-9 in atypical and anaplastic meningiomas. Histopathology. 2006;48:836\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaffetseder U, Wernert N, Ostendorf T, et al. Mesangial cell expression of proto-oncogene Ets-1 during progression of mesangioproliferative glomerulonephritis. Kidney Int. 2004;66:622\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaito T, Razzaque MS, Nazneen A, et al. Renal expression of the Ets-1 proto-oncogene during progression of rat crescentic glomerulonephritis. J Am Soc Nephrol. 2000;11:2243\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng W, Chen B, Xing D, et al. Haploinsufficiency of the Transcription Factor Ets-1 Is Renoprotective in Dahl Salt-Sensitive Rats. J Am Soc Nephrol. 2017;28:3239\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuh SH, Kim SW. Dyslipidemia in Patients with Chronic Kidney Disease: An Updated Overview. Diabetes Metab J. 2023;47:612\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu S, Robyak K, Zhu Y, The CKD-EPI. 2021 Equation and Other Creatinine-Based Race-Independent eGFR Equations in Chronic Kidney Disease Diagnosis and Staging. J Appl Lab Med 2023;8:952\u0026ndash;961.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVivarelli M, Barratt J, Beck LH Jr. et al. The role of complement in kidney disease: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference. Kidney Int. 2024;106:369\u0026ndash;391.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTervaert TW, Mooyaart AL, Amann K, et al. Pathologic classification of diabetic nephropathy. J Am Soc Nephrol. 2010;21:556\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu DX, Liu XM, Su Y, Zhang XJ. Renal expression of proto-oncogene Ets-1 on matrix remodeling in experimental diabetic nephropathy. Acta Histochem. 2011;113:527\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang G, Xiang J, Yang X et al. Nuclear translocation of SIRT4 mediates deacetylation of U2AF2 to modulate renal fibrosis through alternative splicing-mediated upregulation of CCN2. Elife 2024;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGifford CC, Lian F, Tang J, et al. PAI-1 induction during kidney injury promotes fibrotic epithelial dysfunction via deregulation of klotho, p53, and TGF-β1-receptor signaling. Faseb j. 2021;35:e21725.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHua P, Feng W, Rezonzew G, Chumley P, Jaimes EA. The transcription factor ETS-1 regulates angiotensin II-stimulated fibronectin production in mesangial cells. Am J Physiol Ren Physiol. 2012;302:F1418\u0026ndash;1429.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShiu YT, Jaimes EA. Transcription Factor ETS-1 and Reactive Oxygen Species: Role in Vascular and Renal Injury. Antioxid (Basel) 2018;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang CC, Yeh JN, Hsu TW et al. Empagliflozin protected kidney function in CKD rat through suppressing hypoxic and fibrotic signalings mediated inflammation and EMT. Histol Histopathol 2025:18953.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHao G, Han Z, Meng Z, et al. Ets-1 upregulation mediates angiotensin II-related cardiac fibrosis. Int J Clin Exp Pathol. 2015;8:10216\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhan Y, Brown C, Maynard E, et al. Ets-1 is a critical regulator of Ang II-mediated vascular inflammation and remodeling. J Clin Invest. 2005;115:2508\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng W, Xing D, Hua P, et al. The transcription factor ETS-1 mediates proinflammatory responses and neointima formation in carotid artery endoluminal vascular injury. Hypertension. 2010;55:1381\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRayego-Mateos S, Rodrigues-Diez RR, Fernandez-Fernandez B, et al. Targeting inflammation to treat diabetic kidney disease: the road to 2030. Kidney Int. 2023;103:282\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang L, Liang J, Wang T, Meng F, Duan W. ETS proto-oncogene 1 modulates PTP1B expression to participate in high glucose-mediated endothelial inflammation. Acta Biochim Biophys Sin (Shanghai). 2022;54:565\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe Q, Gao H, Chang YL, et al. ETS-1 facilitates Th1 cell-mediated mucosal inflammation in inflammatory bowel diseases through upregulating CIRBP. J Autoimmun. 2022;132:102872.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSantiago FS, Khachigian LM. Ets-1 stimulates platelet-derived growth factor A-chain gene transcription and vascular smooth muscle cell growth via cooperative interactions with Sp1. Circ Res. 2004;95:479\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen C, Wang C, Hu C, et al. Normoalbuminuric diabetic kidney disease. Front Med. 2017;11:310\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Zhang H, Zhang Q, et al. Exenatide and Renal Outcomes in Patients with Type 2 Diabetes and Diabetic Kidney Disease. Am J Nephrol. 2020;51:806\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuilope LM, Ortiz A, Lucia A, et al. Prevention of cardiorenal damage: importance of albuminuria. Eur Heart J. 2023;44:1112\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWebster AC, Nagler EV, Morton RL, Masson P. Chronic Kidney Disease Lancet. 2017;389:1238\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLim CTS, Nordin NZ, Fadhlina NZ, et al. Rapid decline of renal function in patients with type 2 diabetes with heavy proteinuria: a report of three cases. BMC Nephrol. 2019;20:22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChandragiri S, Raju SB, Mandarapu SB, Goli R, Nimmagadda S, Uppin M. A Clinicopathological Study of 267 Patients with Diabetic Kidney Disease Based on the Renal Pathology Society \u0026ndash;\u0026thinsp;2010 Classification System. Indian J Nephrol. 2020;30:104\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMotalebzadeh J, Shabani S, Rezayati S, et al. Prognostic Value of FBXO39 and ETS-1 but not BMI-1 in Iranian Colorectal Cancer Patients. Asian Pac J Cancer Prev. 2018;19:1357\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePuzovic V, Brcic I, Ranogajec I, Jakic-Razumovic J. Prognostic values of ETS-1, MMP-2 and MMP-9 expression and co-expression in breast cancer patients. Neoplasma. 2014;61:439\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSasaki H, Yukiue H, Moiriyama S, et al. Clinical significance of matrix metalloproteinase-7 and Ets-1 gene expression in patients with lung cancer. J Surg Res. 2001;101:242\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Diabetic kidney disease, DKD, Progression risk, Ets-1, Prognosi","lastPublishedDoi":"10.21203/rs.3.rs-8378039/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8378039/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTo investigate the correlation between Ets-1 and the progression of diabetic kidney disease (DKD).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 115 patients with biopsy-proven DKD were followed for three years. Based on renal function after follow-up, they were categorized into a progress group (PG, 57.4%, 66/115) and a relatively stable group (RSG, 42.6%, 49/115). Ets-1 expression in renal tissue was analyzed, along with its associations with clinical, biochemical, and pathological parameters, and its predictive value for DKD progression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eEts-1 expression differed significantly between PG and RSG (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Its expression strongly correlated with clinical indicators of kidney injury (e.g., proteinuria, serum creatinine, blood pressure) and systemic inflammation (CRP) (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and inversely with estimated eGFR (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Importantly, Ets-1 showed strong positive correlations with all renal pathological scores (glomerular, tubulointerstitial, vascular lesions) and expression of the EMT marker α-SMA (all r\u0026thinsp;\u0026gt;\u0026thinsp;0.7, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Binary logistic regression confirmed that Ets-1 expression, UAER, 24-hour UTP, and serum creatinine were independent risk factors for DKD progression. ROC analysis demonstrated high predictive value of Ets-1 for DKD progression (AUC\u0026thinsp;=\u0026thinsp;0.875), comparable to that of UAER (AUC\u0026thinsp;=\u0026thinsp;0.874).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eEts-1 is closely associated with DKD progression and may serve as a potential predictor for disease advancement in clinical practice.\u003c/p\u003e","manuscriptTitle":"Correlation Between Ets-1 and the Progression of Diabetic Kidney Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-30 09:33:32","doi":"10.21203/rs.3.rs-8378039/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d8ec4c16-5e9c-4329-933f-cd1f9b266428","owner":[],"postedDate":"December 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-27T10:39:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-30 09:33:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8378039","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8378039","identity":"rs-8378039","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}