Characterization of Diabetic Kidney Disease in 235 Patients: Clinical and Pathological Insights with or without Concurrent Non-Diabetic Kidney Disease

Characterization of Diabetic Kidney Disease in 235 Patients: Clinical and Pathological Insights with or without Concurrent Non-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 Characterization of Diabetic Kidney Disease in 235 Patients: Clinical and Pathological Insights with or without Concurrent Non-Diabetic Kidney Disease Mengjie Jiang, Hongyu Chen, Jing Luo, Jinhan Chen, Li Gao, Qin Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4297672/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Jan, 2025 Read the published version in BMC Nephrology → Version 1 posted 10 You are reading this latest preprint version Abstract BACKGROUND This study aimed to explore the clinical and pathological features of patients with diabetic kidney disease (DKD), with and without non-diabetic kidney disease (NDKD), through a retrospective analysis. The objective was to provide clinical insights for accurate identification. METHODS A retrospective analysis of 235 patients admitted to the Department of Nephrology at Hangzhou Hospital of Traditional Chinese Medicine was conducted between July 2014 and December 2022. These patients underwent renal biopsy and received a pathology-based diagnosis of DKD. They were categorized into the DKD group (93 cases) and the DKD + NDKD group (142 cases). RESULTS In the DKD group, nodular diabetic glomerulosclerosis was the most prevalent, accounting for 63% of cases. In the DKD + NDKD group, the predominant pathological types were coupled with acute and chronic tubulointerstitial lesions, and IgA nephropathy, accounting for 40.14% and 35.21%, respectively. Clinical correlation analysis revealed associations between glomerular grading, tubulointerstitial lesions, renal arteriolar vitelliform lesions, renal vascular atherosclerosis, and clinical parameters such as 24-hour urine protein, hemoglobin, and urinary specific gravity. Multifactorial logistic regression analysis identified independent factors affecting DKD + NDKD, including body mass index, blood creatinine level, microscopic erythrocyte grade, urinary immunoglobulin G/creatinine ratio, and serum immunoglobulin A. CONCLUSION This study provides important insights into the pathological and clinical features of DKD and identifies independent factors associated with DKD + NDKD. Diabetes mellitus diabetic nephropathy nondiabetic nephropathy pathologic features clinical features Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction Diabetic kidney involvement primarily manifests as diabetic kidney disease (DKD), a leading cause of end-stage kidney disease (ESKD)[ 1 , 2 ]. However, some diabetic patients may solely present with diabetes mellitus or non-diabetic kidney disease (NDKD), or DKD combined with NDRD[ 3 , 4 ]. This study statistically analyzed patients with DKD and DKD + NDKD, investigating their pathological and clinical characteristics, and identifying the independent factors associated with DKD + NDKD to aid clinical differentiation. 2 Materials and methods 2.1 Study design and population This single-center retrospective investigation included 235 patients diagnosed with diabetic kidney disease who underwent renal biopsy at the Nephrology Department of Hangzhou Hospital of Traditional Chinese Medicine between July 2014 and December 2022. Inclusion criteria were: (1) clinical diagnosis of diabetes mellitus; (2) nephropathologic diagnosis of DKD or DKD + NDKD; (3) availability of complete clinical and pathological data. Exclusion criteria were: (1) pathology indicating NDKD; (2) incomplete clinical and pathological data; (3) patients with acute illnesses, immunological disorders, malignancies, or infections (Fig. 1) 2.2 Data collection General clinical data, laboratory examination results, and renal biopsy pathology data were collected from patients diagnosed with DKD who underwent renal pathology biopsy at Hangzhou Hospital of Traditional Chinese Medicine. The data were categorized into the DKD group and the DKD + NDKD group. DKD pathology grading criteria published in the American Journal of Kidney Diseases were used as a reference for grading[ 5 ]. Grade I represents simple glomerular basement membrane thickening, characterized by the absence or presence of mild specific changes under light microscopy. Electron microscopy reveals glomerular basement membrane thickening, exceeding 395 nm in women and 430 nm in men (age ≥ 9 years), with pathological changes not reaching grades II, III, or IV. Grade IIa represents mild tethered basement membrane widening, with mild widening observed in over 25% of the glomeruli and no pathological changes reaching grades III or IV. Grade IIb denotes severe thylakoid stromal widening, with over 25% of glomeruli exhibiting severe widening and pathological changes not extending to grades III or IV. Grade III manifests as nodular sclerosis, characterized by more than one Kimmelstiel–Wilson nodule (K-W nodules), with pathological changes not reaching grade IV. Grade IV indicates advanced diabetic glomerulosclerosis, with over 50% total glomerulosclerosis accompanied by concurrent grade I-III pathological changes. Renal biopsy pathology was evaluated by the same experienced renal pathologist, and based on the criteria, the pathological findings were classified as follows: early diabetic kidney injury in Grade I, diffuse diabetic glomerulosclerosis in Grades IIa and IIb, nodular diabetic glomerulosclerosis in Grade III, and advanced diabetic glomerulosclerosis in Grade IV. 2.3 Statistical analysis Statistical analysis was performed using SPSS 26.0 software. Measurement data following normal distribution were presented as‾ x ± s and compared between groups using the independent samples t-test. Non-normally distributed measurement data were presented as M (P 25 , P 75 ) and compared between groups using the Kruskal–Wallis rank-sum test. Counting data were expressed as the number of cases and percentage, and were compared between groups using the χ 2 test or Fisher's exact probability method. Statistical significance was set at P < 0.05. 3 Results 3.1 Clinical Data Among the 235 patients diagnosed with DKD, 93 were classified in the DKD alone group, while 142 were categorized in the DKD + NDKD group. The highest proportion of patients had a duration of diabetes < 5 years, accounting for 33.2%, followed by those with a duration of 10 to 15 years at 25.1%. Conversely, only 7.7% of patients had a history of diabetes equal to or greater than 20 years. The mean age of all patients was 54.22 ± 10.35 years, ranging from 29 to 76 years. The most common age groups were 50–59 and 60–69 years, representing 65% of the sample. Among the total sample, 182 individuals (77.44%) were men, while 53 individuals (22.55%) were women, resulting in a male-to-female ratio of 3.43:1. In comparing clinical data between the two groups, significant differences were observed in sex, body mass index (BMI), hemoglobin, urine osmolality, blood creatinine, microscopic erythrocyte grade, urinary immunoglobulin G (UIgG)/creatinine ratio, and serum IgA. The proportion of men and the proportion of microscopic erythrocyte grade in the DKD + NDKD group were higher than those in the DKD group. Moreover, BMI, hemoglobin, urine osmolality, blood creatinine, and serum IgA were higher in the DKD + NDKD group. Urinary IgG/creatinine ratio was higher in the DKD group compared to the DKD + NDKD group (Fig. 2 , Table 1 ). Table 1 Comparative Analysis of Clinical Characteristics Among Patient Cohorts DKD b DKD + NDKD b Ratio χ2/Z P Sex Male 65 117 64.30% 5.029 0.025* Female 28 25 47.20% History of diabetes(years) < 5 25 53 67.90% 4.846 0.303 < 10 22 31 58.50% < 15 24 35 59.30% < 20 15 12 44.40% ≥ 20 7 11 61.10% Hypertension No 3 11 78.60% 2.050 0.152 Yes 90 131 59.30% Non-diabetic Retinopathy No 84 118 58.40% 2.430 0.119 Yes 9 24 72.70% High blood fat disease No 36 58 61.70% 0.107 0.744 Yes 57 84 59.60% Metabolic acidosis No 85 117 57.90% 3.774 0.052 Yes 8 25 75.80% Microscopic erythrocyte grade a - 53 77 59.20% 13.040 0.023* +- 23 22 48.90% + 11 11 50.00% ++ 4 19 82.60% +++ 2 9 81.80% ++++ 0 4 100% Antinuclear Antibodies Abnormal 22 25 53.20% 1.286 0.257 Normal 71 117 62.20% Blood light chain KAP/LAM b Abnormal 59 89 60.10% 0.014 0.905 Normal 34 53 60.90% Age 54(47 ~ 61) 56(48 ~ 63) -1.132 0.258 BMI b 23.71 ± 2.86 25.27 ± 3.23 2.344 < 0.001* Average arterial blood pressure 106.31 ± 15.70 108.35 ± 1.34 0.1 0.337 Hemoglobin 116.12 ± 22.59 123.36 ± 22.06 0.013 0.016* Urine osmolality 457(376 ~ 574) 513(421.75 ~ 632) -2.112 0.035* Creatinine 114.7(75 ~ 165) 131.5(91 ~ 176) -2.108 0.035* Blood uric acid 390(327.5 ~ 458) 408(354 ~ 491) -1.827 0.068 Glycosylated hemoglobin 7.4(6.4 ~ 8.35) 7.1(6.3 ~ 7.6) -1.875 0.061 24-hour urine protein 3.02(1.48 ~ 5.02) 2.265(0.883 ~ 5.388) -1.095 0.274 UIgG/urine creatinine ratio b 0.213(0.073 ~ 0.650) 0.113(0.034 ~ 0.303) -2.869 0.004* Serum immunoglobulin G 1050(873.5 ~ 1290) 1020(793.25 ~ 1292.5) -0.999 0.318 Serum immunoglobulin A 233(182.5 ~ 302.5) 263.5(201 ~ 353.5) -2.361 0.018* Serum immunoglobulin M 83(61 ~ 114.5) 84(61.5 ~ 118) -0.005 0.996 Complement C3 103(92 ~ 114) 102(91 ~ 116) -0.006 0.995 Complement C4 26(22 ~ 31) 26(22 ~ 31) -0.437 0.662 Footnote: a. Microscopic erythrocytes: "-" denotes 30, "+++" denotes microscopic erythrocytes greater than three-quarters of the field of view, and "++++" signifies an entire field of view littered with erythrocytes. b. diabetic kidney disease(DKD), non-diabetic kidney disease(NDKD),Kappa(KAP), Lambda(LAM),body mass index(BMI),urinary immunoglobulin G(UIgG). c. The asterisk symbol (*) indicates statistical significance at P < 0.05. 3.2 Renal Pathology 3.2.1 Distribution of Pathological Types In the DKD group, the predominant pathological findings were nodular diabetic glomerulosclerosis, followed by diffuse diabetic glomerulosclerosis, early diabetic nephropathy, and advanced diabetic glomerulosclerosis. Within the DKD + NDKD group, the most prevalent pathological types were acute and chronic tubulointerstitial disease (ATID, CTID) and IgA nephropathy. Furthermore, concurrent cases of IgA nephropathy, CTID, and membranous nephropathy (MN), MN with ATID and CTID, and other renal injuries (such as monoclonal Ig secondary kidney damage, crescentic glomerulonephritis, hepatitis B virus-associated secondary nephritis, lupus nephritis, proliferative sclerosing glomerulonephritis, crystalline nephropathy, dry syndrome kidney injury, or obesity-associated kidney injury) were observed. Specific data are depicted in Figs. 3 and 4 . 3.2.2 Renal pathology grading and scoring As patients with early diabetic nephropathy in the sample did not yet exhibit significant glomerular, tubulointerstitial, and renal vascular lesions, pathological grading of diabetic nephropathy was not applicable. Excluding 65 patients with pathology suggestive of early diabetic nephropathy, the distribution of glomerular grading, tubulointerstitial lesions (interstitial fibrosis and tubular atrophy [IFTA] score, interstitial inflammation score), and renal vasculature (arteriolar hyalinosis, arteriosclerosis) among patients in the DKD group and the DKD + NDKD group is detailed in Table 2 . Table 2 Renal Pathology Grading and Scoring in Both Groups DKD DKD + NDKD F P Glomerular classification IIa 3(3.61%) 19(21.84%) 27.267 < 0.01 IIb 16(19.28%) 30(34.48%) IIb-III 11(13.25%) 14(16.09%) III 50(60.24%) 22(25.29%) Ⅳ 3(3.61%) 2(2.30%) IFTA 1 12(14.46%) 13(14.94%) 0.567 0.753 2 52(62.65%) 50(57.47%) 3 19(22.89%) 24(27.59%) interstitial inflammation 1 66(79.52%) 55(63.22%) 5.501 0.019 2 17(20.48%) 32(36.78%) arteriolar hyalinosis 1 15(18.07%) 19(21.84%) 0.377 0.539 2 68(81.93%) 68(78.16%) Arteriosclerosis 0 13(15.66%) 14(16.09%) 2.832 0.243 1 59(71.08%) 68(78.16%) 2 11(13.25%) 5(5.75%) 3.3 Clinicopathological Correlation The grading of glomerular lesions exhibited a significantly positive correlation with 24-hour urine protein quantification and a highly significant negative correlation with hemoglobin, urinary specific gravity, and osmolality. Tubulointerstitial lesions (IFTA score, interstitial inflammation score) demonstrated a significant positive correlation with blood creatinine and uric acid levels and a significant negative correlation with estimated glomerular filtration rate (GFR). Renal arteriolar hyalinosis displayed a significantly positive correlation with age and blood creatinine levels, and a substantially negative correlation with urine osmolality and GFR. Renal vascular atherosclerosis showed a significant positive correlation with age and a negative correlation with GFR (Fig. 5 ). 3.4 Independent Factors of DKD + NDKD The results of univariate logistic regression analysis indicated that sex, BMI, hemoglobin levels, urine osmolality, blood creatinine levels, microscopic erythrocyte grade, UIgG/urine creatinine ratio, and serum IgA levels were significant factors influencing DKD + NDKD ( P < 0.05). Multifactorial logistic regression analysis revealed that BMI (odds ratio [OR] = 1.193, 95% confidence interval [CI]: 1.072 ~ 1.327, P = 0.001), blood creatinine level (OR = 1.007, 95% CI 1.003 ~ 1.011, P = 0.001), microscopic erythrocyte grade (+++) (OR = 5.879, 95% CI 1.529 ~ 22.604, P = 0.01), UIgG/urine creatinine ratio (OR = 0.242, 95% CI 0.089 ~ 0.661, P = 0.006), and serum IgA (OR = 1.005, 95% CI 1.002 ~ 1.008, P = 0.003) were independently correlated with DKD + NDKD, as shown in Table 3 . The prediction was performed with a classification table utilizing a binary logistic regression model, achieving a prediction accuracy of 73.6%, as presented in Table 4 . Table 3 Logistic Regression Analysis of DKD + NDKD Factors Univariate logistic regression Multivariate logistic analysis χ2/Z P OR (95% CI) P Sex Male 5.029 0.025* 0.911(0.409 ~ 2.026) 0.818 Female BMI(kg/m 2 ) 2.344 < 0.001* 1.193(1.072 ~ 1.327) 0.001* Hemoglobin(g/L) 0.013 0.016* 1.015(0.997 ~ 1.034) 0.102 Urine osmolality(mOsm/kg) -2.112 0.035* 1.002(0.999 ~ 1.004) 0.202 Blood creatinine(umol/L) -2.108 0.035* 1.007(1.003 ~ 1.011) 0.001* Microscopic erythrocyte grade - 13.040 0.023* 0.071 +- 1469427539.261 0.999 + 1.111(0.505 ~ 2.448) 0.793 ++ 1.393(0.439 ~ 4.422) 0.574 +++ 5.879(1.529 ~ 22.604) 0.01* ++++ 5.863(0.937 ~ 36.687) 0.059 UIgG/Urine creatinine(mg/mgCr) -2.869 0.004* 0.242(0.089 ~ 0.661) 0.006* Serum immunoglobulin A(mg/dl) -2.361 0.018* 1.005(1.002 ~ 1.008) 0.003* Table 4 Predictions from the Binary Logistic Regression Model Prediction DKD + NDKD No Yes Percentage correct (%) DKD + NDKD No 55 38 59.1 Yes 24 118 83.1 Overall percentage 73.6 4 Discussion This study examined individuals who underwent renal biopsy at Hangzhou Hospital of Traditional Chinese Medicine between July 2014 and December 2022 and received a pathological diagnosis of DKD. By exploring the relationship between various pathological and clinical factors, comparing variances between the DKD and DKD + NDKD cohorts, and investigating independent factors associated with DKD + NDKD, it was aimed to identify patients with diabetes who could benefit from early renal biopsy in clinical practice. Such an approach could assist in developing personalized treatment strategies and enabling early detection and intervention. Nodular glomerulosclerosis has been consistently identified as the primary pathological change in DKD[ 6 ]. Epidemiological investigations have indicated that MN is the most common pathology in DKD + NDKD, followed by IgA nephropathy[ 7 ]. However, in the present study, ATID/CTID and IgA nephropathy emerged as the predominant pathological types in the DKD + NDKD group, diverging from previous findings. IgA nephropathy remains a prevalent subtype among patients with diabetes[8.9.10]. The present study results indicate that DKD combined with IgA nephropathy is one of the most common forms of DKD + NDKD pathology. IgA nephropathy and DKD can lead to proliferative glomerular lesions and even glomerulosclerosis due to connective tissue proliferation and basement membrane alterations within the glomeruli. While DKD was traditionally associated primarily with pathological glomerular changes[ 11 ], recent studies have highlighted a considerable correlation of tubulointerstitial damage extent with renal function progression and prognosis[ 12 , 13 ]. Tubulointerstitial lesions may manifest independently of glomerular lesions[ 14 , 15 , 16 ], with immunoinflammation[ 17 , 18 , 19 ] hypothesized to play a pivotal role in the onset and progression of renal tubular injury in DKD. Early disease stages frequently exhibit hypertrophic phenomena, characterized by increased renal tubular epithelial cell numbers and thickening of the tubular basement membrane, which are critical in triggering and accelerating renal tubular interstitial fibrosis[ 20 ]. Prolonged hyperglycemia, ischemia, and hypoxia lead to apoptosis, atrophy, and degeneration of renal tubular cells[ 21 ], along with inflammatory cell infiltration, increased inflammatory factors, interstitial fibrosis, interstitial arteriolar atherosclerosis, and small artery hyalinization, all influencing lesion development and progression[ 22 , 23 ].Correlation analyses have revealed a positive association between glomerular grading and 24-hour urine protein quantification, suggesting that glomerular structural damage may increase urinary protein excretion[ 24 ]. This phenomenon could stem from increased filtration membrane permeability and decreased tubular reabsorption. The significant negative correlation between glomerular grading and hemoglobin levels likely results from worsening renal function associated with increasing glomerular grading, leading to reduced renal erythropoietin (EPO) secretion and consequent exacerbation of anemia.[ 25 ] Therefore, a significant negative correlation exists between the progression of glomerular grading and the exacerbation of renal anemia. Glomerular grading demonstrated a significant negative correlation with urinary specific gravity and urine osmolality, indicating that more severe glomerular injury is associated with lower specific gravity and osmolality. This result is likely due to glomerular injury frequently leading to tubulointerstitial damage, which decreases tubular concentrating capacity and lowers urinary specific gravity and osmolality. Tubulointerstitial lesions exhibited a significant positive correlation with blood creatinine and a significant negative correlation with GFR, reflecting the close association between tubulointerstitial lesions and renal function. Damage to the tubulointerstitial stroma affects the filtration and excretory functions of the kidneys, resulting in a weakened ability to filter waste products, such as creatinine, leading to their accumulation in the body, elevated blood creatinine levels, and decreased renal function. The positive correlation between tubulointerstitial lesions and blood uric acid may be attributed to hyperuricemia causing vascular and tubulointerstitial damage by activating the renin-angiotensin system and immune system response[ 26 ]. Furthermore, tubulointerstitial lesions can hinder uric acid excretion, contributing to secondary hyperuricemia. Vitrification of renal arterioles was positively correlated with age and blood creatinine levels, and negatively correlated with urine osmolality and GFR. Physiological aging of blood vessels accelerates the vitrification of renal arterioles with increasing age. Diabetes-induced vitrification of renal arterioles reduces vascular elasticity, affecting renal blood supply and disrupting kidney filtration function, resulting in decreased GFR and impaired creatinine removal from the blood. The severity of renal arteriolar vitrification is negatively correlated with urine osmolality due to ischemic damage to glomeruli and tubules associated with the diseased arterioles. Consequently, the concentrating and reabsorbing functions of both distal and proximal tubules are impaired, resulting in decreased urine osmolality and increased nocturia. Renal vascular atherosclerosis is positively correlated with age and negatively correlated with GFR. Renal vascular sclerosis may be part of systemic arteriosclerosis, affecting multiple organ blood supply and function, including the glomeruli. Increased renal vascular atherosclerosis severity is more likely to cause renal artery narrowing, leading to renal ischemia, glomerular atrophy, fibrosis, and necrosis, thereby reducing GFR. Comparison of clinical and pathological data between the two groups revealed significant differences in glomerulopathy grading, tubulointerstitial lesions, microscopic erythrocyte grade, BMI, urine osmolality, blood creatinine, UIgG/urine creatinine, and serum IgA ( P < 0.05). Multifactorial logistic regression analysis identified BMI, blood creatinine level, microscopic erythrocyte grade, UIgG/urine creatinine, and serum IgA as independent correlates of DKD + NDKD. Specifically, BMI, blood creatinine level, microscopic erythrocyte grade (+++), and serum IgA were identified as risk factors for DKD + NDKD, while UIgG/urine creatinine was identified as a protective factor. Previous studies have confirmed the role of hematuria and blood creatinine in identifying DKD + NDKD, suggesting that NDKD accelerates the deterioration of renal function in patients with type 2 diabetes[ 27 , 28 ]. Previous studies have hinted at a higher BMI in patients with NDKD[ 6 ]. This study similarly suggests that patients with comorbid NDKD exhibit a higher BMI compared to those with DKD, potentially indicating a link to obesity-associated nephropathy. Elevated levels of IgA in humoral immunity frequently signify an abundance of IgA or its immune complexes, which, coupled with factors such as local inflammatory responses, increase the risk of IgA nephropathy. UIgG/urine creatinine, relatively unaffected by other factors, might offer insights into diagnosing DKD. Considering IgG’s sizeable molecular size, when the glomerular basement membrane’s function is impaired, increased basement membrane leads to more frequent excretion of urinary large-molecule proteins, suggesting a likelihood of DKD. These findings provide critical factors for identifying patients with DKD + NDKD, aiding in accurate prediction of the pathological type and individualized treatment planning. In our study, assessing the binary logistic regression model’s predictive performance through a classification table yielded a prediction accuracy of 73.6%. While this result demonstrates substantial efficacy in sample classification for DKD + NDKD prediction, additional metrics are necessary for comprehensive performance evaluation. Further validation efforts are imperative to ensure the model’s reliability and applicability. This study was constrained by single-site, single-sample sources, failing to encompass geographic, population, and healthcare system diversities. Its retrospective nature restricted analysis to baseline data, precluding insights into long-term prognosis across diverse pathology types. Moreover, the study overlooked factors such as genetic predispositions and lifestyle influences beyond the clinical and pathologic data examined. Future investigations should address these limitations by broadening geographic and sample source scopes, conducting multicenter studies for enhanced patient representation, and incorporating long-term prospective follow-up. These initiatives would facilitate the development of more accurate prediction models by monitoring disease progression and treatment responses over extended periods. Moreover, integrating genetic and lifestyle information would enable a comprehensive understanding of diabetic nephropathy mechanisms, informing improved clinical management and treatment strategies. 5 Conclusion In summary, nodular glomerulosclerosis emerges as the primary pathological hallmark of DKD; however, the coexistence of acute and chronic tubulointerstitial lesions alongside IgA nephropathy prevails in patients with DKD and NDKD. Moreover, diabetic nephropathy pathology shows associations with various clinical parameters. For instance, tubulointerstitial lesions correlate positively correlate with blood creatinine and uric acid levels, and negatively correlate with GFR, among others. Independent correlates of DKD + NDKD include BMI, blood creatinine level, microscopic erythrocyte grade, UIgG/urine creatinine ratio, and serum IgA, aiding in DKD differentiation from NDKD. Abbreviations DKD: Diabetic kidney disease NDKD: Non-diabetic kidney disease ESKD: End-stage kidney disease K-W nodules: Kimmelstiel–Wilson nodule BMI: Body mass index UIgG: Urinary immunoglobulin G KAP: Kappa LAM: Lambda ATID: Acute tubulointerstitial disease CTID: Chronic tubulointerstitial disease MN: Membranous nephropathy IFTA: Interstitial fibrosis and tubular atrophy GFR: Glomerular filtration rate MAP: Mean Arterial Pressure Hb: Hemoglobin USG: Urinary Specific Gravity Uosm: Urine Osmolality Cr: Blood Creatinine UA: Blood Uric Acid TG: Triglycerides GFR-EPI: Glomerular Filtration Rate 24hUP: 24-hour Urinary Protein GC: Glomerular classification TIN: Tubular Interstitial Inflammation HAG: Arteriolar hyalinosis AS: Atherosclerosis OR：Odds ratio EPO：Erythropoietin Declarations Data availability All data generated or analyzed during this study are included in this published article [and its supplementary information files]. Acknowledgements This study was financially supported by National Natural Science Foundation of China for Young Scholars (Grant No. 82205008); Medical Scientific Research Foundation of Zhejiang Province, China (Grant No. 2023RC242); Zhejiang Traditional Medicine and Technology Program, China (Grant No. 2023ZF137); Basic Public Welfare Research Program of Zhejiang Province(Grant No.LTGY23H270007). Funding This study was financially supported by National Natural Science Foundation of China for Young Scholars (Grant No. 82205008); Medical Scientific Research Foundation of Zhejiang Province, China (Grant No. 2023RC242); Zhejiang Traditional Medicine and Technology Program, China (Grant No. 2023ZF137); Basic Public Welfare Research Program of Zhejiang Province(Grant No.LTGY23H270007). Author Contributions Conception and design: Mengjie Jiang;Collection and assembly of data: Mengjie Jiang, Jing Luo, Jinhan Chen;Data analysis and interpretation: Mengjie Jiang, Qin Zhu;Graphic illustration: Mengjie Jiang, Jing Luo,Li Gao;Manuscript writing: All authors;Manuscript revision: Hongyu Chen, Qin Zhu;Final approval of manuscript: All authors. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Disclosure The authors report no conflicts of interest in this work. Ethics approval This study received approval from the Ethics Committee of Hangzhou Hospital of Traditional Chinese Medicine (Ethics No. 2023KLL002). Informed consent was obtained from all individual participants included in the study. References Li H, Lu W, Wang A, et al. Changing epidemiology of chronic kidney disease as a result of type 2 diabetes mellitus from 1990 to 2017: estimates from global burden of disease 2017[J]. J Diabetes Investig, 2021,12(3): 346-356. DOI: 10.1111/jdi.13355. Karalliedde J, Winocour P, Chowdhury TA, et al. Clinical practice guidelines for management of hyperglycaemia in adults with diabetic kidney disease[J]. Diabet Med, 2022,39(4): e14769. DOI: 10.1111/dme.14769. Chemouny JM, Bobot M, Sannier A, et al. Kidney biopsy in type 2 diabetes: a multicenter cross-sectional study[J].Am J Nephrol, 2021, 52(2): 131-140. DOI: 10.1159/000514259. Soleymanian T, Hamid G, Arefi M, Najafi I, Ganji MR, Amini M, Hakemi M, Tehrani MR, Larijani B. Non-diabetic renal disease with or without diabetic nephropathy in type 2 diabetes: clinical predictors and outcome. Ren Fail. 2015 May;37(4):572-5. doi: 10.3109/0886022X.2015.1007804. Epub 2015 Feb 16. PMID: 25682971. TERVAERT T W, MOOYAART A L, AMANN K, et al. Pathologic classification of diabetic nephropathy[J]. J Am Soc Nephrol, 2010, 21(4): 556-563. Castellano I, Covarsí A, Novillo R, Gómez-Martino JR, Ferrando L. Lesiones histológicas renales en pacientes con diabetes mellitus tipo II [Renal histological lesions in patients with type II diabetes mellitus]. Nefrologia. 2002;22(2):162-9. Spanish. PMID: 12085417. Li H, Yu X, Lan P, et al. Spectrum of biopsy-proven kidney diseases in northwest China: a review of 30 years of experiences[J]. Int Urol Nephrol, 2022, 54(10): 2609- 2616. DOI: 10.1007/s11255-022-03168-7. Liu D, Huang T, Chen N, et al. The modern spectrum of biopsy-proven renal disease in Chinese diabetic patients-a retrospective descriptive study. PeerJ 2018;6:e4522. Sharma SG, Bomback AS, Radhakrishnan J, et al. The modern spectrum of renal biopsy findings in patients with diabetes. Clin J Am Soc Nephrol 2013;8:1718–24. Zhuo L, Zou G, Li W, et al. Prevalence of diabetic nephropathy complicating non-diabetic renal disease among Chinese patients with type 2 diabetes mellitus. Eur J Med Res 2013;18:4. Kimmelstiel P, Wilson C. Intercapillary lesions in the glomeruli of the kidney.Am J Pathol (1936) 12:83–98.7. Zeni L, Norden AGW, Cancarini G, Unwin RJ. A more tubulocentric view of diabetic kidney disease. J Nephrol (2017) 30:701–17. doi: 10.1007/s40620-017-0423-9 Thomas MC, Brownlee M, Susztak K, Sharma K, Jandeleit-Dahm KAM,Zoungas S, et al. Diabetic kidney disease. Nat Rev Dis Primers (2015) 1:15018.doi: 10.1038/nrdp.2015.18 Gilbert RE. Proximal tubulopathy: prime mover and key therapeutic target in diabetic kidney disease. Diabetes (2017) 66:791–800. doi: 10.2337/db16-0796 Nihalani D, Susztak K. Sirt1–Claudin-1 crosstalk regulates renal function. Nat Med (2013) 19:1371–2. doi: 10.1038/nm.3386 Bonventre JV. Can we target tubular damage to prevent renal function decline in diabetes? Semin Nephrol (2012) 32:452–62. doi: 10.1016/j.semnephrol.2012.07.008 Bohle A, Wehrmann M, Bogenschütz O, Batz C, Müller GA, Müller GA. The pathogenesis of chronic renal failure in diabetic nephropathy. Pathol - Res Pract (1991) 187:251–9. doi: 10.1016/S0344-0338(11)80780-6 Chen J, Liu Q, He J, Li Y. Immune responses in diabetic nephropathy: Pathogenic mechanisms and therapeutic target. Front Immunol (2022) 13:958790. doi: 10.3389/fimmu.2022.958790 Tesch GH. Diabetic nephropathy – is this an immune disorder? Clin Sci (2017) 131:2183–99. doi: 10.1042/CS20160636 Gilbert RE, Cooper ME. The tubulointerstitium in progressive diabetic kidney disease: More than an aftermath of glomerular injury? Kidney Int (1999) 56:1627–37. doi: 10.1046/j.1523-1755.1999.00721.x Shen S, Ji C, Wei K. Cellular senescence and regulated cell death of tubular epithelial cells in diabetic kidney disease. Front Endocrinol (Lausanne) (2022) 13:924299. doi: 10.3389/fendo.2022.924299 Donate-Correa J, Luis-Rodríguez D, Martín-Núñez E, Tagua VG, Hernández-Carballo C, Ferri C, et al. Inflammatory targets in diabetic nephropathy. JCM (2020) 9:458. doi: 10.3390/jcm9020458 Tervaert TWC, Mooyaart AL, Amann K, Cohen AH, Cook HT, Drachenberg CB, et al. Pathologic classification of diabetic nephropathy. J Am Soc Nephrol (2010) 21:556–63. doi: 10.1681/ASN.2010010010 D'Amico G, Bazzi C. Pathophysiology of proteinuria. Kidney Int. 2003 Mar;63(3):809-25. doi: 10.1046/j.1523-1755.2003.00840.x. PMID: 12631062. sai SF, Tarng DC. Anemia in patients of diabetic kidney disease. J Chin Med Assoc. 2019 Oct;82(10):752-755. doi: 10.1097/JCMA.0000000000000175. PMID: 31453863. Ponticelli C, Podestà MA, Moroni G. Hyperuricemia as a trigger of immune response in hypertension and chronic kidney disease. Kidney Int. 2020 Nov;98(5):1149-1159. doi: 10.1016/j.kint.2020.05.056. Epub 2020 Jul 8. PMID: 32650020. Taft JL,Billson VR,Nankervis A,Kincaid-Smith P,et al.A clini-cal histological study of individuals with diabetes mellitus and protein-uria.Diabet Med,1990,7(3):215-221. Taft JL,Billson VR,Nankervis A,Kincaid-Smith P,et al.A clini-cal histological study of individuals with diabetes mellitus and protein-uria.Diabet Med,1990,7(3):215-221. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 17 Jan, 2025 Read the published version in BMC Nephrology → Version 1 posted Editorial decision: Revision requested 14 Oct, 2024 Reviews received at journal 01 Oct, 2024 Reviews received at journal 01 Jul, 2024 Reviewers agreed at journal 01 Jul, 2024 Reviewers agreed at journal 01 Jul, 2024 Reviewers invited by journal 01 Jul, 2024 Editor assigned by journal 18 Jun, 2024 Editor invited by journal 25 Apr, 2024 Submission checks completed at journal 24 Apr, 2024 First submitted to journal 20 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4297672","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":296428388,"identity":"827512af-5e41-4b30-ac3a-a0b55e4cf666","order_by":0,"name":"Mengjie Jiang","email":"","orcid":"","institution":"Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mengjie","middleName":"","lastName":"Jiang","suffix":""},{"id":296428389,"identity":"cae30bdd-37e4-48aa-ac07-267e6e80f770","order_by":1,"name":"Hongyu Chen","email":"","orcid":"","institution":"Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hongyu","middleName":"","lastName":"Chen","suffix":""},{"id":296428390,"identity":"f60d28c1-5d77-42c3-8483-f56b25ac3eba","order_by":2,"name":"Jing Luo","email":"","orcid":"","institution":"Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Luo","suffix":""},{"id":296428391,"identity":"ebdb398e-d378-4559-b042-90758131e6e2","order_by":3,"name":"Jinhan Chen","email":"","orcid":"","institution":"The Second Affiliated College Of Zhejiang Chinese Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jinhan","middleName":"","lastName":"Chen","suffix":""},{"id":296428392,"identity":"995da4a2-b9a4-41f7-a6d3-00a90a5c4a59","order_by":4,"name":"Li Gao","email":"","orcid":"","institution":"Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Gao","suffix":""},{"id":296428393,"identity":"07f13de6-6911-4c6c-bdac-1509abc3a5da","order_by":5,"name":"Qin Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIie3PsQrCMBCA4SuBcwl2zWRe4aQgHYrP0lLI5CD4ADpl6gMI+hC6ObYGnXwABRcRnDsqOBjURYW2o0N+yHYfuQNwuf4xYV8MHUCWQ15S1G9KAkCMoZgOVdqMgCVgCePl2pvUCTnLuvvTijptZIWJKGfQMptFFfHmuyBMdhSg3cwM6NgGrtS+ijAx6IlE3xMtM7LkwkDwXiXBF6GxRr80IRlvUkf4m8SIHAw0IUKoUWhJVyNSkZFKse4WOU2Xh5smKTU7l9d71PdbZltJnj99Xlc3/ktcLpfL9d0DUoREFwLBed0AAAAASUVORK5CYII=","orcid":"","institution":"Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University","correspondingAuthor":true,"prefix":"","firstName":"Qin","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2024-04-20 13:28:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4297672/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4297672/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12882-024-03931-1","type":"published","date":"2025-01-17T15:57:16+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":55639218,"identity":"d3785ebb-85a5-4239-8a0a-84cbb8373582","added_by":"auto","created_at":"2024-04-30 22:08:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":483696,"visible":true,"origin":"","legend":"\u003cp\u003eScreening Process for Selecting Patients with Diabetic Kidney Disease\u003c/p\u003e","description":"","filename":"Fig1ScreeningProcessforSelectingPatientswithDiabeticKidneyDisease.png","url":"https://assets-eu.researchsquare.com/files/rs-4297672/v1/005bbb8833cb4233eaed1a30.png"},{"id":55639214,"identity":"af3a2cd6-bc90-47a0-bcd8-0bf089793cfc","added_by":"auto","created_at":"2024-04-30 22:08:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":14036,"visible":true,"origin":"","legend":"\u003cp\u003eAge and sex Distribution Profile of 235 Patients\u003c/p\u003e","description":"","filename":"Fig2AgeandsexDistributionProfileof235Patients.png","url":"https://assets-eu.researchsquare.com/files/rs-4297672/v1/2b015c0dd7cca9229ed8edf0.png"},{"id":55639215,"identity":"f50f56b7-3266-4071-84e8-b5e9c1271a61","added_by":"auto","created_at":"2024-04-30 22:08:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":182396,"visible":true,"origin":"","legend":"\u003cp\u003ePathology of DKD Group\u003c/p\u003e","description":"","filename":"Fig3PathologyofDKDGroup.png","url":"https://assets-eu.researchsquare.com/files/rs-4297672/v1/6fbda9149a8dc726d095585c.png"},{"id":55639776,"identity":"97742e45-243d-4a6c-8622-075959d417c7","added_by":"auto","created_at":"2024-04-30 22:16:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":96881,"visible":true,"origin":"","legend":"\u003cp\u003ePathology of DKD+NDKD Group\u003c/p\u003e","description":"","filename":"Fig4PathologyofDKDNDKDGroup.png","url":"https://assets-eu.researchsquare.com/files/rs-4297672/v1/3fd6d4a0bb69b0efa13b88b7.png"},{"id":55639216,"identity":"e73a9a41-be61-481c-bd32-7209ba3242bb","added_by":"auto","created_at":"2024-04-30 22:08:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":856163,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of Clinicopathological Correlation\u003c/p\u003e","description":"","filename":"Fig5HeatmapofClinicopathologicalCorrelation.png","url":"https://assets-eu.researchsquare.com/files/rs-4297672/v1/fa668b1e99a50da85ce48caa.png"},{"id":74284534,"identity":"409a7c35-55be-4687-bac6-d75ebf33311d","added_by":"auto","created_at":"2025-01-20 16:08:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2452203,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4297672/v1/a791e04d-be82-452f-ae0b-bed19ffaf345.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Characterization of Diabetic Kidney Disease in 235 Patients: Clinical and Pathological Insights with or without Concurrent Non-Diabetic Kidney Disease","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eDiabetic kidney involvement primarily manifests as diabetic kidney disease (DKD), a leading cause of end-stage kidney disease (ESKD)[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, some diabetic patients may solely present with diabetes mellitus or non-diabetic kidney disease (NDKD), or DKD combined with NDRD[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This study statistically analyzed patients with DKD and DKD\u0026thinsp;+\u0026thinsp;NDKD, investigating their pathological and clinical characteristics, and identifying the independent factors associated with DKD\u0026thinsp;+\u0026thinsp;NDKD to aid clinical differentiation.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e2.1 Study design and population\u003c/h2\u003e\n\u003cp\u003eThis single-center retrospective investigation included 235 patients diagnosed with diabetic kidney disease who underwent renal biopsy at the Nephrology Department of Hangzhou Hospital of Traditional Chinese Medicine between July 2014 and December 2022. Inclusion criteria were: (1) clinical diagnosis of diabetes mellitus; (2) nephropathologic diagnosis of DKD or DKD\u0026thinsp;+\u0026thinsp;NDKD; (3) availability of complete clinical and pathological data. Exclusion criteria were: (1) pathology indicating NDKD; (2) incomplete clinical and pathological data; (3) patients with acute illnesses, immunological disorders, malignancies, or infections (Fig.\u0026nbsp;1)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e2.2 Data collection\u003c/h2\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eGeneral clinical data, laboratory examination results, and renal biopsy pathology data were collected from patients diagnosed with DKD who underwent renal pathology biopsy at Hangzhou Hospital of Traditional Chinese Medicine. The data were categorized into the DKD group and the DKD\u0026thinsp;+\u0026thinsp;NDKD group. DKD pathology grading criteria published in the American Journal of Kidney Diseases were used as a reference for grading[\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e]. Grade I represents simple glomerular basement membrane thickening, characterized by the absence or presence of mild specific changes under light microscopy. Electron microscopy reveals glomerular basement membrane thickening, exceeding 395 nm in women and 430 nm in men (age\u0026thinsp;\u0026ge;\u0026thinsp;9 years), with pathological changes not reaching grades II, III, or IV. Grade IIa represents mild tethered basement membrane widening, with mild widening observed in over 25% of the glomeruli and no pathological changes reaching grades III or IV. Grade IIb denotes severe thylakoid stromal widening, with over 25% of glomeruli exhibiting severe widening and pathological changes not extending to grades III or IV. Grade III manifests as nodular sclerosis, characterized by more than one Kimmelstiel\u0026ndash;Wilson nodule (K-W nodules), with pathological changes not reaching grade IV. Grade IV indicates advanced diabetic glomerulosclerosis, with over 50% total glomerulosclerosis accompanied by concurrent grade I-III pathological changes. Renal biopsy pathology was evaluated by the same experienced renal pathologist, and based on the criteria, the pathological findings were classified as follows: early diabetic kidney injury in Grade I, diffuse diabetic glomerulosclerosis in Grades IIa and IIb, nodular diabetic glomerulosclerosis in Grade III, and advanced diabetic glomerulosclerosis in Grade IV.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003e2.3 Statistical analysis\u003c/h2\u003e\n\u003cp\u003eStatistical analysis was performed using SPSS 26.0 software. Measurement data following normal distribution were presented as\u0026oline;\u003cem\u003ex\u003c/em\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cem\u003es\u003c/em\u003e and compared between groups using the independent samples t-test. Non-normally distributed measurement data were presented as M (P\u003csub\u003e25\u003c/sub\u003e, P\u003csub\u003e75\u003c/sub\u003e) and compared between groups using the Kruskal\u0026ndash;Wallis rank-sum test. Counting data were expressed as the number of cases and percentage, and were compared between groups using the \u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e test or Fisher's exact probability method. Statistical significance was set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1 Clinical Data\u003c/h2\u003e\n\u003cp\u003eAmong the 235 patients diagnosed with DKD, 93 were classified in the DKD alone group, while 142 were categorized in the DKD\u0026thinsp;+\u0026thinsp;NDKD group. The highest proportion of patients had a duration of diabetes\u0026thinsp;\u0026lt;\u0026thinsp;5 years, accounting for 33.2%, followed by those with a duration of 10 to 15 years at 25.1%. Conversely, only 7.7% of patients had a history of diabetes equal to or greater than 20 years. The mean age of all patients was 54.22\u0026thinsp;\u0026plusmn;\u0026thinsp;10.35 years, ranging from 29 to 76 years. The most common age groups were 50\u0026ndash;59 and 60\u0026ndash;69 years, representing 65% of the sample. Among the total sample, 182 individuals (77.44%) were men, while 53 individuals (22.55%) were women, resulting in a male-to-female ratio of 3.43:1.\u003c/p\u003e\n\u003cp\u003eIn comparing clinical data between the two groups, significant differences were observed in sex, body mass index (BMI), hemoglobin, urine osmolality, blood creatinine, microscopic erythrocyte grade, urinary immunoglobulin G (UIgG)/creatinine ratio, and serum IgA. The proportion of men and the proportion of microscopic erythrocyte grade in the DKD\u0026thinsp;+\u0026thinsp;NDKD group were higher than those in the DKD group. Moreover, BMI, hemoglobin, urine osmolality, blood creatinine, and serum IgA were higher in the DKD\u0026thinsp;+\u0026thinsp;NDKD group. Urinary IgG/creatinine ratio was higher in the DKD group compared to the DKD\u0026thinsp;+\u0026thinsp;NDKD group (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eComparative Analysis of Clinical Characteristics Among Patient Cohorts\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDKD\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDKD\u0026thinsp;+\u0026thinsp;NDKD\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eRatio\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u0026chi;2/Z\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eSex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e117\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e64.30%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e5.029\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.025*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47.20%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eHistory of diabetes(years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e67.90%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003e4.846\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003e0.303\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e58.50%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59.30%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44.40%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e61.10%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eHypertension\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e78.60%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e2.050\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.152\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e131\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59.30%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eNon-diabetic Retinopathy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e118\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e58.40%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e2.430\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.119\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e72.70%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eHigh blood fat disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e61.70%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.107\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.744\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59.60%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMetabolic acidosis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e117\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.90%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e3.774\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.052\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e75.80%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eMicroscopic erythrocyte grade\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59.20%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e13.040\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e0.023*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e+-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48.90%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e+\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50.00%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e++\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e82.60%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e+++\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e81.80%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e++++\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAntinuclear Antibodies\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAbnormal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53.20%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.286\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.257\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNormal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e117\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62.20%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eBlood light chain KAP/LAM\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAbnormal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60.10%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.905\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNormal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60.90%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAge\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54(47\u0026thinsp;~\u0026thinsp;61)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56(48\u0026thinsp;~\u0026thinsp;63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.132\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.258\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eBMI\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23.71\u0026thinsp;\u0026plusmn;\u0026thinsp;2.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.27\u0026thinsp;\u0026plusmn;\u0026thinsp;3.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.344\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAverage arterial blood pressure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e106.31\u0026thinsp;\u0026plusmn;\u0026thinsp;15.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e108.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.337\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eHemoglobin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e116.12\u0026thinsp;\u0026plusmn;\u0026thinsp;22.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e123.36\u0026thinsp;\u0026plusmn;\u0026thinsp;22.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.016*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eUrine osmolality\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e457(376\u0026thinsp;~\u0026thinsp;574)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e513(421.75\u0026thinsp;~\u0026thinsp;632)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.112\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.035*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCreatinine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e114.7(75\u0026thinsp;~\u0026thinsp;165)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e131.5(91\u0026thinsp;~\u0026thinsp;176)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.108\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.035*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eBlood uric acid\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e390(327.5\u0026thinsp;~\u0026thinsp;458)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e408(354\u0026thinsp;~\u0026thinsp;491)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.827\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.068\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eGlycosylated hemoglobin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.4(6.4\u0026thinsp;~\u0026thinsp;8.35)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.1(6.3\u0026thinsp;~\u0026thinsp;7.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.875\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.061\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e24-hour urine protein\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.02(1.48\u0026thinsp;~\u0026thinsp;5.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.265(0.883\u0026thinsp;~\u0026thinsp;5.388)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.095\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.274\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eUIgG/urine creatinine ratio\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.213(0.073\u0026thinsp;~\u0026thinsp;0.650)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.113(0.034\u0026thinsp;~\u0026thinsp;0.303)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.869\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.004*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eSerum immunoglobulin G\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1050(873.5\u0026thinsp;~\u0026thinsp;1290)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1020(793.25\u0026thinsp;~\u0026thinsp;1292.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.999\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.318\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eSerum immunoglobulin A\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e233(182.5\u0026thinsp;~\u0026thinsp;302.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e263.5(201\u0026thinsp;~\u0026thinsp;353.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.361\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.018*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eSerum immunoglobulin M\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e83(61\u0026thinsp;~\u0026thinsp;114.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e84(61.5\u0026thinsp;~\u0026thinsp;118)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.996\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eComplement C3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e103(92\u0026thinsp;~\u0026thinsp;114)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e102(91\u0026thinsp;~\u0026thinsp;116)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.995\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eComplement C4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26(22\u0026thinsp;~\u0026thinsp;31)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26(22\u0026thinsp;~\u0026thinsp;31)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.437\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.662\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFootnote:\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003ea. Microscopic erythrocytes: \"-\" denotes\u0026thinsp;\u0026lt;\u0026thinsp;3, \"+-\" signifies 4\u0026ndash;9, \"+\" represents 10\u0026ndash;30, \"++\" indicates\u0026thinsp;\u0026gt;\u0026thinsp;30, \"+++\" denotes microscopic erythrocytes greater than three-quarters of the field of view, and \"++++\" signifies an entire field of view littered with erythrocytes.\u003c/p\u003e\n\u003cp\u003eb. diabetic kidney disease(DKD), non-diabetic kidney disease(NDKD),Kappa(KAP), Lambda(LAM),body mass index(BMI),urinary immunoglobulin G(UIgG).\u003c/p\u003e\n\u003cp\u003ec. The asterisk symbol (*) indicates statistical significance at \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2 Renal Pathology\u003c/h2\u003e\n\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n\u003ch2\u003e3.2.1 Distribution of Pathological Types\u003c/h2\u003e\n\u003cp\u003eIn the DKD group, the predominant pathological findings were nodular diabetic glomerulosclerosis, followed by diffuse diabetic glomerulosclerosis, early diabetic nephropathy, and advanced diabetic glomerulosclerosis. Within the DKD\u0026thinsp;+\u0026thinsp;NDKD group, the most prevalent pathological types were acute and chronic tubulointerstitial disease (ATID, CTID) and IgA nephropathy. Furthermore, concurrent cases of IgA nephropathy, CTID, and membranous nephropathy (MN), MN with ATID and CTID, and other renal injuries (such as monoclonal Ig secondary kidney damage, crescentic glomerulonephritis, hepatitis B virus-associated secondary nephritis, lupus nephritis, proliferative sclerosing glomerulonephritis, crystalline nephropathy, dry syndrome kidney injury, or obesity-associated kidney injury) were observed. Specific data are depicted in Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n\u003ch2\u003e3.2.2 Renal pathology grading and scoring\u003c/h2\u003e\n\u003cp\u003eAs patients with early diabetic nephropathy in the sample did not yet exhibit significant glomerular, tubulointerstitial, and renal vascular lesions, pathological grading of diabetic nephropathy was not applicable. Excluding 65 patients with pathology suggestive of early diabetic nephropathy, the distribution of glomerular grading, tubulointerstitial lesions (interstitial fibrosis and tubular atrophy [IFTA] score, interstitial inflammation score), and renal vasculature (arteriolar hyalinosis, arteriosclerosis) among patients in the DKD group and the DKD\u0026thinsp;+\u0026thinsp;NDKD group is detailed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eRenal Pathology Grading and Scoring in Both Groups\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDKD\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDKD\u0026thinsp;+\u0026thinsp;NDKD\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eF\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eGlomerular classification\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIIa\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3(3.61%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19(21.84%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003e27.267\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIIb\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16(19.28%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30(34.48%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIIb-III\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11(13.25%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14(16.09%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50(60.24%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22(25.29%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eⅣ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3(3.61%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2(2.30%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eIFTA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12(14.46%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13(14.94%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.567\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.753\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52(62.65%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50(57.47%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19(22.89%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24(27.59%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003einterstitial inflammation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66(79.52%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e55(63.22%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e5.501\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.019\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17(20.48%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32(36.78%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003earteriolar hyalinosis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15(18.07%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19(21.84%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.377\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.539\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e68(81.93%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e68(78.16%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eArteriosclerosis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13(15.66%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14(16.09%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e2.832\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.243\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59(71.08%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e68(78.16%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11(13.25%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5(5.75%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e3.3 Clinicopathological Correlation\u003c/h2\u003e\n\u003cp\u003eThe grading of glomerular lesions exhibited a significantly positive correlation with 24-hour urine protein quantification and a highly significant negative correlation with hemoglobin, urinary specific gravity, and osmolality. Tubulointerstitial lesions (IFTA score, interstitial inflammation score) demonstrated a significant positive correlation with blood creatinine and uric acid levels and a significant negative correlation with estimated glomerular filtration rate (GFR). Renal arteriolar hyalinosis displayed a significantly positive correlation with age and blood creatinine levels, and a substantially negative correlation with urine osmolality and GFR. Renal vascular atherosclerosis showed a significant positive correlation with age and a negative correlation with GFR (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003e3.4 Independent Factors of DKD\u0026thinsp;+\u0026thinsp;NDKD\u003c/h2\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eThe results of univariate logistic regression analysis indicated that sex, BMI, hemoglobin levels, urine osmolality, blood creatinine levels, microscopic erythrocyte grade, UIgG/urine creatinine ratio, and serum IgA levels were significant factors influencing DKD\u0026thinsp;+\u0026thinsp;NDKD (\u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05). Multifactorial logistic regression analysis revealed that BMI (odds ratio [OR]\u0026thinsp;=\u0026thinsp;1.193, 95% confidence interval [CI]: 1.072\u0026thinsp;~\u0026thinsp;1.327, P\u0026thinsp;=\u0026thinsp;0.001), blood creatinine level (OR\u0026thinsp;=\u0026thinsp;1.007, 95% CI 1.003\u0026thinsp;~\u0026thinsp;1.011, P\u0026thinsp;=\u0026thinsp;0.001), microscopic erythrocyte grade (+++) (OR\u0026thinsp;=\u0026thinsp;5.879, 95% CI 1.529\u0026thinsp;~\u0026thinsp;22.604, P\u0026thinsp;=\u0026thinsp;0.01), UIgG/urine creatinine ratio (OR\u0026thinsp;=\u0026thinsp;0.242, 95% CI 0.089\u0026thinsp;~\u0026thinsp;0.661, P\u0026thinsp;=\u0026thinsp;0.006), and serum IgA (OR\u0026thinsp;=\u0026thinsp;1.005, 95% CI 1.002\u0026thinsp;~\u0026thinsp;1.008, P\u0026thinsp;=\u0026thinsp;0.003) were independently correlated with DKD\u0026thinsp;+\u0026thinsp;NDKD, as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The prediction was performed with a classification table utilizing a binary logistic regression model, achieving a prediction accuracy of 73.6%, as presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eLogistic Regression Analysis of DKD\u0026thinsp;+\u0026thinsp;NDKD Factors\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eUnivariate logistic regression\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMultivariate logistic analysis\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u0026chi;2/Z\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOR (95% CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eSex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e5.029\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.025*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.911(0.409\u0026thinsp;~\u0026thinsp;2.026)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.818\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eBMI(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.344\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.193(1.072\u0026thinsp;~\u0026thinsp;1.327)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eHemoglobin(g/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.016*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.015(0.997\u0026thinsp;~\u0026thinsp;1.034)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.102\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eUrine osmolality(mOsm/kg)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.112\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.035*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.002(0.999\u0026thinsp;~\u0026thinsp;1.004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.202\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eBlood creatinine(umol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.108\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.035*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.007(1.003\u0026thinsp;~\u0026thinsp;1.011)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eMicroscopic erythrocyte grade\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e13.040\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e0.023*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.071\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e+-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1469427539.261\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.999\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e+\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.111(0.505\u0026thinsp;~\u0026thinsp;2.448)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.793\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e++\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.393(0.439\u0026thinsp;~\u0026thinsp;4.422)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.574\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e+++\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.879(1.529\u0026thinsp;~\u0026thinsp;22.604)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.01*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e++++\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.863(0.937\u0026thinsp;~\u0026thinsp;36.687)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.059\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eUIgG/Urine creatinine(mg/mgCr)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.869\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.004*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.242(0.089\u0026thinsp;~\u0026thinsp;0.661)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.006*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eSerum immunoglobulin A(mg/dl)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.361\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.018*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.005(1.002\u0026thinsp;~\u0026thinsp;1.008)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePredictions from the Binary Logistic Regression Model\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePrediction\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eDKD\u0026thinsp;+\u0026thinsp;NDKD\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePercentage correct (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eDKD\u0026thinsp;+\u0026thinsp;NDKD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e118\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e83.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOverall percentage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e73.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study examined individuals who underwent renal biopsy at Hangzhou Hospital of Traditional Chinese Medicine between July 2014 and December 2022 and received a pathological diagnosis of DKD. By exploring the relationship between various pathological and clinical factors, comparing variances between the DKD and DKD\u0026thinsp;+\u0026thinsp;NDKD cohorts, and investigating independent factors associated with DKD\u0026thinsp;+\u0026thinsp;NDKD, it was aimed to identify patients with diabetes who could benefit from early renal biopsy in clinical practice. Such an approach could assist in developing personalized treatment strategies and enabling early detection and intervention.\u003c/p\u003e \u003cp\u003eNodular glomerulosclerosis has been consistently identified as the primary pathological change in DKD[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Epidemiological investigations have indicated that MN is the most common pathology in DKD\u0026thinsp;+\u0026thinsp;NDKD, followed by IgA nephropathy[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, in the present study, ATID/CTID and IgA nephropathy emerged as the predominant pathological types in the DKD\u0026thinsp;+\u0026thinsp;NDKD group, diverging from previous findings. IgA nephropathy remains a prevalent subtype among patients with diabetes[8.9.10]. The present study results indicate that DKD combined with IgA nephropathy is one of the most common forms of DKD\u0026thinsp;+\u0026thinsp;NDKD pathology. IgA nephropathy and DKD can lead to proliferative glomerular lesions and even glomerulosclerosis due to connective tissue proliferation and basement membrane alterations within the glomeruli. While DKD was traditionally associated primarily with pathological glomerular changes[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], recent studies have highlighted a considerable correlation of tubulointerstitial damage extent with renal function progression and prognosis[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Tubulointerstitial lesions may manifest independently of glomerular lesions[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], with immunoinflammation[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] hypothesized to play a pivotal role in the onset and progression of renal tubular injury in DKD. Early disease stages frequently exhibit hypertrophic phenomena, characterized by increased renal tubular epithelial cell numbers and thickening of the tubular basement membrane, which are critical in triggering and accelerating renal tubular interstitial fibrosis[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Prolonged hyperglycemia, ischemia, and hypoxia lead to apoptosis, atrophy, and degeneration of renal tubular cells[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], along with inflammatory cell infiltration, increased inflammatory factors, interstitial fibrosis, interstitial arteriolar atherosclerosis, and small artery hyalinization, all influencing lesion development and progression[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].Correlation analyses have revealed a positive association between glomerular grading and 24-hour urine protein quantification, suggesting that glomerular structural damage may increase urinary protein excretion[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This phenomenon could stem from increased filtration membrane permeability and decreased tubular reabsorption. The significant negative correlation between glomerular grading and hemoglobin levels likely results from worsening renal function associated with increasing glomerular grading, leading to reduced renal erythropoietin (EPO) secretion and consequent exacerbation of anemia.[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] Therefore, a significant negative correlation exists between the progression of glomerular grading and the exacerbation of renal anemia. Glomerular grading demonstrated a significant negative correlation with urinary specific gravity and urine osmolality, indicating that more severe glomerular injury is associated with lower specific gravity and osmolality. This result is likely due to glomerular injury frequently leading to tubulointerstitial damage, which decreases tubular concentrating capacity and lowers urinary specific gravity and osmolality.\u003c/p\u003e \u003cp\u003eTubulointerstitial lesions exhibited a significant positive correlation with blood creatinine and a significant negative correlation with GFR, reflecting the close association between tubulointerstitial lesions and renal function. Damage to the tubulointerstitial stroma affects the filtration and excretory functions of the kidneys, resulting in a weakened ability to filter waste products, such as creatinine, leading to their accumulation in the body, elevated blood creatinine levels, and decreased renal function. The positive correlation between tubulointerstitial lesions and blood uric acid may be attributed to hyperuricemia causing vascular and tubulointerstitial damage by activating the renin-angiotensin system and immune system response[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Furthermore, tubulointerstitial lesions can hinder uric acid excretion, contributing to secondary hyperuricemia.\u003c/p\u003e \u003cp\u003eVitrification of renal arterioles was positively correlated with age and blood creatinine levels, and negatively correlated with urine osmolality and GFR. Physiological aging of blood vessels accelerates the vitrification of renal arterioles with increasing age. Diabetes-induced vitrification of renal arterioles reduces vascular elasticity, affecting renal blood supply and disrupting kidney filtration function, resulting in decreased GFR and impaired creatinine removal from the blood. The severity of renal arteriolar vitrification is negatively correlated with urine osmolality due to ischemic damage to glomeruli and tubules associated with the diseased arterioles. Consequently, the concentrating and reabsorbing functions of both distal and proximal tubules are impaired, resulting in decreased urine osmolality and increased nocturia.\u003c/p\u003e \u003cp\u003eRenal vascular atherosclerosis is positively correlated with age and negatively correlated with GFR. Renal vascular sclerosis may be part of systemic arteriosclerosis, affecting multiple organ blood supply and function, including the glomeruli. Increased renal vascular atherosclerosis severity is more likely to cause renal artery narrowing, leading to renal ischemia, glomerular atrophy, fibrosis, and necrosis, thereby reducing GFR.\u003c/p\u003e \u003cp\u003eComparison of clinical and pathological data between the two groups revealed significant differences in glomerulopathy grading, tubulointerstitial lesions, microscopic erythrocyte grade, BMI, urine osmolality, blood creatinine, UIgG/urine creatinine, and serum IgA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Multifactorial logistic regression analysis identified BMI, blood creatinine level, microscopic erythrocyte grade, UIgG/urine creatinine, and serum IgA as independent correlates of DKD\u0026thinsp;+\u0026thinsp;NDKD. Specifically, BMI, blood creatinine level, microscopic erythrocyte grade (+++), and serum IgA were identified as risk factors for DKD\u0026thinsp;+\u0026thinsp;NDKD, while UIgG/urine creatinine was identified as a protective factor. Previous studies have confirmed the role of hematuria and blood creatinine in identifying DKD\u0026thinsp;+\u0026thinsp;NDKD, suggesting that NDKD accelerates the deterioration of renal function in patients with type 2 diabetes[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Previous studies have hinted at a higher BMI in patients with NDKD[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This study similarly suggests that patients with comorbid NDKD exhibit a higher BMI compared to those with DKD, potentially indicating a link to obesity-associated nephropathy. Elevated levels of IgA in humoral immunity frequently signify an abundance of IgA or its immune complexes, which, coupled with factors such as local inflammatory responses, increase the risk of IgA nephropathy. UIgG/urine creatinine, relatively unaffected by other factors, might offer insights into diagnosing DKD. Considering IgG\u0026rsquo;s sizeable molecular size, when the glomerular basement membrane\u0026rsquo;s function is impaired, increased basement membrane leads to more frequent excretion of urinary large-molecule proteins, suggesting a likelihood of DKD. These findings provide critical factors for identifying patients with DKD\u0026thinsp;+\u0026thinsp;NDKD, aiding in accurate prediction of the pathological type and individualized treatment planning.\u003c/p\u003e \u003cp\u003eIn our study, assessing the binary logistic regression model\u0026rsquo;s predictive performance through a classification table yielded a prediction accuracy of 73.6%. While this result demonstrates substantial efficacy in sample classification for DKD\u0026thinsp;+\u0026thinsp;NDKD prediction, additional metrics are necessary for comprehensive performance evaluation. Further validation efforts are imperative to ensure the model\u0026rsquo;s reliability and applicability.\u003c/p\u003e \u003cp\u003eThis study was constrained by single-site, single-sample sources, failing to encompass geographic, population, and healthcare system diversities. Its retrospective nature restricted analysis to baseline data, precluding insights into long-term prognosis across diverse pathology types. Moreover, the study overlooked factors such as genetic predispositions and lifestyle influences beyond the clinical and pathologic data examined. Future investigations should address these limitations by broadening geographic and sample source scopes, conducting multicenter studies for enhanced patient representation, and incorporating long-term prospective follow-up. These initiatives would facilitate the development of more accurate prediction models by monitoring disease progression and treatment responses over extended periods. Moreover, integrating genetic and lifestyle information would enable a comprehensive understanding of diabetic nephropathy mechanisms, informing improved clinical management and treatment strategies.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn summary, nodular glomerulosclerosis emerges as the primary pathological hallmark of DKD; however, the coexistence of acute and chronic tubulointerstitial lesions alongside IgA nephropathy prevails in patients with DKD and NDKD. Moreover, diabetic nephropathy pathology shows associations with various clinical parameters. For instance, tubulointerstitial lesions correlate positively correlate with blood creatinine and uric acid levels, and negatively correlate with GFR, among others. Independent correlates of DKD\u0026thinsp;+\u0026thinsp;NDKD include BMI, blood creatinine level, microscopic erythrocyte grade, UIgG/urine creatinine ratio, and serum IgA, aiding in DKD differentiation from NDKD.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eDKD: Diabetic kidney disease\u003c/p\u003e\n\u003cp\u003eNDKD: Non-diabetic kidney disease\u003c/p\u003e\n\u003cp\u003eESKD: End-stage kidney disease\u003c/p\u003e\n\u003cp\u003eK-W nodules: Kimmelstiel\u0026ndash;Wilson nodule\u003c/p\u003e\n\u003cp\u003eBMI: Body mass index\u003c/p\u003e\n\u003cp\u003eUIgG: Urinary immunoglobulin G\u003c/p\u003e\n\u003cp\u003eKAP: Kappa\u003c/p\u003e\n\u003cp\u003eLAM: Lambda\u003c/p\u003e\n\u003cp\u003eATID: Acute tubulointerstitial disease\u003c/p\u003e\n\u003cp\u003eCTID: Chronic tubulointerstitial disease\u003c/p\u003e\n\u003cp\u003eMN: Membranous nephropathy\u003c/p\u003e\n\u003cp\u003eIFTA: Interstitial fibrosis and tubular atrophy\u003c/p\u003e\n\u003cp\u003eGFR: Glomerular filtration rate\u003c/p\u003e\n\u003cp\u003eMAP: Mean Arterial Pressure\u003c/p\u003e\n\u003cp\u003eHb: Hemoglobin\u003c/p\u003e\n\u003cp\u003eUSG: Urinary Specific Gravity\u003c/p\u003e\n\u003cp\u003eUosm: Urine Osmolality\u003c/p\u003e\n\u003cp\u003eCr: Blood Creatinine\u003c/p\u003e\n\u003cp\u003eUA: Blood Uric Acid\u003c/p\u003e\n\u003cp\u003eTG: Triglycerides\u003c/p\u003e\n\u003cp\u003eGFR-EPI: Glomerular Filtration Rate\u003c/p\u003e\n\u003cp\u003e24hUP: 24-hour Urinary Protein\u003c/p\u003e\n\u003cp\u003eGC: Glomerular classification\u003c/p\u003e\n\u003cp\u003eTIN: Tubular Interstitial Inflammation\u003c/p\u003e\n\u003cp\u003eHAG: Arteriolar hyalinosis\u003c/p\u003e\n\u003cp\u003eAS: Atherosclerosis\u003c/p\u003e\n\u003cp\u003eOR：Odds ratio\u003c/p\u003e\n\u003cp\u003eEPO：Erythropoietin\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article [and its supplementary information files].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was financially supported by National Natural Science Foundation of China for Young Scholars (Grant No. 82205008); Medical Scientific Research Foundation of Zhejiang Province, China (Grant No. 2023RC242); Zhejiang Traditional Medicine and Technology Program, China (Grant No. 2023ZF137); Basic Public Welfare Research Program of Zhejiang Province(Grant No.LTGY23H270007).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was financially supported by National Natural Science Foundation of China for Young Scholars (Grant No. 82205008); Medical Scientific Research Foundation of Zhejiang Province, China (Grant No. 2023RC242); Zhejiang Traditional Medicine and Technology Program, China (Grant No. 2023ZF137); Basic Public Welfare Research Program of Zhejiang Province(Grant No.LTGY23H270007).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception and design: Mengjie Jiang;Collection and assembly of data: Mengjie Jiang, Jing Luo, Jinhan Chen;Data analysis and interpretation: Mengjie Jiang, Qin Zhu;Graphic illustration: Mengjie Jiang, Jing Luo,Li Gao;Manuscript writing: All authors;Manuscript revision: Hongyu Chen, Qin Zhu;Final approval of manuscript: All authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report no conflicts of interest in this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received approval from the Ethics Committee of Hangzhou Hospital of Traditional Chinese Medicine (Ethics No. 2023KLL002). Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLi H, Lu W, Wang A, et al. Changing epidemiology of chronic kidney disease as a result of type 2 diabetes mellitus from 1990 to 2017: estimates from global burden of disease 2017[J]. J Diabetes Investig, 2021,12(3): 346-356. DOI: 10.1111/jdi.13355.\u003c/li\u003e\n\u003cli\u003eKaralliedde J, Winocour P, Chowdhury TA, et al. Clinical practice guidelines for management of hyperglycaemia in adults with diabetic kidney disease[J]. Diabet Med, 2022,39(4): e14769. DOI: 10.1111/dme.14769.\u003c/li\u003e\n\u003cli\u003eChemouny JM, Bobot M, Sannier A, et al. Kidney biopsy in type 2 diabetes: a multicenter cross-sectional study[J].Am J Nephrol, 2021, 52(2): 131-140. DOI: 10.1159/000514259.\u003c/li\u003e\n\u003cli\u003eSoleymanian T, Hamid G, Arefi M, Najafi I, Ganji MR, Amini M, Hakemi M, Tehrani MR, Larijani B. Non-diabetic renal disease with or without diabetic nephropathy in type 2 diabetes: clinical predictors and outcome. Ren Fail. 2015 May;37(4):572-5. doi: 10.3109/0886022X.2015.1007804. Epub 2015 Feb 16. PMID: 25682971.\u003c/li\u003e\n\u003cli\u003eTERVAERT T W, MOOYAART A L, AMANN K, et al. Pathologic classification of diabetic nephropathy[J]. J Am Soc Nephrol, 2010, 21(4): 556-563.\u003c/li\u003e\n\u003cli\u003eCastellano I, Covars\u0026iacute; A, Novillo R, G\u0026oacute;mez-Martino JR, Ferrando L. Lesiones histol\u0026oacute;gicas renales en pacientes con diabetes mellitus tipo II [Renal histological lesions in patients with type II diabetes mellitus]. Nefrologia. 2002;22(2):162-9. Spanish. PMID: 12085417.\u003c/li\u003e\n\u003cli\u003eLi H, Yu X, Lan P, et al. Spectrum of biopsy-proven kidney diseases in northwest China: a review of 30 years of experiences[J]. Int Urol Nephrol, 2022, 54(10): 2609- 2616. DOI: 10.1007/s11255-022-03168-7.\u003c/li\u003e\n\u003cli\u003eLiu D, Huang T, Chen N, et al. The modern spectrum of biopsy-proven renal disease in Chinese diabetic patients-a retrospective descriptive study. PeerJ 2018;6:e4522.\u003c/li\u003e\n\u003cli\u003eSharma SG, Bomback AS, Radhakrishnan J, et al. The modern spectrum of renal biopsy findings in patients with diabetes. Clin J Am Soc Nephrol 2013;8:1718\u0026ndash;24.\u003c/li\u003e\n\u003cli\u003eZhuo L, Zou G, Li W, et al. Prevalence of diabetic nephropathy complicating non-diabetic renal disease among Chinese patients with type 2 diabetes mellitus. Eur J Med Res 2013;18:4.\u003c/li\u003e\n\u003cli\u003eKimmelstiel P, Wilson C. Intercapillary lesions in the glomeruli of the kidney.Am J Pathol (1936) 12:83\u0026ndash;98.7.\u003c/li\u003e\n\u003cli\u003eZeni L, Norden AGW, Cancarini G, Unwin RJ. A more tubulocentric view of diabetic kidney disease. J Nephrol (2017) 30:701\u0026ndash;17. doi: 10.1007/s40620-017-0423-9\u003c/li\u003e\n\u003cli\u003eThomas MC, Brownlee M, Susztak K, Sharma K, Jandeleit-Dahm KAM,Zoungas S, et al. Diabetic kidney disease. Nat Rev Dis Primers (2015) 1:15018.doi: 10.1038/nrdp.2015.18\u003c/li\u003e\n\u003cli\u003eGilbert RE. Proximal tubulopathy: prime mover and key therapeutic target in diabetic kidney disease. Diabetes (2017) 66:791\u0026ndash;800. doi: 10.2337/db16-0796\u003c/li\u003e\n\u003cli\u003eNihalani D, Susztak K. Sirt1\u0026ndash;Claudin-1 crosstalk regulates renal function. Nat Med (2013) 19:1371\u0026ndash;2. doi: 10.1038/nm.3386\u003c/li\u003e\n\u003cli\u003eBonventre JV. Can we target tubular damage to prevent renal function decline in diabetes? Semin Nephrol (2012) 32:452\u0026ndash;62. doi: 10.1016/j.semnephrol.2012.07.008\u003c/li\u003e\n\u003cli\u003eBohle A, Wehrmann M, Bogensch\u0026uuml;tz O, Batz C, M\u0026uuml;ller GA, M\u0026uuml;ller GA. The pathogenesis of chronic renal failure in diabetic nephropathy. Pathol - Res Pract (1991) 187:251\u0026ndash;9. doi: 10.1016/S0344-0338(11)80780-6\u003c/li\u003e\n\u003cli\u003eChen J, Liu Q, He J, Li Y. Immune responses in diabetic nephropathy: Pathogenic mechanisms and therapeutic target. Front Immunol (2022) 13:958790. doi: 10.3389/fimmu.2022.958790\u003c/li\u003e\n\u003cli\u003eTesch GH. Diabetic nephropathy \u0026ndash; is this an immune disorder? Clin Sci (2017) 131:2183\u0026ndash;99. doi: 10.1042/CS20160636\u003c/li\u003e\n\u003cli\u003eGilbert RE, Cooper ME. The tubulointerstitium in progressive diabetic kidney disease: More than an aftermath of glomerular injury? Kidney Int (1999) 56:1627\u0026ndash;37. doi: 10.1046/j.1523-1755.1999.00721.x\u003c/li\u003e\n\u003cli\u003eShen S, Ji C, Wei K. Cellular senescence and regulated cell death of tubular epithelial cells in diabetic kidney disease. Front Endocrinol (Lausanne) (2022) 13:924299. doi: 10.3389/fendo.2022.924299\u003c/li\u003e\n\u003cli\u003eDonate-Correa J, Luis-Rodr\u0026iacute;guez D, Mart\u0026iacute;n-N\u0026uacute;\u0026ntilde;ez E, Tagua VG, Hern\u0026aacute;ndez-Carballo C, Ferri C, et al. Inflammatory targets in diabetic nephropathy. JCM (2020) 9:458. doi: 10.3390/jcm9020458\u003c/li\u003e\n\u003cli\u003eTervaert TWC, Mooyaart AL, Amann K, Cohen AH, Cook HT, Drachenberg CB, et al. Pathologic classification of diabetic nephropathy. J Am Soc Nephrol (2010) 21:556\u0026ndash;63. doi: 10.1681/ASN.2010010010\u003c/li\u003e\n\u003cli\u003eD\u0026apos;Amico G, Bazzi C. Pathophysiology of proteinuria. Kidney Int. 2003 Mar;63(3):809-25. doi: 10.1046/j.1523-1755.2003.00840.x. PMID: 12631062.\u003c/li\u003e\n\u003cli\u003esai SF, Tarng DC. Anemia in patients of diabetic kidney disease. J Chin Med Assoc. 2019 Oct;82(10):752-755. doi: 10.1097/JCMA.0000000000000175. PMID: 31453863.\u003c/li\u003e\n\u003cli\u003ePonticelli C, Podest\u0026agrave; MA, Moroni G. Hyperuricemia as a trigger of immune response in hypertension and chronic kidney disease. Kidney Int. 2020 Nov;98(5):1149-1159. doi: 10.1016/j.kint.2020.05.056. Epub 2020 Jul 8. PMID: 32650020.\u003c/li\u003e\n\u003cli\u003eTaft JL,Billson VR,Nankervis A,Kincaid-Smith P,et al.A clini-cal histological study of individuals with diabetes mellitus and protein-uria.Diabet Med,1990,7(3):215-221.\u003c/li\u003e\n\u003cli\u003eTaft JL,Billson VR,Nankervis A,Kincaid-Smith P,et al.A clini-cal histological study of individuals with diabetes mellitus and protein-uria.Diabet Med,1990,7(3):215-221.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Diabetes mellitus, diabetic nephropathy, nondiabetic nephropathy, pathologic features, clinical features","lastPublishedDoi":"10.21203/rs.3.rs-4297672/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4297672/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBACKGROUND\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis study aimed to explore the clinical and pathological features of patients with diabetic kidney disease (DKD), with and without non-diabetic kidney disease (NDKD), through a retrospective analysis. The objective was to provide clinical insights for accurate identification.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMETHODS\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA retrospective analysis of 235 patients admitted to the Department of Nephrology at Hangzhou Hospital of Traditional Chinese Medicine was conducted between July 2014 and December 2022. These patients underwent renal biopsy and received a pathology-based diagnosis of DKD. They were categorized into the DKD group (93 cases) and the DKD\u0026thinsp;+\u0026thinsp;NDKD group (142 cases).\u003c/p\u003e\u003cp\u003e\u003cb\u003eRESULTS\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn the DKD group, nodular diabetic glomerulosclerosis was the most prevalent, accounting for 63% of cases. In the DKD\u0026thinsp;+\u0026thinsp;NDKD group, the predominant pathological types were coupled with acute and chronic tubulointerstitial lesions, and IgA nephropathy, accounting for 40.14% and 35.21%, respectively. Clinical correlation analysis revealed associations between glomerular grading, tubulointerstitial lesions, renal arteriolar vitelliform lesions, renal vascular atherosclerosis, and clinical parameters such as 24-hour urine protein, hemoglobin, and urinary specific gravity. Multifactorial logistic regression analysis identified independent factors affecting DKD\u0026thinsp;+\u0026thinsp;NDKD, including body mass index, blood creatinine level, microscopic erythrocyte grade, urinary immunoglobulin G/creatinine ratio, and serum immunoglobulin A.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCONCLUSION\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis study provides important insights into the pathological and clinical features of DKD and identifies independent factors associated with DKD\u0026thinsp;+\u0026thinsp;NDKD.\u003c/p\u003e","manuscriptTitle":"Characterization of Diabetic Kidney Disease in 235 Patients: Clinical and Pathological Insights with or without Concurrent Non-Diabetic Kidney Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-30 22:08:18","doi":"10.21203/rs.3.rs-4297672/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-14T07:17:42+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-01T05:59:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-01T12:21:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"253685226944940274616618913987870288288","date":"2024-07-01T12:00:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"290545956047853063242578705438478094212","date":"2024-07-01T08:03:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-01T07:57:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-18T12:54:23+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-04-25T19:17:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-24T19:46:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nephrology","date":"2024-04-20T13:22:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7048e1cb-062d-45a4-b259-f1fddebf3814","owner":[],"postedDate":"April 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-20T16:00:39+00:00","versionOfRecord":{"articleIdentity":"rs-4297672","link":"https://doi.org/10.1186/s12882-024-03931-1","journal":{"identity":"bmc-nephrology","isVorOnly":false,"title":"BMC Nephrology"},"publishedOn":"2025-01-17 15:57:16","publishedOnDateReadable":"January 17th, 2025"},"versionCreatedAt":"2024-04-30 22:08:18","video":"","vorDoi":"10.1186/s12882-024-03931-1","vorDoiUrl":"https://doi.org/10.1186/s12882-024-03931-1","workflowStages":[]},"version":"v1","identity":"rs-4297672","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4297672","identity":"rs-4297672","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}