Significance of intrarenal vascular lesions in Ig A nephropathy prognosis 

Significance of intrarenal vascular lesions in Ig A nephropathy prognosis | 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 Significance of intrarenal vascular lesions in Ig A nephropathy prognosis Hyeon Tae Yang, Tae In Park, Yong-Jin Kim, Mee-seon Kim, Sun-Hee Park, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4836375/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Oct, 2024 Read the published version in BMC Nephrology → Version 1 posted 13 You are reading this latest preprint version Abstract Background Immunoglobulin A (IgA) nephropathy is the predominant primary glomerulonephritis globally and remains a subject of active research with a focus on understanding its course and prognosis. Although vascular lesions are associated with IgAN, the current histopathological grading systems do not consider intrarenal vascular lesions when predicting patient prognosis. Therefore, this retrospective study, conducted at Kyungpook National University Hospital between October 2016 and December 2021, aimed to elucidate the significance of intrarenal vascular lesions in IgAN by comparing the clinical data of patients with and without such lesions. Methods Data of patients with biopsy-confirmed primary IgAN between October 2016 and June 2021 at Kyungpook National University Hospital (Daegu, South Korea) were collected, and their medical records were reviewed. All slides from these 138 cases were independently pathologically reviewed by two nephropathologists (Y. J. K. and M. S. K.) using light microscope. The vascular lesions included in this study were fibrous intimal thickening, arteriolar wall thickening, and arteriolar hyalinosis. All cases were reviewed according to the Oxford Classification of IgA Nephropathy (2016) and Haas classification. Results Of the 138 patients, 88 exhibited at least one intrarenal vascular lesion. Patients with arteriolar wall thickening demonstrated a reduced estimated glomerular filtration rate (eGFR), elevated serum creatinine level and urine protein-to-creatinine ratio, an increased proportion of global glomerulosclerosis, and a higher histologic grade of interstitial fibrosis and tubular atrophy at the time of biopsy. Conclusion Arteriolar wall thickening in IgAN are associated with reduced eGFR and global glomerulosclerosis. Moreover, reduced eGFR and global glomerulosclerosis are correlated with the progression to end-stage renal disease. Although the direct correlation between vascular lesions and end-stage renal disease is not entirely clear, a marginally significant association (log-rank test, p = 0.06) was observed with arterial wall thickening. This study suggests the potential importance of vascular lesions in the prognosis of IgAN, encouraging further investigation using larger cohort studies to establish a clearer association. Immunoglobulin A nephropathy prognosis intrarenal vascular lesion glomerular filtration rate Oxford Haas Figures Figure 1 Figure 2 Background Immunoglobulin A nephropathy (IgAN) is the most common primary glomerulonephritis worldwide [ 1 ]. Research on the course and prognosis of the disease is ongoing, and patient prognosis is currently predicted through histopathological grading. The Oxford and Haas classifications are the representative grading systems. The Oxford classification evaluates and grades biopsy specimens based on the following five categories: mesangial hypercellularity (M), segmental sclerosis (S), interstitial fibrosis/tubular atrophy (T), and crescents (C) [ 2 ]. These parameters are associated with a reduced estimated glomerular filtration rate (eGFR) or progression to end-stage renal disease. The Haas classification classifies specimens into five grades according to the ratio of glomerular sclerosis to glomerular hypercellularity (classes I to V) [ 3 ]. As the proportion of glomeruli with sclerosis or hypercellularity increases, patients tend towards end-stage renal disease. In one study, intrarenal arterial-arteriolar lesions were more frequently associated with IgAN than with non-IgA nephropathy [ 4 ]. In this study, the prevalence of intrarenal small artery and arteriolar lesions was 54.6% in patients with IgAN and 26.6% in patients without IgAN ( p < 0.01). However, the Oxford and Haas classifications do not consider intrarenal vascular lesions. Therefore, this study aimed to analyze these differences by comparing clinical data between patients with IgAN with intrarenal vascular lesions and patients with IgAN without intrarenal vascular lesions. We ultimately aimed to determine whether vascular lesions can be added to these classification methods. Methods Patients This study was approved by the Institutional Review Board (IRB) of Kyungpook National University Hospital, Daegu, Korea (IRB FILE No. KNUH 2023-12-017, 8 January 2024). As the study was retrospective, the IRB waived the need for written informed consent. Data of patients with biopsy-confirmed primary IgAN between October 2016 and June 2021 at Kyungpook National University Hospital (Daegu, South Korea) were collected, and their medical records were reviewed. Patients older than 18 years and those with more than seven glomeruli on biopsy were included. Of the 166 patients, 138 were included in the study. At the time of biopsy, we collected data, including age, sex, past medical history, initial blood pressure, CKD-EPI eGFR [ 5 ], serum creatinine, serum blood urea nitrogen (BUN), urine protein-to-creatinine ratio (UPCR), serum albumin, hemoglobin, platelet count, serum uric acid, and total cholesterol levels from electronic medical records. Histological evaluation All slides from the 138 cases were independently pathologically reviewed by two nephropathologists (Y. J. K. and M. S. K.) who were blinded to the patient information. Any discrepancies in the pathological diagnosis between the pathologists were resolved through re-evaluation of the cases until consensus. The vascular lesions included in this study were fibrous intimal thickening (Fig. 1 a), arteriolar wall thickening (Fig. 1 b), and arteriolar hyalinosis (Fig. 1 c). No cases with arteriolar fibrinoid necrosis, arteriolar thrombi, subintimal myxoid changes, or arteriolar onion skin changes were observed. Vascular lesions were scored using a two-tier system: no or mild change and moderate to severe change. Moderate to severe changes were considered positive, whereas no or mild changes were considered negative. All cases were reviewed according to the Oxford Classification of IgA Nephropathy 2016 and the Haas classification. Outcome Of the initial 138 patients, 35 were lost after follow-up, leaving 103 patients included in the survival analysis. The median follow-up time was 1442 days (range: 253–2529 days). Survival time was defined as the duration from the date of kidney biopsy until the date of the last follow-up or the occurrence of end-stage renal disease (eGFR < 15 mL/min/1.73 m²) requiring kidney replacement. Statistical analysis All analyses were conducted using R version 4.3.2, incorporating the "survival" and "Hmisc" packages from the R Foundation for Statistical Computing (Auckland, New Zealand). A p-value of p < 0.05 was considered statistically significant. Clinicopathological differences between the vascular lesion-positive and vascular lesion-negative groups were assessed using Fisher’s exact test and a two-sample t-test (Tables 1 , 2 , and 3 ). Survival analysis was performed using the log-rank test, Kaplan–Meier method (Fig. 2 ), and univariate and multivariate Cox proportional hazards models (Tables 4 and 5 ). The results were expressed as hazard ratios (HR) with 95% confidence intervals (CI). Results The cohort comprised 70 male and 68 female patients, with a male-to-female ratio of 1:0.97. The mean age was 41.1 years (range, 18–74 years). The mean systolic blood pressure was 126.6 mmHg, and 50 patients (36.2%) had a history of hypertension. The distributions across the Haas classification classes I, II, III, IV, and V were 30.4%, 21.7%, 35.5%, 6.5%, and 5.8%, respectively. The mean eGFR 79.8 ml/min per body surface area (BSA), and the mean UPCR was 1.6 mg/g. Clinical and pathologic features related to arteriolar wall thickening Clinical and pathologic features related to arteriolar wall thickening are summarized in Table 1 . Old age, a history of hypertension, high diastolic blood pressure, low eGFR, high UPCR, high serum creatinine, low albumin, high total cholesterol, and use of renin-angiotensin-aldosterone system (RAAS) blockers were associated with arteriolar wall thickening. In the Oxford classification, high S scores and T scores were associated with arteriolar wall thickening. Moreover, global glomerulosclerosis, high interstitial fibrosis and tubular atrophy (IFTA) grade, high inflammatory IFTA grade, and high total inflammation score were also associated with arteriolar wall thickening. Table 1 Clinicopathological characteristics according to arteriolar wall thickening Arteriolar wall thickening (+) Arteriolar wall thickening (-) p-value (n = 76) (n = 62) Clinical Information Gender (%) Male 37(48.7) 33(53.2) 0.6 Female 39(51.3) 29(46.8) Age (mean) 19–74 (46) 18–74 (35) 0.0000065* Hypertension (%) 34(44.7) 16(25.8) 0.03* DM (%) 5(6.6) 0(0) 0.06 Systolic BP, mm Hg (mean) 98–210 (128) 78–172 (124.8) 0.28 Diastolic BP, mm Hg (mean) 46–120 (76.7) 46–90 (72.1) 0.03* eGFR, ml/min/BSA (mean) 9-128 (65.7) 15–155 (97.2) 0.00000005* UPCR, mg/g (mean) 0.09–13.12 (2.17) 0.05–5.58 (1.07) 0.0009* BUN, mg/dL (mean) 6-64.1 (20.1) 5.8–96 (16.6) 0.08 Scr, µmol/L (mean) 0.5–5.12 (1.4) 0.49–5.49 (1.0) 0.004* Uric acid, µmol/L (mean) 3.1–11.4 (6.8) 3.5–11.3 (6.2) 0.055 Hemoglobin, g/L (mean) 8.4–17.1 (13.1) 4–19 (13.7) 0.1 Platelet count, ×109 /L (mean) 24–603 (254.6) 157–463 (265.6) 0.44 Albumin, g/L (mean) 1.9–5.1 (4.1) 2.7–5.4 (4.4) 0.003* Total cholesterol, mmol/L (mean) 120–360 (207) 101–243 (171) 0.000005* Medications RAAS blockers (%) 47(61.8) 15(24.2) 0.00001* Steroids and other immunosuppressants (%) 1(1.3) 1(1.6) 1 Oxford classification score M0/1(%) 70(92.1)/6(7.9) 56(90.3)/6(9.3) 0.77 E0/1(%) 61(80.3)/15(19.7) 56(90.3)/6(9.7) 0.15 S0/1(%) 24(31.6)/52(68.4) 31(50)/31(50) 0.036* T0/1/2(%) 35(46.1)/19(25)/22(28.9) 56(90.3)/4(6.5)/2(3.2) 0.00000007* C0/C1/C2(%) 51(67.1)/23(30.3)/2(2.6) 42(67.7)/20(32.3)/0(0) 0.63 Pathological Features Global glomerulosclerosis, % (mean) 0-96.3 (28.2) 0-63.6 (9.9) 0.0000009* Haas I/II/III/IV/V (%) 17(22.4)/16(21.1)/28(36.8)/7(9.2)/8(10.5) 25(40.3)/14(22.6)/21(33.9)/2(3.2)/0(0) 0.01 IFTA 0/1/2/3(%) 14(18.4)/23(30.3)/18(23.7)/21(27.6) 39(62.9)/15(24.2)/5(8.1)/3(4.8) 0.0000001* I IFTA 0/1/2/3(%) 17(22.4)/23(30.3)/24(31.6)/12(15.8) 41(66.1)/15(24.2)/3(4.8)/3(4.8) 0.0000002* Ti 0/1/2/3(%) 18(23.7)/32(42.1)/11(14.5)/15(19.7) 42(67.7)/16(25.8)/2(3.2)/2(3.2) 0.0000006* BP, blood pressure; eGFR, estimated glomerular filtration rate; BSA, body surface area; UPCR, urine protein-to-creatinine ratio; BUN, blood urea nitrogen; Scr, serum creatinine; RAAS renin-angiotensin-aldosterone system; IFTA, interstitial fibrosis and tubular atrophy. I IFTA, inflammatory IFTA; Oxford classification: M, mesangial hypercellularity; S, segmental sclerosis; T, interstitial fibrosis/tubular atrophy; C, and crescents. Clinical and pathological features related to fibrous intimal thickening The clinical and pathological features related to fibrous intimal thickening are summarized in (Table 2 ). Five people were excluded because of the absence of available vessels. Similarly, old age, low eGFR, high UPCR, low albumin, and high total cholesterol were related to fibrous intimal thickening. In the Oxford classification, a high T-score was observed. Moreover, global glomerulosclerosis, high IFTA grade, and total inflammation score were related to fibrous intimal thickening. Table 2 Clinicopathological characteristics according to fibrous intimal thickening Intimal thickening (+) Intimal thickening (-) p-value (n = 47) (n = 86) Clinical Information Gender (%) Male 21(44.7) 47(54.7) 0.28 Female 26(55.3) 39(45.3) Age (mean) 24–74 (49) 18–73 (37) 0.000004* Hypertension (%) 21(44.7) 27(31.4) 0.14 DM (%) 1(2.1) 3(3.5) 1 Systolic BP, mm Hg (mean) 100–210 (130.1) 78–172 (125.2) 0.11 Diastolic BP, mm Hg (mean) 55–110 (77.1) 46–120 (73.7) 0.12 eGFR, ml/min/BSA (mean) 9-120 (64.6) 9-141 (88) 0.0002* UPCR, mg/g (mean) 0.2–8.18 (2.19) 0.05–9.81 (1.29) 0.003* BUN, mg/dL (mean) 10.3–64.1 (20.4) 6–96 (17.5) 0.2 Scr, µmol/L (mean) 0.5–4.92 (1.37) 0.55–5.49 (1.18) 0.2 Uric acid, µmol/L (mean) 4.4–11.4 (7) 3.1–11.3 (6.3) 0.06 Hemoglobin, g/L (mean) 8.7–17.1 (12.9) 4–19 (13.6) 0.07 Platelet count, ×109 /L (mean) 149–603 (273.2) 24–464 (250.3) 0.13 Albumin, g/L (mean) 2.1-5 (4) 2.7–5.4 (4.3) 0.006* Total cholesterol, mmol/L (mean) 138–360 (207) 101–328 (182) 0.003* Medications RAAS blockers (%) 24(51.1) 38(44.2) 0.47 Steroids and immunosuppressants (%) 1(2.1) 1(1.2) 1 Oxford classification score M0/1(%) 42(89.4)/5(10.6) 80(93)/6(7) 0.52 E0/1(%) 40(85.1)/7(14.9) 73(84.9)/13(15.1) 1 S0/1(%) 16(34)/31(66) 36(41.9)/50(58.1) 0.46 T0/1/2(%) 22(46.8)/11(23.4)/14(29.8) 67(77.9)/10(11.6)/9(10.5) 0.001* C0/C1/C2(%) 29(61.7)/16(34)/2(4.3) 59(68.6)/27(31.4)/0(0) 0.17 Pathological Features Global glomerulosclerosis, % (mean) 0-96.3 (26.3) 0–90 (16.2) 0.01* Haas I/II/III/IV/V(%) 10(21.3)/12(25.5)/18(38.3)/3(6.4)/4(8.5) 30(34.9)/16(18.6)/31(36)/5(5.8)/4(4.7) 0.47 IFTA 0/1/2/3(%) 8(17)/15(31.9)/9(19.1)/15(31.9) 44(51.2)/22(25.6)/12(14)/8(9.3) 0.0002* I IFTA 0/1/2/3(%) 13(27.7)/16(34)/10(21.3)/8(17) 43(50)/21(24.4)/15(17.4)/7(8.1) 0.066 Ti 0/1/2/3(%) 13(27.7)/17(36.2)/6(12.7)/11(23.4) 45(52.3)/30(34.9)/5(5.8)/6(7) 0.006* BP, blood pressure; eGFR, estimated glomerular filtration rate; BSA, body surface area; UPCR, urine protein-to-creatinine ratio; BUN, blood urea nitrogen; Scr, serum creatinine; RAAS renin-angiotensin-aldosterone system; IFTA, interstitial fibrosis and tubular atrophy. I IFTA, inflammatory IFTA; Oxford classification: M, mesangial hypercellularity; S, segmental sclerosis; T, interstitial fibrosis/tubular atrophy; C, and crescents. Clinical and pathological features according to any vascular lesions The clinical and pathological features according to any vascular lesions are summarized in Table 3 . If the specimen exhibited at least one vascular lesion (arteriolar wall thickening, arteriolar hyalinosis, or fibrous intimal thickening), it was categorized as a vascular lesion (+). In the cohort, 88 specimens had vascular lesions and 50 had no vascular lesions. The vascular lesion (+) group was associated with old age, a history of hypertension, high diastolic blood pressure, low eGFR, high UPCR, high BUN, high serum creatinine, high uric acid, low albumin, high total cholesterol, and the use of renin-angiotensin-aldosterone system (RAAS) blockers. Specimens with vascular lesions had a high T-score in the Oxford classification. Moreover, global glomerulosclerosis, high IFTA grade, high inflammatory IFTA grade, and high total inflammation score were associated with vascular lesions. Table 3 Clinicopathologic characteristics according to vascular lesions Vascular Lesions (+) Vascular Lesions (-) p-value (n = 88) (n = 50) Clinical Information Gender (%) Male 42(47.7) 28(56) 0.38 Female 46(52.3) 22(44) Age (mean) 19–74 (46) 18–73 (32.5) 0.00000005* Hypertension (%) 40(45.5) 10(20) 0.003* DM (%) 5(5.7) 0(0) 0.16 Systolic BP, mm Hg (mean) 98–210 (127.8) 78–172 (124.5) 0.28 Diastolic BP, mm Hg (mean) 46–120 (76.4) 46–90 (71.6) 0.02* eGFR, ml/min/BSA (mean) 9-135 (66.8) 15–155 (102.8) 0.000000001* UPCR, mg/g (mean) 0.09–13.12 (2.0) 0.05–5.58 (1.0) 0.004* BUN, mg/dL (mean) 6–96 (20.7) 5.8–62.3 (14.8) 0.005* Scr, µmol/L (mean) 0.5–5.12 (1.39) 0.49–5.49 (1) 0.01* Uric acid, µmol/L (mean) 3.1–11.4 (6.9) 3.5–11.3 (6) 0.009* Hemoglobin, g/L (mean) 8.4–17.1 (13.2) 4–19 (13.7) 0.12 Platelet count, ×109 /L (mean) 24–603 (257.7) 157–462 (262.8) 0.7 Albumin, g/L (mean) 1.9–5.1 (4.1) 2.7–5.4 (4.4) 0.008* Total cholesterol, mmol/L (mean) 101–360 (202.4) 102–243 (170.1) 0.00009* Medications RAAS blockers (%) 48(54.5) 14(28) 0.004* Steroids and immunosuppressants (%) 2(2.3) 0(0) 0.5 Oxford classification score M0/1(%) 80(90.9)/8(9.1) 46(92)/4(8) 1 E0/1(%) 73(83)/15(17) 44(88)/6(12) 0.47 S0/1(%) 31(35.2)/57(64.8) 24(48)/26(52) 0.15 T0/1/2(%) 44(50)/21(23.9)/23(26.1) 47(94)/2(4)/1(2) 0.0000002* C0/C1/C2(%) 60(68.2)/26(29.5)/2(2.3) 33(66)/17(34)/0(0) 0.62 Pathological Features Global glomerulosclerosis, % (mean) 0-96.3 (26.8) 0-51.9 (8) 0.000001* Haas I/II/III/IV/V (%) 21(23.9)/21(23.9)/31(35.2)/7(8)/8(9) 21(42)/9(18)/18(36)/2(4)/0(0) 0.049 IFTA 0/1/2/3(%) 17(19.3)/29(33)/19(21.6)/23(26.1) 36(72)/9(18)/4(8)/1(2) 0.000000004* I IFTA 0/1/2/3(%) 22(25)/28(31.8)/24(27.3)/14(15.9) 36(72)/10(20)/3(6)/1(2) 0.0000004* Ti 0/1/2/3(%) 22(25)/38(43.2)/11(12.5)/17(19.3) 38(76)/10(20)/2(4)/0(0) 0.00000001* BP, blood pressure; eGFR, estimated glomerular filtration rate; BSA, body surface area; UPCR, urine protein-to-creatinine ratio; BUN, blood urea nitrogen; Scr, serum creatinine; RAAS renin-angiotensin-aldosterone system; IFTA, interstitial fibrosis and tubular atrophy. I IFTA, inflammatory IFTA; Oxford classification: M, mesangial hypercellularity; S, segmental sclerosis; T, interstitial fibrosis/tubular atrophy; C, and crescents. Vascular lesions and prognosis. Among the 138 enrolled patients, 103 patients had follow-up data (mean follow-up date: 1404.9 days), and during that period, nine patients reached the endpoint (the occurrence of end-stage renal disease, eGFR < 15 mL/min/1.73 m², requiring kidney replacement). The univariate Cox regression analysis showed that global glomerulosclerosis, low eGFR, high BUN, high serum creatinine, and low albumin were associated with eventual progression to end-stage renal disease (Table 4 ). Multivariate Cox hazard analysis also showed that global glomerulosclerosis and high serum creatinine levels were associated with progression to end-stage renal disease after adjustment for any vascular lesions, arteriolar wall thickening, and fibrous intimal thickening (Table 5 ). However, any vascular lesions did not show a direct relationship with the occurrence of end-stage renal disease (Fig. 2 a, Log-rank test: p = 0.2). Even when analyzed separately for arterial wall thickening and fibrous intimal thinkening, no significant relationship was found (Fig. 2 b and 2 c, log-rank test: p = 0.06 and p = 0.6, respectively). Table 4 Univariate hazards model of survival according to clinicopathological characteristics Univariate hazard model HR (95% CI) p-value Vascular changes Any vascular lesions 3.89 (0.49–31.09) 0.2 Arteriolar wall thickening 5.76 (0.72–46.2) 0.1 Fibrous intimal thickening 1.37 (0.37–5.15) 0.6 Pathological Features Global glomerulosclerosis 1.05 (1.03–1.07) 0.00006* Clinical Information Age 1.05 (0.99–1.1) 0.067 eGFR 0.93 (0.9–0.97) 0.0004* UPCR 1.15 (0.96–1.37) 0.13 BUN 1.14 (1.08–1.21) 0.000003* Serum creatinine 3.264 (2.12–5.03) 0.00000009* Albumin 0.37 (0.17–0.81) 0.013* HR, hazard ratio; CI, confidence interval; eGFR, estimated glomerular filtration rate; UPCR, urine protein-to-creatinine ratio; BUN, blood urea nitrogen. Table 5 Multivariate hazards model of survival according to clinicopathological characteristics Multivariate hazard model HR (95% CI) p-value HR (95% CI) p-value HR (95% CI) p-value Any vascular lesions Arteriolar wall thickening Fibrous intimal thickening 0.56 (0.03–9.57) 0.7 0.71 (0.05–10.17) 0.8 0.87 (0.036–21.04) 0.9 Global glomerulosclerosis 1.06 (1.02–1.1) 0.0018* 1.06 (1.02–1.100) 0.0025* 1.07 (1.01–1.13) 0.02* eGFR 0.97 (0.93–1.01) 0.1 0.97 (0.93–1.01) 0.1 0.95 (0.89–1.02) 0.18 BUN 1.02 (0.9–1.15) 0.75 1.02 (0.91–1.16) 0.7 1.02 (0.88–1.18) 0.8 Serum creatinine 4.39 (1.43–13.47) 0.01* 4.38 (1.41–13.62) 0.01* 6.31 (1.43–27.78) 0.015* HR, hazard ratio; CI, confidence interval; eGFR, estimated glomerular filtration rate; UPCR, urine protein-to-creatinine ratio; BUN, blood urea nitrogen. Discussion Vascular lesions are frequently observed in renal biopsy specimens from patients with IgAN. However, the prognostic significance of these lesions remains unclear. Our study indicates that arterial wall thickening and fibrous intimal thickening are associated with a reduced eGFR and increased UPCR. These lesions also correlated with the presence of global sclerosis and tubular atrophy, consistent with the findings of other studies [ 6 – 9 ]. Notably, global sclerosis and high serum creatinine levels were observed and eventually lead to patients requiring dialysis, thereby suggesting the potential involvement of vasculopathies in the progression or prognosis of IgAN. Although our study was cross-sectional, the presence of these lesions implies the need for an aggressive treatment approach in affected patients. The results indicate that arterial wall thickening and fibrous intimal thickening are related to hypertension, and decreased eGFR aligns with the findings of other studies [ 10 – 13 ]. While this association was also observed in the original Oxford cohort, it did not exhibit a significant relationship with kidney failure events, and as a result, vascular lesions were not included in the classification criteria. In our study, vascular lesions did not show a significant relationship with the occurrence of end-stage renal disease (Fig. 2 a, log-rank test: p = 0.2). Even when analyzed separately for arterial wall thickening and fibrous intimal thickening, no significant relationship was found (Fig. 2 b and 2 c, log-rank test: p = 0.06 and p = 0.6, respectively). However, a case-control study by Huang et al. reported a significant relationship between the presence of vascular lesions and the transition to end-stage renal disease or death in patients with IgAN [ 13 ]. The discrepancy in the results may be attributed to the relatively small number of cases in our study, which resulted in a limited number of endpoint events. Nonetheless, in our study, arterial wall thickening showed a marginally significant correlation with the endpoint (p-value = 0.06), and we hypothesized that similar results could be obtained with an increased sample size and comprehensive eGFR follow-up data. The current treatment approach for IgAN aims to decelerate renal damage progression, encompassing blood pressure and proteinuria control, through the use of RAAS blockade. In cases where serum creatinine levels are elevated and biopsy reveals features, such as endocapillary hypercellularity or crescents, or if there is proteinuria in the nephrotic range, immunosuppressors, such as steroids, may be employed [ 14 ]. In our study, individuals with vascular lesions had a high prevalence of hypertension. Arterial wall thickening and fibrous intimal thickening, both of which are associated with hypertension, are common vascular lesions. However, among individuals with vascular lesions, 45.5% were diagnosed with hypertension, leaving a substantial proportion without hypertension. This suggests that although high blood pressure may contribute to vascular lesions, it is also plausible that vascular lesions could lead to secondary hypertension. Further research on the impact of nonhypertensive intrarenal vascular lesions on the disease course could contribute to a more comprehensive understanding. If a robust correlation is established, interventions aimed at slowing the progression of IgAN, based on biopsy findings before the onset of proteinuria or hypertension, may become a viable treatment strategy. Conclusion Intrarenal vascular lesions in IgAN are linked to reduced eGFR and global glomerulosclerosis. Moreover, reduced eGFR and global glomerulosclerosis are correlated with the progression to end-stage renal disease. Although the direct correlation between vascular lesions and end-stage renal disease is not entirely clear, a marginally significant association (log-rank test, p = 0.06) was observed with arterial wall thickening. This study suggests the potential importance of vascular lesions in the prognosis of IgAN, encouraging further investigation using large cohort studies to establish a clarified association. Declarations Availability of data and materials The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. Competing interests The authors have no conflict of interests to declare . Ethics approval and consent to participate The study protocol was approved by Institutional Review Board (IRB) of Kyungpook National University Hospital, Daegu, Korea (IRB FILE No. KNUH 2023-12-017, 8 January 2024) and has been conducted in full accordance with the Declaration of Helsinki. The study was retrospective therefore the Institutional Review Board (IRB) of Kyungpook National University Hospital, Daegu, Korea (IRB FILE No. KNUH 2023-12-017, 8 January 2024) waived the need for written informed consent. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Funding None Authors' contributions Conception and design of study: Man-Hoon Han, Yong-Jin Kim Acquisition of data: Jeong-Hoon Lim, Sun-Hee Park Analysis and/or interpretation of data: Yuna Kang, DongJa Kim Drafting the manuscript: Hyeon Tae Yang, Mee-seon Kim Revising the manuscript critically for important intellectual content: Man-Hoon Han, Tae In Park , Hyeon Tae Yang Acknowledgements None References J.C. Rodrigues, M. Haas, H.N. Reich, IgA Nephropathy, Clin. J. Am. Soc. Nephrol. 12 (2017) 677–686. https://doi.org/10.2215/cjn.07420716. H. Trimarchi, J. Barratt, D.C. Cattran, H.T. Cook, R. Coppo, M. Haas, Z.H. Liu, I.S. Roberts, Y. Yuzawa, H. Zhang, J. Feehally, Oxford classification of IgA nephropathy 2016: an update from the IgA nephropathy classification working group. Kidney Int. 91 (2017) 1014–1021. https://doi.org/10.1016/j.kint.2017.02.003. M Haas, Histologic subclassification of IgA nephropathy: a clinicopathologic study of 244 cases, Am. J. Kidney Dis. 29 (1997) 829–842. https://doi.org/10.1016/s0272-6386(97)90456-x. J. Wu, X. Chen, Y. Xie, N. Yamanaka, S. Shi, D. Wu, S. Liu, G. Cai, Characteristics and risk factors of intrarenal arterial lesions in patients with IgA nephropathy. Nephrol. Dial. Transplant. 20 (2005) 719–727. https://doi.org/10.1093/ndt/gfh716. K. Matsushita, B.K. Mahmoodi, M. Woodward, J.R. Emberson, T.H. Jafar, S.H. Jee, K.R. Polkinghorne, A. Shankar, D.H. Smith, M. Tonelli, D.G. Warnock, C.P. Wen, J. Coresh, R.T Gansevoort, B.R. Hemmelgarn, A.S. Levey, Chronic Kidney Disease Prognosis C: Comparison of risk prediction using the CKD-EPI equation and the MDRD study equation for estimated glomerular filtration rate. JAMA. 307 (2012) 1941–1951. https://doi.org/10.1001/jama.2012.3954. Y. Zhang, L. Sun, S. Zhou, Q. Xu, Q. Xu, D. Liu, L. Liu, R. Hu, S. Quan, G. Xing, Intrarenal arterial lesions are associated with higher blood pressure, reduced renal function and poorer renal outcomes in patients with IgA nephropathy. Kidney Blood Press. Res. 43 (2018) 639–650. https://doi.org/10.1159/000489290. F.F. Chen, X.J. Yu, H. Wang, X. Zhang, Y. Tan, Z. Qu, S.X. Wang, F. Yu, M. Chen, M.H. Zhao, Clinical value of the renal pathologic scoring system in complement-mediated thrombotic microangiopathy. Ren. Fail. 45 (2023) 2161396. https://doi.org/10.1080/0886022x.2022.2161396. Q. Cai, S. Shi, S. Wang, Y. Ren, W. Hou, L. Liu, J. Lv, M. Haas, H. Zhang, Microangiopathic lesions in IgA nephropathy: A cohort study. Am. J. Kidney Dis. 74 (2019) 629–639. https://doi.org/10.1053/j.ajkd.2019.03.416. C.H. Zeng, W. Le, Z. Ni, M. Zhang, L. Miao, P. Luo, R. Wang, Z. Lv, J. Chen, J. Tian, N. Chen, X. Pan, P. Fu, Z. Hu, L. Wang, Q. Fan, H. Zheng, D. Zhang, Y. Wang, Y. Huo, H. Lin, S. Chen, S. Sun, Y. Wang, Z. Liu, D. Liu, L. Ma, T. Pan, A. Zhang, X. Jiang, C. Xing, B. Sun, Q. Zhou, W. Tang, F. Liu, Y. Liu, S. Liang, F. Xu, Q. Huang, H. Shen, J. Wang, Y. Shyr, S. Phillips, S. Troyanov, A. Fogo, Z-.H. Liu, A multicenter application and evaluation of the Oxford classification of IgA nephropathy in adult Chinese patients. Am. J. Kidney Dis. 60 (2012) 812–820. https://doi.org/10.1053/j.ajkd.2012.06.011. R. Coppo, S. Troyanov, S. Bellur, D. Cattran, H.T. Cook, J. Feehally, I.S.D Roberts, L. Morando, R. Camilla, V. Tesar, S. Lunberg, L.Gesualdo, F. Emma, C. Rollino, A. Amore, M. Praga, S. Feriozzi, G. Segoloni, A. Pani, G. Cancarini, M. Durlik, E. Moggia, G. Mazzucco, C. Giannakakis, E. Honsova, B.B. Sundelin, A.M. Di Palma, F. Ferrario, E. Gutierrez, A.M. Asunis, J. Barratt, R. Tardanico, A. Perkowska-Ptasinska VALIGA study of the ERA-EDTA Immunonephrology Working Group, Validation of the Oxford classification of IgA nephropathy in cohorts with different presentations and treatments. Kidney Int. 86 (2014) 828–836. https://doi.org/10.1038/ki.2014.63. A.M. Herzenberg, A.B. Fogo, H.N. Reich, S. Troyanov, N. Bavbek, A.E. Massat, T.E. Hunley, M.A. Hladunewich, B.A. Julian, F.C. Fervenza, D.C. Cattran, Validation of the Oxford classification of IgA nephropathy. Kidney Int. 80 (2011) 310–317. https://doi.org/10.1038/ki.2011.126. S.F. Shi, S.X. Wang, L. Jiang, Ji-.C. Lv, Li-.J. Liu, Yu-.Q. Chen, S-.N. Zhu, G. Liu, W-.Z. Zou, H. Zhang, H-.Y. Wang, Pathologic predictors of renal outcome and therapeutic efficacy in IgA nephropathy: Validation of the Oxford classification. Clin. J. Am. Soc. Nephrol. 6 (2011) 2175–2184. https://doi.org/10.2215/cjn.11521210. Z. Huang, Y. Hu, B. Chen, Y. Liang, D. Li, W. Qiu, J. Zhang, C. Chen, Clinical significance of intrarenal vascular lesions in non-hypertensive patients with IgA nephropathy. J. Nephrol. 36 (2023) 429–440. https://doi.org/10.1007/s40620-022-01511-w. Kidney Disease: Improving Global Outcomes (KDIGO) Glomerular Diseases Work Group, KDIGO 2021 Clinical Practice Guideline for the Management of Glomerular Diseases. Kidney Int. 100 (2021) S1–S276. https://doi.org/10.1016/j.kint.2021.05.021. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 16 Oct, 2024 Read the published version in BMC Nephrology → Version 1 posted Editorial decision: Revision requested 23 Aug, 2024 Reviews received at journal 21 Aug, 2024 Reviews received at journal 21 Aug, 2024 Reviewers agreed at journal 14 Aug, 2024 Reviews received at journal 14 Aug, 2024 Reviewers agreed at journal 13 Aug, 2024 Reviewers agreed at journal 11 Aug, 2024 Reviewers agreed at journal 11 Aug, 2024 Reviewers invited by journal 11 Aug, 2024 Editor invited by journal 08 Aug, 2024 Editor assigned by journal 08 Aug, 2024 Submission checks completed at journal 08 Aug, 2024 First submitted to journal 31 Jul, 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. 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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-4836375","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":344053328,"identity":"7a2bfd22-b73b-4dfc-8adb-35bd44868d93","order_by":0,"name":"Hyeon Tae Yang","email":"","orcid":"","institution":"Kyungpook National University, Kyungpook National University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hyeon","middleName":"Tae","lastName":"Yang","suffix":""},{"id":344053329,"identity":"086f6cde-6dbd-4e29-a035-9edc5951d2a2","order_by":1,"name":"Tae In Park","email":"","orcid":"","institution":"Kyungpook National University, Kyungpook National University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Tae","middleName":"In","lastName":"Park","suffix":""},{"id":344053330,"identity":"8d050897-9cb2-4890-94ac-e0bea6e60e52","order_by":2,"name":"Yong-Jin Kim","email":"","orcid":"","institution":"Kyungpook National University, Kyungpook National University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yong-Jin","middleName":"","lastName":"Kim","suffix":""},{"id":344053331,"identity":"3eb84c8c-c3ad-4c4c-bb70-001aef7277d1","order_by":3,"name":"Mee-seon Kim","email":"","orcid":"","institution":"Kyungpook National University, Kyungpook National University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mee-seon","middleName":"","lastName":"Kim","suffix":""},{"id":344053332,"identity":"a25ae204-541d-41ef-aa49-164ad484021c","order_by":4,"name":"Sun-Hee Park","email":"","orcid":"","institution":"Kyungpook National University, Kyungpook National University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Sun-Hee","middleName":"","lastName":"Park","suffix":""},{"id":344053333,"identity":"e6d15741-dd76-4232-810a-bd06de79035b","order_by":5,"name":"Jeong-Hoon Lim","email":"","orcid":"","institution":"Kyungpook National University, Kyungpook National University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jeong-Hoon","middleName":"","lastName":"Lim","suffix":""},{"id":344053334,"identity":"da0a3fd4-97dd-48c8-877f-5f75b3a394fd","order_by":6,"name":"Yuna Kang","email":"","orcid":"","institution":"Kyungpook National University, Kyungpook National University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yuna","middleName":"","lastName":"Kang","suffix":""},{"id":344053335,"identity":"a5307a48-725e-445a-bc22-4f2cf481b9f7","order_by":7,"name":"DongJa Kim","email":"","orcid":"","institution":"Kyungpook National University, Kyungpook National University Hospital","correspondingAuthor":false,"prefix":"","firstName":"DongJa","middleName":"","lastName":"Kim","suffix":""},{"id":344053336,"identity":"00304e0e-fd16-4d93-bfce-4dd050440fe4","order_by":8,"name":"Man-Hoon Han","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYHACNiCW4OGXP3wAxJAhVouNjOQMtgSwXmK1pNkY3OAxAPEIa9FtP3vsMe+OwzwMt3s+v7pRY8HDwH746AZ8WszO5KUb8545zMM45+w265xjQIfxpKXdwKvlQI6ZNG/bYR5mhtxtxjlsQC0SPGb4tZx/A9HCxpDzzDjnHzFaboBtSePhkchhfpzbRpSWN2aSc9tseCR4jpkx5/ZJ8LAR9Mv5HDOJt20S9vbHmx9/zvlWJ8fPfvgYXi3IgE0CTBKrHASYP5CiehSMglEwCkYOAAA+TENhUviSogAAAABJRU5ErkJggg==","orcid":"","institution":"Kyungpook National University, Kyungpook National University Hospital","correspondingAuthor":true,"prefix":"","firstName":"Man-Hoon","middleName":"","lastName":"Han","suffix":""}],"badges":[],"createdAt":"2024-07-31 14:33:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4836375/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4836375/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12882-024-03803-8","type":"published","date":"2024-10-16T15:56:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":65787574,"identity":"ff82cf75-5b14-4c8f-ae06-06e7a490483e","added_by":"auto","created_at":"2024-10-02 16:41:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2402171,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative cases of vascular lesions. (\u003cstrong\u003ea\u003c/strong\u003e) Fibrous intimal thickening (modified elastic tissue-Masson trichrome stain, x400). (\u003cstrong\u003eb\u003c/strong\u003e) Arteriolar wall thickening (modified elastic tissue-Masson trichrome stain, x400). (\u003cstrong\u003ec\u003c/strong\u003e) Arteriolar hyalinosis (periodic acid–Schiff stain, x400).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4836375/v1/db0d4e1ba0b1e8a25416c32f.png"},{"id":65787572,"identity":"9c94a7f4-839f-46ca-896e-d8d8eba1ca6f","added_by":"auto","created_at":"2024-10-02 16:41:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":117359,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier curves of Immunoglobulin A nephropathy (IgA) according to vascular lesions. (\u003cstrong\u003ea\u003c/strong\u003e) vascular lesions, (\u003cstrong\u003eb\u003c/strong\u003e) arteriolar wall thickening, (\u003cstrong\u003ec\u003c/strong\u003e) fibrous intimal thickening.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4836375/v1/2876566d70730eea3f02b62c.png"},{"id":67148670,"identity":"3c29f553-5fdd-49a1-b500-43dc6591fa29","added_by":"auto","created_at":"2024-10-21 16:04:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4142061,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4836375/v1/e533a83a-c5dd-4292-8dbf-aba037ede0b4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Significance of intrarenal vascular lesions in Ig A nephropathy prognosis ","fulltext":[{"header":"Background","content":"\u003cp\u003eImmunoglobulin A nephropathy (IgAN) is the most common primary glomerulonephritis worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Research on the course and prognosis of the disease is ongoing, and patient prognosis is currently predicted through histopathological grading. The Oxford and Haas classifications are the representative grading systems. The Oxford classification evaluates and grades biopsy specimens based on the following five categories: mesangial hypercellularity (M), segmental sclerosis (S), interstitial fibrosis/tubular atrophy (T), and crescents (C) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These parameters are associated with a reduced estimated glomerular filtration rate (eGFR) or progression to end-stage renal disease. The Haas classification classifies specimens into five grades according to the ratio of glomerular sclerosis to glomerular hypercellularity (classes I to V) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. As the proportion of glomeruli with sclerosis or hypercellularity increases, patients tend towards end-stage renal disease. In one study, intrarenal arterial-arteriolar lesions were more frequently associated with IgAN than with non-IgA nephropathy [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In this study, the prevalence of intrarenal small artery and arteriolar lesions was 54.6% in patients with IgAN and 26.6% in patients without IgAN (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). However, the Oxford and Haas classifications do not consider intrarenal vascular lesions. Therefore, this study aimed to analyze these differences by comparing clinical data between patients with IgAN with intrarenal vascular lesions and patients with IgAN without intrarenal vascular lesions. We ultimately aimed to determine whether vascular lesions can be added to these classification methods.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003e This study was approved by the Institutional Review Board (IRB) of Kyungpook National University Hospital, Daegu, Korea (IRB FILE No. KNUH 2023-12-017, 8 January 2024). As the study was retrospective, the IRB waived the need for written informed consent. Data of patients with biopsy-confirmed primary IgAN between October 2016 and June 2021 at Kyungpook National University Hospital (Daegu, South Korea) were collected, and their medical records were reviewed. Patients older than 18 years and those with more than seven glomeruli on biopsy were included. Of the 166 patients, 138 were included in the study. At the time of biopsy, we collected data, including age, sex, past medical history, initial blood pressure, CKD-EPI eGFR [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], serum creatinine, serum blood urea nitrogen (BUN), urine protein-to-creatinine ratio (UPCR), serum albumin, hemoglobin, platelet count, serum uric acid, and total cholesterol levels from electronic medical records.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eHistological evaluation\u003c/h2\u003e \u003cp\u003eAll slides from the 138 cases were independently pathologically reviewed by two nephropathologists (Y. J. K. and M. S. K.) who were blinded to the patient information. Any discrepancies in the pathological diagnosis between the pathologists were resolved through re-evaluation of the cases until consensus. The vascular lesions included in this study were fibrous intimal thickening (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea), arteriolar wall thickening (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb), and arteriolar hyalinosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). No cases with arteriolar fibrinoid necrosis, arteriolar thrombi, subintimal myxoid changes, or arteriolar onion skin changes were observed. Vascular lesions were scored using a two-tier system: no or mild change and moderate to severe change. Moderate to severe changes were considered positive, whereas no or mild changes were considered negative. All cases were reviewed according to the Oxford Classification of IgA Nephropathy 2016 and the Haas classification.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eOutcome\u003c/h2\u003e \u003cp\u003eOf the initial 138 patients, 35 were lost after follow-up, leaving 103 patients included in the survival analysis. The median follow-up time was 1442 days (range: 253\u0026ndash;2529 days). Survival time was defined as the duration from the date of kidney biopsy until the date of the last follow-up or the occurrence of end-stage renal disease (eGFR\u0026thinsp;\u0026lt;\u0026thinsp;15 mL/min/1.73 m\u0026sup2;) requiring kidney replacement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll analyses were conducted using R version 4.3.2, incorporating the \"survival\" and \"Hmisc\" packages from the R Foundation for Statistical Computing (Auckland, New Zealand). A p-value of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Clinicopathological differences between the vascular lesion-positive and vascular lesion-negative groups were assessed using Fisher\u0026rsquo;s exact test and a two-sample t-test (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Survival analysis was performed using the log-rank test, Kaplan\u0026ndash;Meier method (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), and univariate and multivariate Cox proportional hazards models (Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The results were expressed as hazard ratios (HR) with 95% confidence intervals (CI).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe cohort comprised 70 male and 68 female patients, with a male-to-female ratio of 1:0.97. The mean age was 41.1 years (range, 18\u0026ndash;74 years). The mean systolic blood pressure was 126.6 mmHg, and 50 patients (36.2%) had a history of hypertension. The distributions across the Haas classification classes I, II, III, IV, and V were 30.4%, 21.7%, 35.5%, 6.5%, and 5.8%, respectively. The mean eGFR 79.8 ml/min per body surface area (BSA), and the mean UPCR was 1.6 mg/g.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eClinical and pathologic features related to arteriolar wall thickening\u003c/h2\u003e \u003cp\u003eClinical and pathologic features related to arteriolar wall thickening are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Old age, a history of hypertension, high diastolic blood pressure, low eGFR, high UPCR, high serum creatinine, low albumin, high total cholesterol, and use of renin-angiotensin-aldosterone system (RAAS) blockers were associated with arteriolar wall thickening. In the Oxford classification, high S scores and T scores were associated with arteriolar wall thickening. Moreover, global glomerulosclerosis, high interstitial fibrosis and tubular atrophy (IFTA) grade, high inflammatory IFTA grade, and high total inflammation score were also associated with arteriolar wall thickening.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinicopathological characteristics according to arteriolar wall thickening\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eArteriolar wall thickening (+)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eArteriolar wall thickening (-)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical Information\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGender (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37(48.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e33(53.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39(51.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e29(46.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u0026ndash;74 (46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e18\u0026ndash;74 (35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.0000065*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHypertension (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34(44.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e16(25.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.03*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDM (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSystolic BP, mm Hg (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98\u0026ndash;210 (128)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e78\u0026ndash;172 (124.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDiastolic BP, mm Hg (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46\u0026ndash;120 (76.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e46\u0026ndash;90 (72.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.03*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eeGFR, ml/min/BSA (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9-128 (65.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u0026ndash;155 (97.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.00000005*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eUPCR, mg/g (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u0026ndash;13.12 (2.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.05\u0026ndash;5.58 (1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.0009*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBUN, mg/dL (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6-64.1 (20.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e5.8\u0026ndash;96 (16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eScr, \u0026micro;mol/L (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u0026ndash;5.12 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.49\u0026ndash;5.49 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.004*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eUric acid, \u0026micro;mol/L (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.1\u0026ndash;11.4 (6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e3.5\u0026ndash;11.3 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHemoglobin, g/L (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.4\u0026ndash;17.1 (13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e4\u0026ndash;19 (13.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePlatelet count, \u0026times;109 /L (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u0026ndash;603 (254.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e157\u0026ndash;463 (265.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAlbumin, g/L (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9\u0026ndash;5.1 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2.7\u0026ndash;5.4 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTotal cholesterol, mmol/L (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120\u0026ndash;360 (207)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e101\u0026ndash;243 (171)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.000005*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedications\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRAAS blockers (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47(61.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e15(24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.00001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSteroids and other immunosuppressants (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1(1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOxford classification score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eM0/1(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70(92.1)/6(7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e56(90.3)/6(9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eE0/1(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61(80.3)/15(19.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e56(90.3)/6(9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eS0/1(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24(31.6)/52(68.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e31(50)/31(50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.036*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eT0/1/2(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35(46.1)/19(25)/22(28.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e56(90.3)/4(6.5)/2(3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.00000007*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eC0/C1/C2(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51(67.1)/23(30.3)/2(2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e42(67.7)/20(32.3)/0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePathological Features\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGlobal glomerulosclerosis, % (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0-96.3 (28.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0-63.6 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.0000009*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHaas I/II/III/IV/V (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(22.4)/16(21.1)/28(36.8)/7(9.2)/8(10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e25(40.3)/14(22.6)/21(33.9)/2(3.2)/0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eIFTA 0/1/2/3(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(18.4)/23(30.3)/18(23.7)/21(27.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e39(62.9)/15(24.2)/5(8.1)/3(4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.0000001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eI IFTA 0/1/2/3(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(22.4)/23(30.3)/24(31.6)/12(15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e41(66.1)/15(24.2)/3(4.8)/3(4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.0000002*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTi 0/1/2/3(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(23.7)/32(42.1)/11(14.5)/15(19.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e42(67.7)/16(25.8)/2(3.2)/2(3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.0000006*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBP, blood pressure; eGFR, estimated glomerular filtration rate; BSA, body surface area; UPCR, urine protein-to-creatinine ratio; BUN, blood urea nitrogen; Scr, serum creatinine; RAAS renin-angiotensin-aldosterone system; IFTA, interstitial fibrosis and tubular atrophy. I IFTA, inflammatory IFTA; Oxford classification: M, mesangial hypercellularity; S, segmental sclerosis; T, interstitial fibrosis/tubular atrophy; C, and crescents.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eClinical and pathological features related to fibrous intimal thickening\u003c/h2\u003e \u003cp\u003eThe clinical and pathological features related to fibrous intimal thickening are summarized in (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Five people were excluded because of the absence of available vessels. Similarly, old age, low eGFR, high UPCR, low albumin, and high total cholesterol were related to fibrous intimal thickening. In the Oxford classification, a high T-score was observed. Moreover, global glomerulosclerosis, high IFTA grade, and total inflammation score were related to fibrous intimal thickening.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinicopathological characteristics according to fibrous intimal thickening\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntimal thickening (+)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntimal thickening (-)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical Information\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21(44.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47(54.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26(55.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39(45.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u0026ndash;74 (49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u0026ndash;73 (37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000004*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21(44.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27(31.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic BP, mm Hg (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u0026ndash;210 (130.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78\u0026ndash;172 (125.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic BP, mm Hg (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55\u0026ndash;110 (77.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46\u0026ndash;120 (73.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR, ml/min/BSA (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9-120 (64.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9-141 (88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0002*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUPCR, mg/g (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2\u0026ndash;8.18 (2.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u0026ndash;9.81 (1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN, mg/dL (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.3\u0026ndash;64.1 (20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u0026ndash;96 (17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScr, \u0026micro;mol/L (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5\u0026ndash;4.92 (1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.55\u0026ndash;5.49 (1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid, \u0026micro;mol/L (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.4\u0026ndash;11.4 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.1\u0026ndash;11.3 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin, g/L (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.7\u0026ndash;17.1 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u0026ndash;19 (13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet count, \u0026times;109 /L (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e149\u0026ndash;603 (273.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u0026ndash;464 (250.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin, g/L (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.1-5 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7\u0026ndash;5.4 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol, mmol/L (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138\u0026ndash;360 (207)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101\u0026ndash;328 (182)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedications\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRAAS blockers (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(51.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38(44.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSteroids and immunosuppressants (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOxford classification score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM0/1(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42(89.4)/5(10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80(93)/6(7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE0/1(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40(85.1)/7(14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73(84.9)/13(15.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS0/1(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16(34)/31(66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36(41.9)/50(58.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT0/1/2(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22(46.8)/11(23.4)/14(29.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67(77.9)/10(11.6)/9(10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC0/C1/C2(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29(61.7)/16(34)/2(4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59(68.6)/27(31.4)/0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePathological Features\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal glomerulosclerosis, % (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0-96.3 (26.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;90 (16.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaas I/II/III/IV/V(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10(21.3)/12(25.5)/18(38.3)/3(6.4)/4(8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30(34.9)/16(18.6)/31(36)/5(5.8)/4(4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIFTA 0/1/2/3(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(17)/15(31.9)/9(19.1)/15(31.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44(51.2)/22(25.6)/12(14)/8(9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0002*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI IFTA 0/1/2/3(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13(27.7)/16(34)/10(21.3)/8(17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43(50)/21(24.4)/15(17.4)/7(8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTi 0/1/2/3(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13(27.7)/17(36.2)/6(12.7)/11(23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45(52.3)/30(34.9)/5(5.8)/6(7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBP, blood pressure; eGFR, estimated glomerular filtration rate; BSA, body surface area; UPCR, urine protein-to-creatinine ratio; BUN, blood urea nitrogen; Scr, serum creatinine; RAAS renin-angiotensin-aldosterone system; IFTA, interstitial fibrosis and tubular atrophy. I IFTA, inflammatory IFTA; Oxford classification: M, mesangial hypercellularity; S, segmental sclerosis; T, interstitial fibrosis/tubular atrophy; C, and crescents.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eClinical and pathological features according to any vascular lesions\u003c/h2\u003e \u003cp\u003eThe clinical and pathological features according to any vascular lesions are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. If the specimen exhibited at least one vascular lesion (arteriolar wall thickening, arteriolar hyalinosis, or fibrous intimal thickening), it was categorized as a vascular lesion (+). In the cohort, 88 specimens had vascular lesions and 50 had no vascular lesions. The vascular lesion (+) group was associated with old age, a history of hypertension, high diastolic blood pressure, low eGFR, high UPCR, high BUN, high serum creatinine, high uric acid, low albumin, high total cholesterol, and the use of renin-angiotensin-aldosterone system (RAAS) blockers. Specimens with vascular lesions had a high T-score in the Oxford classification. Moreover, global glomerulosclerosis, high IFTA grade, high inflammatory IFTA grade, and high total inflammation score were associated with vascular lesions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinicopathologic characteristics according to vascular lesions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVascular Lesions (+)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVascular Lesions (-)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical Information\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42(47.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28(56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46(52.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u0026ndash;74 (46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u0026ndash;73 (32.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00000005*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40(45.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic BP, mm Hg (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98\u0026ndash;210 (127.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78\u0026ndash;172 (124.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic BP, mm Hg (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46\u0026ndash;120 (76.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46\u0026ndash;90 (71.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR, ml/min/BSA (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9-135 (66.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u0026ndash;155 (102.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000000001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUPCR, mg/g (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.09\u0026ndash;13.12 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u0026ndash;5.58 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN, mg/dL (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u0026ndash;96 (20.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.8\u0026ndash;62.3 (14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScr, \u0026micro;mol/L (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5\u0026ndash;5.12 (1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.49\u0026ndash;5.49 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid, \u0026micro;mol/L (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.1\u0026ndash;11.4 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5\u0026ndash;11.3 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin, g/L (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.4\u0026ndash;17.1 (13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u0026ndash;19 (13.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet count, \u0026times;109 /L (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u0026ndash;603 (257.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e157\u0026ndash;462 (262.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin, g/L (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.9\u0026ndash;5.1 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7\u0026ndash;5.4 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol, mmol/L (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101\u0026ndash;360 (202.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102\u0026ndash;243 (170.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00009*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedications\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRAAS blockers (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48(54.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSteroids and immunosuppressants (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOxford classification score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM0/1(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80(90.9)/8(9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46(92)/4(8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE0/1(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73(83)/15(17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44(88)/6(12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS0/1(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31(35.2)/57(64.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24(48)/26(52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT0/1/2(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44(50)/21(23.9)/23(26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47(94)/2(4)/1(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0000002*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC0/C1/C2(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60(68.2)/26(29.5)/2(2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33(66)/17(34)/0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePathological Features\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal glomerulosclerosis, % (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0-96.3 (26.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0-51.9 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaas I/II/III/IV/V (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21(23.9)/21(23.9)/31(35.2)/7(8)/8(9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21(42)/9(18)/18(36)/2(4)/0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIFTA 0/1/2/3(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17(19.3)/29(33)/19(21.6)/23(26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36(72)/9(18)/4(8)/1(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000000004*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI IFTA 0/1/2/3(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22(25)/28(31.8)/24(27.3)/14(15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36(72)/10(20)/3(6)/1(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0000004*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTi 0/1/2/3(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22(25)/38(43.2)/11(12.5)/17(19.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38(76)/10(20)/2(4)/0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00000001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBP, blood pressure; eGFR, estimated glomerular filtration rate; BSA, body surface area; UPCR, urine protein-to-creatinine ratio; BUN, blood urea nitrogen; Scr, serum creatinine; RAAS renin-angiotensin-aldosterone system; IFTA, interstitial fibrosis and tubular atrophy. I IFTA, inflammatory IFTA; Oxford classification: M, mesangial hypercellularity; S, segmental sclerosis; T, interstitial fibrosis/tubular atrophy; C, and crescents.\u003c/p\u003e \u003cp\u003e \u003cb\u003eVascular lesions and prognosis.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAmong the 138 enrolled patients, 103 patients had follow-up data (mean follow-up date: 1404.9 days), and during that period, nine patients reached the endpoint (the occurrence of end-stage renal disease, eGFR\u0026thinsp;\u0026lt;\u0026thinsp;15 mL/min/1.73 m\u0026sup2;, requiring kidney replacement). The univariate Cox regression analysis showed that global glomerulosclerosis, low eGFR, high BUN, high serum creatinine, and low albumin were associated with eventual progression to end-stage renal disease (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Multivariate Cox hazard analysis also showed that global glomerulosclerosis and high serum creatinine levels were associated with progression to end-stage renal disease after adjustment for any vascular lesions, arteriolar wall thickening, and fibrous intimal thickening (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). However, any vascular lesions did not show a direct relationship with the occurrence of end-stage renal disease (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, Log-rank test: p\u0026thinsp;=\u0026thinsp;0.2). Even when analyzed separately for arterial wall thickening and fibrous intimal thinkening, no significant relationship was found (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec, log-rank test: p\u0026thinsp;=\u0026thinsp;0.06 and p\u0026thinsp;=\u0026thinsp;0.6, respectively).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate hazards model of survival according to clinicopathological characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eUnivariate hazard model\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHR (95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVascular changes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny vascular lesions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.89 (0.49\u0026ndash;31.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArteriolar wall thickening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.76 (0.72\u0026ndash;46.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFibrous intimal thickening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.37 (0.37\u0026ndash;5.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePathological Features\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal glomerulosclerosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05 (1.03\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00006*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical Information\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05 (0.99\u0026ndash;1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.93 (0.9\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0004*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUPCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15 (0.96\u0026ndash;1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.14 (1.08\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000003*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum creatinine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.264 (2.12\u0026ndash;5.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00000009*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.37 (0.17\u0026ndash;0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.013*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHR, hazard ratio; CI, confidence interval; eGFR, estimated glomerular filtration rate; UPCR, urine protein-to-creatinine ratio; BUN, blood urea nitrogen.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate hazards model of survival according to clinicopathological characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eMultivariate hazard model\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHR (95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eHR (95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eHR (95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAny vascular lesions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eArteriolar wall thickening\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003eFibrous intimal thickening\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.56 (0.03\u0026ndash;9.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.71 (0.05\u0026ndash;10.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.87 (0.036\u0026ndash;21.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlobal glomerulosclerosis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.06 (1.02\u0026ndash;1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0018*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.06 (1.02\u0026ndash;1.100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0025*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.07 (1.01\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eeGFR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.97 (0.93\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97 (0.93\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.95 (0.89\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBUN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.02 (0.9\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02 (0.91\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.02 (0.88\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSerum creatinine\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.39 (1.43\u0026ndash;13.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.38 (1.41\u0026ndash;13.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.31 (1.43\u0026ndash;27.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.015*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHR, hazard ratio; CI, confidence interval; eGFR, estimated glomerular filtration rate; UPCR, urine protein-to-creatinine ratio; BUN, blood urea nitrogen.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eVascular lesions are frequently observed in renal biopsy specimens from patients with IgAN. However, the prognostic significance of these lesions remains unclear. Our study indicates that arterial wall thickening and fibrous intimal thickening are associated with a reduced eGFR and increased UPCR. These lesions also correlated with the presence of global sclerosis and tubular atrophy, consistent with the findings of other studies [\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Notably, global sclerosis and high serum creatinine levels were observed and eventually lead to patients requiring dialysis, thereby suggesting the potential involvement of vasculopathies in the progression or prognosis of IgAN. Although our study was cross-sectional, the presence of these lesions implies the need for an aggressive treatment approach in affected patients.\u003c/p\u003e \u003cp\u003eThe results indicate that arterial wall thickening and fibrous intimal thickening are related to hypertension, and decreased eGFR aligns with the findings of other studies [\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. While this association was also observed in the original Oxford cohort, it did not exhibit a significant relationship with kidney failure events, and as a result, vascular lesions were not included in the classification criteria. In our study, vascular lesions did not show a significant relationship with the occurrence of end-stage renal disease (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, log-rank test: p\u0026thinsp;=\u0026thinsp;0.2). Even when analyzed separately for arterial wall thickening and fibrous intimal thickening, no significant relationship was found (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec, log-rank test: p\u0026thinsp;=\u0026thinsp;0.06 and p\u0026thinsp;=\u0026thinsp;0.6, respectively). However, a case-control study by Huang et al. reported a significant relationship between the presence of vascular lesions and the transition to end-stage renal disease or death in patients with IgAN [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The discrepancy in the results may be attributed to the relatively small number of cases in our study, which resulted in a limited number of endpoint events. Nonetheless, in our study, arterial wall thickening showed a marginally significant correlation with the endpoint (p-value\u0026thinsp;=\u0026thinsp;0.06), and we hypothesized that similar results could be obtained with an increased sample size and comprehensive eGFR follow-up data.\u003c/p\u003e \u003cp\u003eThe current treatment approach for IgAN aims to decelerate renal damage progression, encompassing blood pressure and proteinuria control, through the use of RAAS blockade. In cases where serum creatinine levels are elevated and biopsy reveals features, such as endocapillary hypercellularity or crescents, or if there is proteinuria in the nephrotic range, immunosuppressors, such as steroids, may be employed [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In our study, individuals with vascular lesions had a high prevalence of hypertension. Arterial wall thickening and fibrous intimal thickening, both of which are associated with hypertension, are common vascular lesions. However, among individuals with vascular lesions, 45.5% were diagnosed with hypertension, leaving a substantial proportion without hypertension. This suggests that although high blood pressure may contribute to vascular lesions, it is also plausible that vascular lesions could lead to secondary hypertension. Further research on the impact of nonhypertensive intrarenal vascular lesions on the disease course could contribute to a more comprehensive understanding. If a robust correlation is established, interventions aimed at slowing the progression of IgAN, based on biopsy findings before the onset of proteinuria or hypertension, may become a viable treatment strategy.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIntrarenal vascular lesions in IgAN are linked to reduced eGFR and global glomerulosclerosis. Moreover, reduced eGFR and global glomerulosclerosis are correlated with the progression to end-stage renal disease. Although the direct correlation between vascular lesions and end-stage renal disease is not entirely clear, a marginally significant association (log-rank test, p\u0026thinsp;=\u0026thinsp;0.06) was observed with arterial wall thickening. This study suggests the potential importance of vascular lesions in the prognosis of IgAN, encouraging further investigation using large cohort studies to establish a clarified association.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflict of interests to declare\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was\u0026nbsp;approved by Institutional Review Board (IRB) of Kyungpook National University Hospital, Daegu, Korea (IRB FILE No. KNUH 2023-12-017, 8 January 2024)\u0026nbsp;and has been conducted in\u0026nbsp;full accordance with the Declaration of Helsinki. The study was retrospective therefore the Institutional Review Board (IRB) of Kyungpook National University Hospital, Daegu, Korea (IRB FILE No. KNUH 2023-12-017, 8 January 2024) waived the need for written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception and design of study: Man-Hoon Han, Yong-Jin Kim\u003c/p\u003e\n\u003cp\u003eAcquisition of data: Jeong-Hoon Lim, Sun-Hee Park\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnalysis and/or interpretation of data: Yuna Kang, DongJa Kim\u003c/p\u003e\n\u003cp\u003eDrafting the manuscript: Hyeon Tae Yang, Mee-seon Kim\u003c/p\u003e\n\u003cp\u003eRevising the manuscript critically for important intellectual content: Man-Hoon Han, Tae In Park , Hyeon Tae Yang\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJ.C. Rodrigues, M. Haas, H.N. Reich, IgA Nephropathy, Clin. J. Am. Soc. Nephrol. 12 (2017) 677\u0026ndash;686. https://doi.org/10.2215/cjn.07420716.\u003c/li\u003e\n\u003cli\u003eH. Trimarchi, J. Barratt, D.C. Cattran, H.T. Cook, R. Coppo, M. Haas, Z.H. Liu, I.S. Roberts, Y. Yuzawa, H. Zhang, J. Feehally, Oxford classification of IgA nephropathy 2016: an update from the IgA nephropathy classification working group. Kidney Int. 91 (2017) 1014\u0026ndash;1021. https://doi.org/10.1016/j.kint.2017.02.003.\u003c/li\u003e\n\u003cli\u003eM Haas, Histologic subclassification of IgA nephropathy: a clinicopathologic study of 244 cases, Am. J. Kidney Dis. 29 (1997) 829\u0026ndash;842. https://doi.org/10.1016/s0272-6386(97)90456-x. \u003c/li\u003e\n\u003cli\u003eJ. Wu, X. Chen, Y. Xie, N. Yamanaka, S. Shi, D. Wu, S. Liu, G. Cai, Characteristics and risk factors of intrarenal arterial lesions in patients with IgA nephropathy. Nephrol. Dial. Transplant. 20 (2005) 719\u0026ndash;727. https://doi.org/10.1093/ndt/gfh716.\u003c/li\u003e\n\u003cli\u003eK. Matsushita, B.K. Mahmoodi, M. Woodward, J.R. Emberson, T.H. Jafar, S.H. Jee, K.R. Polkinghorne, A. Shankar, D.H. Smith, M. Tonelli, D.G. Warnock, C.P. Wen, J. Coresh, R.T Gansevoort, B.R. Hemmelgarn, A.S. Levey, Chronic Kidney Disease Prognosis C: Comparison of risk prediction using the CKD-EPI equation and the MDRD study equation for estimated glomerular filtration rate. JAMA. 307 (2012) 1941\u0026ndash;1951. https://doi.org/10.1001/jama.2012.3954.\u003c/li\u003e\n\u003cli\u003eY. Zhang, L. Sun, S. Zhou, Q. Xu, Q. Xu, D. Liu, L. Liu, R. Hu, S. Quan, G. Xing, Intrarenal arterial lesions are associated with higher blood pressure, reduced renal function and poorer renal outcomes in patients with IgA nephropathy. Kidney Blood Press. Res. 43 (2018) 639\u0026ndash;650. https://doi.org/10.1159/000489290.\u003c/li\u003e\n\u003cli\u003eF.F. Chen, X.J. Yu, H. Wang, X. Zhang, Y. Tan, Z. Qu, S.X. Wang, F. Yu, M. Chen, M.H. Zhao, Clinical value of the renal pathologic scoring system in complement-mediated thrombotic microangiopathy. Ren. Fail. 45 (2023) 2161396. https://doi.org/10.1080/0886022x.2022.2161396.\u003c/li\u003e\n\u003cli\u003eQ. Cai, S. Shi, S. Wang, Y. Ren, W. Hou, L. Liu, J. Lv, M. Haas, H. Zhang, Microangiopathic lesions in IgA nephropathy: A cohort study. Am. J. Kidney Dis. 74 (2019) 629\u0026ndash;639. https://doi.org/10.1053/j.ajkd.2019.03.416.\u003c/li\u003e\n\u003cli\u003eC.H. Zeng, W. Le, Z. Ni, M. Zhang, L. Miao, P. Luo, R. Wang, Z. Lv, J. Chen, J. Tian, N. Chen, X. Pan, P. Fu, Z. Hu, L. Wang, Q. Fan, H. Zheng, D. Zhang, Y. Wang, Y. Huo, H. Lin, S. Chen, S. Sun, Y. Wang, Z. Liu, D. Liu, L. Ma, T. Pan, A. Zhang, X. Jiang, C. Xing, B. Sun, Q. Zhou, W. Tang, F. Liu, Y. Liu, S. Liang, F. Xu, Q. Huang, H. Shen, J. Wang, Y. Shyr, S. Phillips, S. Troyanov, A. Fogo, Z-.H. Liu, A multicenter application and evaluation of the Oxford classification of IgA nephropathy in adult Chinese patients. Am. J. Kidney Dis. 60 (2012) 812\u0026ndash;820. https://doi.org/10.1053/j.ajkd.2012.06.011.\u003c/li\u003e\n\u003cli\u003eR. Coppo, S. Troyanov, S. Bellur, D. Cattran, H.T. Cook, J. Feehally, I.S.D Roberts, L. Morando, R. Camilla, V. Tesar, S. Lunberg, L.Gesualdo, F. Emma, C. Rollino, A. Amore, M. Praga, S. Feriozzi, G. Segoloni, A. Pani, G. Cancarini, M. Durlik, E. Moggia, G. Mazzucco, C. Giannakakis, E. Honsova, B.B. Sundelin, A.M. Di Palma, F. Ferrario, E. Gutierrez, A.M. Asunis, J. Barratt, R. Tardanico, A. Perkowska-Ptasinska VALIGA study of the ERA-EDTA Immunonephrology Working Group, Validation of the Oxford classification of IgA nephropathy in cohorts with different presentations and treatments. Kidney Int. 86 (2014) 828\u0026ndash;836. https://doi.org/10.1038/ki.2014.63.\u003c/li\u003e\n\u003cli\u003eA.M. Herzenberg, A.B. Fogo, H.N. Reich, S. Troyanov, N. Bavbek, A.E. Massat, T.E. Hunley, M.A. Hladunewich, B.A. Julian, F.C. Fervenza, D.C. Cattran, Validation of the Oxford classification of IgA nephropathy. Kidney Int. 80 (2011) 310\u0026ndash;317. https://doi.org/10.1038/ki.2011.126.\u003c/li\u003e\n\u003cli\u003eS.F. Shi, S.X. Wang, L. Jiang, Ji-.C. Lv, Li-.J. Liu, Yu-.Q. Chen, S-.N. Zhu, G. Liu, W-.Z. Zou, H. Zhang, H-.Y. Wang, Pathologic predictors of renal outcome and therapeutic efficacy in IgA nephropathy: Validation of the Oxford classification. Clin. J. Am. Soc. Nephrol. 6 (2011) 2175\u0026ndash;2184. https://doi.org/10.2215/cjn.11521210.\u003c/li\u003e\n\u003cli\u003eZ. Huang, Y. Hu, B. Chen, Y. Liang, D. Li, W. Qiu, J. Zhang, C. Chen, Clinical significance of intrarenal vascular lesions in non-hypertensive patients with IgA nephropathy. J. Nephrol. 36 (2023) 429\u0026ndash;440. https://doi.org/10.1007/s40620-022-01511-w.\u003c/li\u003e\n\u003cli\u003eKidney Disease: Improving Global Outcomes (KDIGO) Glomerular Diseases Work Group, KDIGO 2021 Clinical Practice Guideline for the Management of Glomerular Diseases. Kidney Int. 100 (2021) S1\u0026ndash;S276. https://doi.org/10.1016/j.kint.2021.05.021.\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":"Immunoglobulin A nephropathy, prognosis, intrarenal vascular lesion, glomerular filtration rate, Oxford, Haas","lastPublishedDoi":"10.21203/rs.3.rs-4836375/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4836375/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eImmunoglobulin A (IgA) nephropathy is the predominant primary glomerulonephritis globally and remains a subject of active research with a focus on understanding its course and prognosis. Although vascular lesions are associated with IgAN, the current histopathological grading systems do not consider intrarenal vascular lesions when predicting patient prognosis. Therefore, this retrospective study, conducted at Kyungpook National University Hospital between October 2016 and December 2021, aimed to elucidate the significance of intrarenal vascular lesions in IgAN by comparing the clinical data of patients with and without such lesions.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e Data of patients with biopsy-confirmed primary IgAN between October 2016 and June 2021 at Kyungpook National University Hospital (Daegu, South Korea) were collected, and their medical records were reviewed. All slides from these 138 cases were independently pathologically reviewed by two nephropathologists (Y. J. K. and M. S. K.) using light microscope. The vascular lesions included in this study were fibrous intimal thickening, arteriolar wall thickening, and arteriolar hyalinosis. All cases were reviewed according to the Oxford Classification of IgA Nephropathy (2016) and Haas classification.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOf the 138 patients, 88 exhibited at least one intrarenal vascular lesion. Patients with arteriolar wall thickening demonstrated a reduced estimated glomerular filtration rate (eGFR), elevated serum creatinine level and urine protein-to-creatinine ratio, an increased proportion of global glomerulosclerosis, and a higher histologic grade of interstitial fibrosis and tubular atrophy at the time of biopsy.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eArteriolar wall thickening in IgAN are associated with reduced eGFR and global glomerulosclerosis. Moreover, reduced eGFR and global glomerulosclerosis are correlated with the progression to end-stage renal disease. Although the direct correlation between vascular lesions and end-stage renal disease is not entirely clear, a marginally significant association (log-rank test, p\u0026thinsp;=\u0026thinsp;0.06) was observed with arterial wall thickening. This study suggests the potential importance of vascular lesions in the prognosis of IgAN, encouraging further investigation using larger cohort studies to establish a clearer association.\u003c/p\u003e","manuscriptTitle":"Significance of intrarenal vascular lesions in Ig A nephropathy prognosis ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-02 16:41:41","doi":"10.21203/rs.3.rs-4836375/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-23T08:00:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-21T18:13:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-21T11:14:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"32333317396093333769760551452555711202","date":"2024-08-14T12:40:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-14T08:37:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"99910158339021159496925758795902366525","date":"2024-08-13T08:39:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"236726484138238395192916593262140204058","date":"2024-08-11T21:15:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"332309268745231097128644670551395847913","date":"2024-08-11T14:45:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-11T14:40:43+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-08-08T14:10:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-08T11:16:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-08T11:15:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nephrology","date":"2024-07-31T14:30:45+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":"5fb6aed9-5608-4a59-a4d6-deb42ad1977b","owner":[],"postedDate":"October 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-10-21T15:58:30+00:00","versionOfRecord":{"articleIdentity":"rs-4836375","link":"https://doi.org/10.1186/s12882-024-03803-8","journal":{"identity":"bmc-nephrology","isVorOnly":false,"title":"BMC Nephrology"},"publishedOn":"2024-10-16 15:56:52","publishedOnDateReadable":"October 16th, 2024"},"versionCreatedAt":"2024-10-02 16:41:41","video":"","vorDoi":"10.1186/s12882-024-03803-8","vorDoiUrl":"https://doi.org/10.1186/s12882-024-03803-8","workflowStages":[]},"version":"v1","identity":"rs-4836375","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4836375","identity":"rs-4836375","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}