Contrasting Prognoses: Regional Disparities in Primary Membranous Nephropathy Patients

Contrasting Prognoses: Regional Disparities in Primary Membranous Nephropathy Patients | 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 Article Contrasting Prognoses: Regional Disparities in Primary Membranous Nephropathy Patients Meifang Shang, Shengchun Wu, Yuan Cheng, Xun Qin, Ji Cen, Dongli Qi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4279443/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Primary membranous nephropathy (PMN) is the predominant histological subtype of nephrotic syndrome in adults, exhibiting a wide range of prognostic outcomes. This study employed a retrospective cohort design to investigate patients with confirmed PMN diagnosis via renal biopsy at the Second People's Hospital of Shenzhen, Guangdong Province between January 2008 and March 2020, as well as at Hechi People's Hospital from June 2013 to September 2021. Utilizing regression analysis, survival analysis, and cross-examination, the study aimed to compare epidemiological characteristics and prognostic indicators among PMN patients across different geographic regions. The study included a cohort of 662 patients diagnosed with PMN, with 362 (54.7%) of these patients residing in Guangxi. Patients in Guangxi exhibited characteristics such as advanced age, a higher proportion of males, elevated systolic and diastolic blood pressure, increased levels of serum PLA2R antibody concentration, uric acid, total cholesterol, and urine protein, as well as lower levels of hemoglobin, serum albumin, and baseline estimated glomerular filtration rate (eGFR). Results from multivariate Cox analysis indicated that regional disparities were identified as independent risk factors for a 30% decline in eGFR among PMN patients, with the risk of reaching renal endpoint being 7.84 times higher in Guangxi compared to Guangdong. The KM curve analysis indicated a significantly higher incidence of reaching the renal endpoint in Guangxi (P<0.0001). Furthermore, the interaction test revealed significant regional variations in the impact on renal endpoint incidence across different eGFR quantiles, suggesting a notable interaction effect. Conclusion: The clinical and pathological manifestations of primary membranous nephropathy (PMN) patients in Guangxi are more severe and have a worse renal prognosis compared to those in Shenzhen, Guangdong. Therefore, it is crucial to prioritize the allocation of limited medical resources and implement early intervention strategies in the formulation of chronic kidney disease health policies to effectively prevent and treat PMN in this region. Health sciences/Nephrology/Kidney diseases/Glomerular diseases/Membranous nephropathy Health sciences/Risk factors Primary membranous nephropathy geography epidemiological features prognosis Introduction Primary membranous nephropathy, representing the majority of cases of MN, is the predominant etiology of nephrotic syndrome in non-diabetic adults and the primary cause of end-stage renal disease in primary glomerulonephritis [ 1 ] . The pathogenesis of PMN involves the shedding of podocyte antigens onto the basement membrane, where they subsequently bind to antibodies. Pathologically, PMN is distinguished by the extensive accumulation of immune complexes in the subepithelial space [ 2 ] ,The target antigens most frequently implicated in PMN are PLA2R and THSD7A [ 3 , 4 ] . The prognostic outcome of PMN exhibits variability, with an estimated 20 to 35 percent of patients experiencing spontaneous remission within the initial two to three years of diagnosis, approximately 15 to 30 percent encountering relapse during remission, and 30 to 40 percent progressing to end-stage renal disease (ESRD) within a decade [ 5 – 8 ] . These outcomes may be influenced by factors such as environmental conditions, air pollution levels, ethnicity, regional economic status, gender, age, renal histopathology, baseline renal function, levels of PLA2R antibodies, extent of proteinuria, and specific target antigens [ 9 – 18 ] . Several studies have demonstrated variations in the prevalence of glomerular diseases across different geographical and ethnic populations [ 19 – 21 ] . Additionally, disparities in economic development, educational resources, and healthcare infrastructure exist between regions within China. For example, Shenzhen City in Guangdong Province stands out as a special economic development zone with a consistently high GDP ranking among Chinese provinces, and abundant educational resources. Comparing a significant number of high-level hospitals and medical research institutions in one region with the remote mountainous area of Hechi City, Guangxi Zhuang Autonomous Region, characterized by a predominantly Zhuang population and limited economic development, educational resources, and medical services, offers valuable insights for the prevention, diagnosis, and treatment of PMN. Prior research indicates that the prevalence of membranous nephropathy in Hechi, Guangxi Province accounts for 59.43% of primary glomerular diseases, surpassing rates observed in other geographical areas [ 22 ] . Additionally, a significant proportion of membranous nephropathy patients in this region belong to the Zhuang ethnic group, exhibit poor renal function, and display prominent pathological segmental sclerosis lesions [ 23 ] . Furthermore, elderly individuals with membranous nephropathy in this region tend to experience diminished renal function and severe renal pathological damage [ 24 ] . Nonetheless, there remains a dearth of research regarding the influence of various geographic regions on the epidemiological features and prognosis of PMN. Therefore, this study aims to examine the epidemiological characteristics and prognostic variances among PMN patients in different regions, with the goal of informing future preventive interventions, resource allocation decisions, and the development of health policies for chronic kidney disease. Ultimately, these efforts seek to enhance prevention and treatment strategies for individuals with PMN. Materials and methods Study population Patients admitted to Shenzhen Second People's Hospital between 2008 to 2020, as well as Hechi People's Hospital between 2013 to 2021 and were diagnosed with membranous nephropathy by renal puncture biopsy were continuously included, were consistently enrolled in the study from June 2013 to September 2021. Exclusion criteria encompassed individuals under the age of 14, the number of glomeruli was less than 8, patients diagnosed with secondary membranous nephropathy, including lupus nephritis, viral hepatitis, and malignancy. For the cross-sectional investigation, individuals lacking pertinent clinical or pathological information, and those with less than 6 months of follow-up or fewer follow-up visits. The study population was stratified into two groups based on distinct geographical regions.See Fig. 1 . method All data were collected by accessing the electronic medical record system and pathology reports. Clinical data Gender, age, ethnicity, body mass index, history of hypertension, history of diabetes, eGFR (MRDR formula), serum albumin, total cholesterol, white blood cells, neutrophils, lymphocytes, and 24-hour urine protein quantification were were collected at the time of the first renal biopsy. The kidney biopsy tissues of the selected candidates were sent to Guangzhou Jinyu Laboratory Center for light microscopy, immunofluorescence and electron microscopy, the light microscope specimens were stained with HE, PAS, PASM and Masson, and the immunofluorescence specimens were stained with IgG, IgA, IgM, C3, C1q, Fib, IgG1, IgG4 and PLA2R, and the electron microscope specimens were examined with ultrathin sections of toluidine blue stain, and the data of patients with ball sclerosis, segmental sclerosis and renal tubular injury were collected. Renal endpoint was defined as a 30% reduction in eGFR from baseline, an eGFR < 15 ml min-1 1.73 m-2, initiation of dialysis, or kidney transplantation. Retrospective collection and recording of follow-up blood creatinine levels and test times post-renal biopsy were conducted. Follow-up data were compiled, with a median time to reach the specified renal follow-up time of 6 months, and follow-up concluded on June 30, 2023. Statistical analysis Statistical data analysis was carried out by Yili software ( www.empowerstats.com ). The normally distributed continuous data were presented in the form of ± s, and the t-test was used for comparison between groups. The non-compliance normal distribution of continuous data was expressed as M(1/4, 3/4), and the Wilcoxon rank-sum test was used for comparison between the two groups. Count data were expressed in N (%), and comparisons between the two groups were performed by chi-square test or exact probability. Univariate and multivariate Cox analysis, KM survival curve analysis and interaction test analysis were used to explore the regional differences in epidemiological characteristics and prognosis of PMN patients. P < 0.05 was statistically significant. Ethical approval The research adhered to the principles outlined in the Declaration of Helsinki and received approval from the Medical Ethics Committee of Hechi People's Hospital (Ethics Batch No. 2020022) and the Ethics Committee of Shenzhen Second People's Hospital (Ethics Batch No. 201408186). Results Table 1 showed that patients from Guangxi exhibited advanced age, a higher proportion of male individuals, elevated systolic and diastolic blood pressure levels, increased serum PLA2R antibody concentration, uric acid, total cholesterol, and urine protein levels, as well as decreased hemoglobin, serum albumin, and baseline eGFR compared to patients from other regions. The thickness of basement membrane, the proportion of spherical sclerosis, the proportion of focal segmental sclerosis, the proportion of renal arteriole wall thickening, the proportion of tubular atrophy, the proportion of interstitial inflammatory cell infiltration, and the proportion of renal PLA2R deposition 2+~3+ were higher in Guangxi area. Table 1 Baseline data for PMN patients in different regions Variable Guangdong region group （ n=300） Guangxi region group （ n=362） P-value Gender (male%) 183 (61.0) 226 (62.4) 0.706 Ages 46.0 ± 14.5 50.4 ± 13.4 <0.001 Systolic pressure,mmhg 130.4 ± 22.8 143.4 ± 23.9 <0.001 Diastolic pressure,mmhg 85.2 ± 17.4 87.3 ± 13.6 <0.001 Serum PLA2R,ng/ml 122.7 ± 285.1 157.1 ± 268.5 0.006 Hemoglobin,g/L 132.3 ± 19.7 126.8 ± 21.2 <0.001 Albumin,g/L 26.9 ± 7.3 24.7 ± 5.4 <0.001 Total cholesterol,mmol/L 7.1 ± 2.3 8.3 ± 2.5 <0.001 24-hour urine protein quantification,g 5034.7 ± 4505.5 5.5 ± 3.5 <0.001 Uric acid,umol/l 388.4 ± 95.8 416.4 ± 126.5 0.010 eGFR[ml·min-1·(1.73 m2)-1] 110.6 ± 35.7 98.7 ± 43.6 <0.001 Thickness of basement membrane，nm 1192.0 ± 456.4 1301.6 ± 447.9 0.066 Proportion of spherical sclerosis，% 6.8 ± 11.8 7.6 ± 11.4 <0.001 Proportion of focal segmental sclerosis，% 0.9 ± 3.7 2.9 ± 6.7 <0.001 Renal arteriole wall thickening ，n(%) 165 (55.0%) 271 (74.9%) 0.012 Tubular atrophy，n(%) 153 (51.0%) 220 (60.8%) <0.001 Interstitial inflammatory cell infiltrate ，n(%) 219 (73.0%) 307 (84.8%) 0.009 Renal PLA2R deposition <0.001 Negative，n(%) 14 (7.6%) 12 (6.1%) Weak positive，n(%) 15 (8.2%) 4 (2.0%) 1+，n(%) 112 (60.9%) 73 (37.1%) 2+，n(%) 26 (14.1%) 87 (44.2%) 3+，n(%) 17 (9.2%) 21 (10.7%) Note: P<0.05 was statistically significant. Table 2 showed that region, age, systolic blood pressure, diastolic blood pressure, serum PLA2R tibody, albumin, total cholesterol, 24-hour urine protein quantification, uric acid, eGFR, spherical sclerosis ratio, focal segmental sclerosis ratio, renal arteriolar wall thickening, renal tubular atrophy, and interstitial inflammatory cell infiltration were the relevant influencing factors for the 30% decrease in eGFR in PMN patients. Table 2 Univariate COX analysis of renal endpoints Statistics decreased by 30% P-value Patients in Guangxi ,n(%) 362 (54.68%) 14.78 (9.62, 22.72) <0.0001 Gender (male%) 409 (61.78%) 1.16 (0.84, 1.60) 0.3821 Ages 48.37 ± 14.06 1.03 (1.02, 1.04) <0.0001 Systolic pressure,mmhg 137.51 ± 24.28 1.02 (1.01, 1.02) <0.0001 Diastolic pressure,mmhg 86.37 ± 15.46 1.02 (1.01, 1.03) 0.0036 Serum PLA2R antibody ,ng/ml 144.73 ± 274.38 1.00 (1.00, 1.00) 0.0108 Hemoglobin,g/L 129.27 ± 20.71 0.99 (0.99, 1.00) 0.0609 Albumin,g/L 25.68 ± 6.42 0.94 (0.92, 0.97) <0.0001 Total cholesterol,mmol/L 7.77 ± 2.49 1.11 (1.04, 1.18) 0.0015 24-hour urine protein quantification,g 2284.63 ± 3931.86 1.00 (1.00, 1.00) <0.0001 Uric acid,umol/l 403.73 ± 114.37 1.00 (1.00, 1.00) 0.0325 eGFR[ml·min-1·(1.73 m 2 )-1] 103.96 ± 40.75 0.99 (0.99, 1.00) 0.0067 D-dimer 1.97 ± 4.74 1.03 (0.98, 1.07) 0.2187 Proportion of spherical sclerosis,% 7.23 ± 11.56 1.02 (1.01, 1.04) 0.0018 Proportion of focal segmental sclerosis,% 2.01 ± 5.66 1.04 (1.01, 1.08) 0.0036 Renal arteriolar wall thickening .n(%) 436 (65.86%) 2.08 (1.47, 2.94) <0.0001 Tubular atrophy,n(%) 373 (56.34%) 1.79 (1.30, 2.47) 0.0004 Interstitial inflammatory cell infiltration,n(%) 526 (79.46%) 2.35 (1.53, 3.59) <0.0001 Note: P<0.05 was statistically significant. Table 3 showed that following adjustment for potential confounding variables including gender, age, ethnicity, history of hypertension, history of diabetes, body mass index, hemoglobin, serum albumin, total cholesterol, eGFRMDRD, proportion of globular sclerosis, and proportion of focal segmental sclerosis, distinct geographical regions emerged as significant independent risk factors for a 30% reduction in eGFR among PMN patients. Specifically, the risk of reaching the renal endpoint in the Guangxi region was 7.84 times greater compared to the Guangdong region (OR=7.84, 95% CI 4.94-12.44, P<0.001). Table 3 Multivariate COX analysis of renal endpoints Variables Model I（HR ，95%CI，P） Model II（HR ，95%CI，P） Geographical grouping Guangdong Group 1.0 1.0 Guangxi Group 14.78(9.62, 22.72)<0.001 7.84 (4.94, 12.44) <0.001 Systolic pressure 1.01 (1.00, 1.01) 0.0275 1.00 (1.00, 1.01) 0.5669 Total cholesterol 1.07 (1.02, 1.12) 0.0070 1.01 (0.95, 1.07) 0.7940 D-dimer 1.02 (1.00, 1.04) 0.0553 1.01 (0.98, 1.04) 0.4445 Note 1: Model I: No adjustment for confounding factors, Model II: Adjusted for confounding factors: gender, age, ethnicity, history of hypertension, history of diabetes, body mass index, hemoglobin, serum albumin, total cholesterol, eGFRMDRD, proportion of globular sclerosis, proportion of focal segmental sclerosis Note 2: P<0.05 was statistically significant. Figure 2 showed that the proportion of kidney endpoints reached was lower in the Guangdong group than in the Guangxi group, and the 1st, 3th, and 5th year renal survival rates were 90.8%, 85.6%, and 82.5%, and the 1st, 3th, and 5th year renal survival rates in the Guangxi group were 52.2%, 42.8%, and 27.3%. The difference was statistically significant (P<0.0001). Interaction test analysis Table 4 showed that there are significant differences in the effects of different regions on the incidence of renal endpoints in different eGFR classifications, and there are certain interactions. Table 4 Analysis of the interaction test eGFR ertile （mL/min/1.73 m²） Model I Model II Guangdong group Low（＜30） Ref. Ref. Guangxi group Low（＜30） 2.89 (1.67, 4.98) 0.0001 3.86 (2.02, 7.37) <0.0001 Guangdong group Middle（≥30，＜90） 0.41 (0.18, 0.94) 0.0359 0.48 (0.19, 1.21) 0.1190 Guangxi group Middle（≥30，＜90） 3.18 (1.83, 5.53) <0.0001 4.71 (2.33, 9.54) <0.0001 Guangdong group High（≥90） 0.24 (0.10, 0.58) 0.0016 0.30 (0.09, 0.96) 0.0425 Guangxi group High（≥90） 2.97 (1.68, 5.23) 0.0002 4.51 (1.85, 11.00) 0.0009 P interaction 0.0053 (0.0014 #) 0.0258 (0.0082 #) Model I: No adjustment for confounders; Model II: Adjusted for confounders: gender, age, ethnicity, history of hypertension, history of diabetes, body mass index, hemoglobin, serum albumin, total cholesterol, eGFRMDRD, proportion of globular sclerosis, proportion of focal segmental sclerosis. Discussion Geographical disparities refer to variations among distinct geographical regions across a range of dimensions, such as economic, cultural, social structure, education and political systems. These disparities can give rise to unique social environments that impact individuals nutritional status, living conditions, educational opportunities, and access to healthcare services. Consequently, these factors can contribute to imbalances in individuals physiological functioning, organ systems, and social behaviors, rendering them susceptible to chronic diseases. Patzer RE et al [25] found that different socioeconomic levels in the United States have a clear correlation with the incidence and progression of chronic kidney disease, which may be related to mediating risk factors such as diet, obesity, diabetes, hypertension, the interaction between educational attainment and health literacy, among other things. Hossain et al [26] found in a clinical study in the United Kingdom that lower socioeconomic levels were independently associated with the prevalence of proteinuria, the rate of progression of chronic kidney disease and the increased risk of progression to end-stage renal disease. A study in New Zealand identified Māori and Pacific Islanders living in New Zealand with higher poverty rates and relatively low socioeconomic status, and a much higher prevalence of microalbuminuria compared with people of European descent in New Zealand [27] . Several studies in several socioeconomically developed countries have also shown that delayed referral to specialized nephrology is associated with increased morbidity and mortality in chronic kidney disease [28-30] . This multicenter retrospective study aimed to investigate the potential correlation between PMN and geographical location. The findings revealed a significantly lower renal survival rate among PMN patients in the Guangxi group compared to those in the Guangdong Shenzhen groups. Additionally, the risk of reaching a renal endpoint was found to be 7.84 times higher in the Guangxi group compared to the Guangdong group (OR=7.84, 95%CI 4.94~12.44, P<0.001). The findings of a survey on glomerular diseases in Asian, African, and Eastern European nations align with the conclusions of this study, indicating that individuals in low-income environments may experience delays in accessing immunosuppressive therapy due to financial constraints, limited national resources, low rates of renal biopsy, and restricted availability of renal pathology techniques [31] . Canney M et al [32] studied socioeconomic status and the incidence of glomerular diseases, including membranous nephropathy, IgA nephropathy, FSGS, ANCA-associated nephropathy, and lupus nephritis, and found that socioeconomic status was negatively correlated with ANCA-associated nephropathy and lupus nephritis, but no significant association was found with membranous nephropathy and IgA nephropathy, which was inconsistent with the conclusions of this study, and may be related to race, ethnicity, genetic influence, and exposure to risk factors in the study samples. Heeringa et al [33] noted that baseline eGFR renal function is an independent risk factor for PMN disease progression. Zhang et al [34] showed that baseline serum uric acid is an independent risk factor for poor prognosis of PMN. Zhang et al [35] showed that the severity of renal pathology, particularly interstitial inflammatory cell infiltration, globular sclerosis, and vascular lesions, are risk factors for poor prognosis of PMN. Chen et al [36] showed that patients with PMN with hypertension had more severe clinical and pathological manifestations and a worse renal prognosis than those without hypertension, and diastolic blood pressure and mean arterial pressure were independent risk factors for disease progression in patients with idiopathic MN with hypertension. Compared with the Guangdong group, this study also found that compared with the Guangdong group, the Guangxi group had a low baseline eGFR, high blood uric acid and cholesterol levels, a high proportion of hypertension and diabetes history, a deviation in blood pressure control, and pathological results suggesting interstitial inflammatory cell infiltration, globular sclerosis and other diseases. In conclusion, the findings of this study indicate that patients diagnosed with MN in the Hechi group in Guangxi exhibited more pronounced clinical and pathological manifestations and a poorer renal prognosis compared to those in the Shenzhen group in Guangdong. This is a very valuable research direction, but the article also has some disadvantages, regional differences by what mechanism to affect the onset, progression and prognosis of PMN patients, is not clear, such as socio-economic situation, genetic susceptibility, environmental factors, growth and development, dietary factors, hypertension and diabetes, education and health literacy, healthcare resources, etc., there is no specific data or in-depth analysis to support the exact association of these factors with PMN. Elucidating these mechanisms may contribute to the prevention and treatment of PMN, as well as early intervention in public health, and also have reference significance for the allocation of limited medical resources and health policy formulation for chronic kidney disease. Therefore, we need to further expand the sample size in the future to explore the association between geography and chronic kidney disease in a multi-level context. Declarations Acknowledgements This work was supported by the Shenzhen Science and Technology R&D Fund (No. JCYJ20190806163801637) and the Shenzhen Medical Key Discipline Construction Fund (No. SZXK009). Author contributions Meifang Shang, Shengchun Wu，Yuan Cheng, Dongli Qi, Ji Cen, Xun Qin, and Qijun Wan participated in the investigation of patients and the analysis of data, Meifang Shang wrote the paper, and Zhe Wei planned and was responsible for these studies. Conflict of Interest Statement All authors declare no conflicts of interest Informed consent statement Due to the retrospective nature of the study, waived the need of obtaining informed consent. Data availability statement The datasets used and analysed during the current study available from the corresponding author on reasonable request. All data generated or analysed during this study are included in this published article [and its supplementary information files. Informed Consent Statement The requirement for obtaining informed consent is exempted due to the retrospective nature of the study. References Ronco P, Debiec H. Pathophysiological advances in membranous nephropathy: time for a shift in patient's care. Lancet. 2015;385(9981):1983-92. doi: 10.1016/S0140-6736(15)60731-0 . PMID: 26090644. Fogo AB, Lusco MA, Najafian B, Alpers CE. AJKD Atlas of Renal Pathology: Membranous Nephropathy. Am J Kidney Dis. 2015;66(3):e15-7. doi: 10.1053/j.ajkd.2015.07.006 . PMID: 26300203. Gu Y, Xu H, Tang D. Mechanisms of Primary Membranous Nephropathy. Biomolecules. 2021;11(4):513. doi: 10.3390/biom11040513 . PMID: 33808418; PMCID: PMC8065962. Keri KC, Blumenthal S, Kulkarni V, Beck L, Chongkrairatanakul T. Primary membranous nephropathy: comprehensive review and historical perspective. Postgrad Med J. 2019;95(1119):23–31. doi: 10.1136/postgradmedj-2018-135729 . Epub 2019 Jan 25. PMID: 30683678. Glassock RJ. Diagnosis and natural course of membranous nephropathy.Semin Nephrol.2003;23(4):324 – 32.doi: 10.1016/s0270-9295(03)00049-4 . PMID: 12923720. Fervenza FC, Sethi S, Specks U. Idiopathic membranous nephropathy: diagnosis and treatment. Clin J Am Soc Nephrol. 2008;3(3):905 – 19. doi: 10.2215/CJN.04321007. Epub 2008 Jan 30. PMID: 18235148. Cattran DC. Outcomes research in glomerulonephritis. Semin Nephrol. 2003;23(4):340 – 54. doi: 10.1016/s0270-9295(03)00062-7 . PMID: 12923722. Noel LH, Zanetti M, Droz D, Barbanel C. Long-term prognosis of idiopathic membranous glomerulonephritis. Study of 116 untreated patients. Am J Med. 1979;66(1):82–90. doi: 10.1016/0002-9343(79)90486-8 . PMID: 420255. Xu X, Wang G, Chen N, Lu T, Nie S, Xu G, Zhang P, Luo Y, Wang Y, Wang X, Schwartz J, Geng J, Hou FF. Long-Term Exposure to Air Pollution and Increased Risk of Membranous Nephropathy in China. J Am Soc Nephrol. 2016;27(12):3739–3746. doi: 10.1681/ASN.2016010093. Epub 2016 Jun 30. PMID: 27365535; PMCID: PMC5118492. Donadio JV Jr, Torres VE, Velosa JA, Wagoner RD, Holley KE, Okamura M, Ilstrup DM, Chu CP. Idiopathic membranous nephropathy: the natural history of untreated patients. Kidney Int. 1988;33(3):708 – 15. doi: 10.1038/ki.1988.56 . PMID: 3367560. Schieppati A, Mosconi L, Perna A, Mecca G, Bertani T, Garattini S, Remuzzi G. Prognosis of untreated patients with idiopathic membranous nephropathy. N Engl J Med. 1993;329(2):85 – 9. doi: 10.1056/NEJM199307083290203 . PMID: 8510707. Chen Y, Tang L, Feng Z, et al. Pathological predictors of renal outcomes in nephrotic idiopathic membranous nephropathy with decreased renal function[J]. J Nephrol, 2014, 27(3):307–316. Dumoulin A, Hill G S, Montseny J J, et al. Clinical and morphological prognostic factors in membranous nephropathy: significance of focal segmental glomerulosclerosis[J]. Am J Kidney Dis, 2003, 41(1):38–48. Heeringa S F, Branten A J, Deegens J K, et al. Focal segmental glomerulosclerosis is not a sufficient predictor of renal outcome in patients with membranous nephropathy[J]. Nephrol Dial Transplant, 2007, 22(8):2201–2207. Van Damme B, Tardanico R, Vanrenterghem Y, et al. Adhesions, focal sclerosis, protein crescents, and capsular lesions in membranous nephropathy[J]. J Pathol, 1990, 161(1):47–56. He H G, Wu C Q, Ye K, et al. Focal segmental glomerulosclerosis, excluding atypical lesion, is a predictor of renal outcome in patients with membranous nephropathy: a retrospective analysis of 716 cases[J]. BMC Nephrol, 2019, 20(1):328. Li J, Chen B, Gao C, et al. Clinical and pathological features of idiopathic membranous nephropathy with focal segmental sclerosis[J]. BMC Nephrol, 2019, 20(1):467. D'Agati V D, Fogo A B, Bruijn J A, et al. Pathologic classification of focal segmental glomerulosclerosis: a working proposal[J]. Am J Kidney Dis, 2004, 43(2):368–382. McGrogan A, Franssen CF, de Vries CS: The incidence of primary glomerulonephritis worldwide: a systematic review of the literature. Nephrol Dial Transplant 26: 414–430, 2011 [PubMed] [Google Scholar] Zuo L, Wang M: Beijing Blood Purification Quality Control and Improvement Center: Current status of maintenance hemodialysis in Beijing, China. Kidney Int Suppl 3: 167–169, 2013 [PMC free article] [PubMed] [Google Scholar] Wakai K, Nakai S, Kikuchi K, Iseki K, Miwa N, Masakane I, Wada A, Shinzato T, Nagura Y, Akiba T: Trends in incidence of end-stage renal disease in Japan, 1983–2000: age-adjusted and age-specific rates by gender and cause. Nephrol Dial Transplant 19: 2044–2052, 2004 [PubMed] [Google Scholar] Cen Ji, Hu Haofei, Cheng Yuan, et al.. The analysis of the pathology information and national characteristics of the single centers in Guangxi multi -ethnic groups [J]. Journal of Chengdu Medical College Qin Xun, Qi Dongli, Cen Ji, Hu Haofei, Cheng Yuan, Wan Qijun, Wei Yan. Analysis of clinical and kidney pathological characteristics of different ethnicic membrane kidney disease [J]. System Medicine, 2022,12: 54–58 + 62. Qin Xun, Qi Dongli, Cen Ji, Hu Haofei, Cheng Yuan, Wan Qijun, Wei Yan. Analysis of clinical and kidney pathological characteristics of specialized membrane kidney disease in different ages [J]. 04: 261–264. Patzer RE, McClellan WM. Influence of race, ethnicity and socioeconomic status on kidney disease. Nat Rev Nephrol. 2012;8(9):533–41. doi: 10.1038/nrneph.2012.117 . Epub 2012 Jun 26. PMID: 22735764; PMCID: PMC3950900. Hossain MP, Palmer D, Goyder E, El Nahas AM. Association of deprivation with worse outcomes in chronic kidney disease: findings from a hospital-based cohort in the United Kingdom. Nephron Clin Pract. 2012;120:c59–c70. Collins JF. Kidney disease in Maori and Pacific people in New Zealand. Clin Nephrol. 2010;74(Suppl. 1):S61–S65. Diez-Roux AV. Bringing context back into epidemiology: variables and fallacies in multilevel analysis. Am J Public Health. 1998;88:216–222. [PMC free article] [PubMed] [Google Scholar] Ward MM. Access to care and the incidence of end-stage renal disease due to diabetes. Diabetes Care. 2009;32:1032–1036. [PMC free article] [PubMed] [Google Scholar] Kershaw KN, et al. Metropolitan-level racial residential segregation and black-white disparities in hypertension. Am J Epidemiol. 2011;174:537–545. [PMC free article] [PubMed] [Google Scholar] Ramachandran R, Sulaiman S, Chauhan P, Ulasi I, Onu U, Villaneuva R, Alam MR, Akhtar F, Vincent L, Aulakh GS, Sutranto AL, Zakharova E, Jha V. Challenges in Diagnosis and Management of Glomerular Disease in Resource-Limited Settings. Kidney Int Rep. 2022;7(10):2141–2149. doi: 10.1016/j.ekir.2022.07.002. PMID: 36217525; PMCID: PMC9546742. Canney M, Induruwage D, Sahota A, McCrory C, Hladunewich MA, Gill J, Barbour SJ. Socioeconomic Position and Incidence of Glomerular Diseases. Clin J Am Soc Nephrol. 2020;15(3):367–374. doi: 10.2215/CJN.08060719 . Epub 2020 Feb 20. PMID: 32079609; PMCID: PMC7057310. Heeringa SF, Branten AJ, Deegens JK, et al. Focal segmental glomerulosclerosis is not a sufficient predictor of renal outcome in patients with membranous nephropathy [J]. Nephrol Dial Transplant, 2007, 22(8) : 2201–2207. Zhang J, Pan M, Zhang J, et al. Serum uric acid is an independ- ent predictor of renal outcomes in patients with idiopathic mem- branous nephropathy [J]. Int Urol Nephrol, 2019, 51 (10) : 1797–1804. Zhang XD, Cui Z, Zhang MF. Clinical implications of pathologi- cal features of primary mem branous nephropathy [J]. BMC Nephrol, 2018, 19(1) : 215–224. CHEN Jia, CHEN Yuan, HU Haofei, QI Dongli, GUAN Mijie, WAN Qijun. Clinicopathologic characteristics and prognosis of idiopathic membranous nephropathy with hypertension[J]. Chinese Journal of Nephrology,2021,08:677–681. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4279443","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":298747198,"identity":"f9763106-8e9d-4ab9-bdd1-d9d9db274746","order_by":0,"name":"Meifang Shang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYBACNvbm47//VEjIARkHHyRU1BDWwsdzLEGC54yNMT/PsWSDB2eOEdYiJ5FjIMHblpY4c4aPmeTDFmYiHAa0xUDizGFjgxtsaRWJDWwM/O3dCYT8ciDBoOKwnMHt5mM3EnfIMEicObuBoC0HEkC23DmWdiPxDBuDgUQuAS0SOYYNB9sOJ264kWNWkNjGTJQWY8ZGsPdzzBiI08JzLI2ZARrIEglnjvEQ9It8e/MxZgZoVH78UVEjx9/ei18LBuAhTfkoGAWjYBSMAqwAAO6cTxEE9lc9AAAAAElFTkSuQmCC","orcid":"","institution":"Hechi People's Hospital, Youjiang Medical College for Nationalities)","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Meifang","middleName":"","lastName":"Shang","suffix":""},{"id":298747200,"identity":"a63838b1-7e65-4430-8f53-2f9cb83cb3d6","order_by":1,"name":"Shengchun Wu","email":"","orcid":"","institution":"Hechi People's Hospital, Youjiang Medical College for Nationalities)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shengchun","middleName":"","lastName":"Wu","suffix":""},{"id":298747201,"identity":"1e548a43-af87-484b-9fff-58edcf8060cb","order_by":2,"name":"Yuan Cheng","email":"","orcid":"","institution":"First Affiliated Hospital of Shenzhen University)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Cheng","suffix":""},{"id":298747202,"identity":"8d74f884-1c4c-4de3-a04c-3bac211fdd12","order_by":3,"name":"Xun Qin","email":"","orcid":"","institution":"Hechi People's Hospital, Youjiang Medical College for Nationalities)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xun","middleName":"","lastName":"Qin","suffix":""},{"id":298747203,"identity":"8a715a0c-c73b-4988-b949-fc91115a2124","order_by":4,"name":"Ji Cen","email":"","orcid":"","institution":"Hechi People's Hospital, Youjiang Medical College for Nationalities)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ji","middleName":"","lastName":"Cen","suffix":""},{"id":298747204,"identity":"261b48ab-1fd9-4dd2-9e3d-812a788d47df","order_by":5,"name":"Dongli Qi","email":"","orcid":"","institution":"First Affiliated Hospital of Shenzhen University)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Dongli","middleName":"","lastName":"Qi","suffix":""},{"id":298747205,"identity":"2347cf33-025a-4860-ab4a-9bc54c43dce7","order_by":6,"name":"Qijun Wan","email":"","orcid":"","institution":"First Affiliated Hospital of Shenzhen University)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qijun","middleName":"","lastName":"Wan","suffix":""},{"id":298747206,"identity":"5478deeb-dcd7-4ea6-80ef-cbf96569dda0","order_by":7,"name":"Zhe Wei","email":"","orcid":"","institution":"Hechi People's Hospital, Youjiang Medical College for Nationalities)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhe","middleName":"","lastName":"Wei","suffix":""}],"badges":[],"createdAt":"2024-04-17 05:29:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4279443/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4279443/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66852619,"identity":"07fcb6fa-78a1-4887-a206-9212f0bb7d03","added_by":"auto","created_at":"2024-10-17 07:17:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":433402,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4279443/v1/0eb087c9-62ba-4ef1-8d52-c23e558befe9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Contrasting Prognoses: Regional Disparities in Primary Membranous Nephropathy Patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePrimary membranous nephropathy, representing the majority of cases of MN, is the predominant etiology of nephrotic syndrome in non-diabetic adults and the primary cause of end-stage renal disease in primary glomerulonephritis\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. The pathogenesis of PMN involves the shedding of podocyte antigens onto the basement membrane, where they subsequently bind to antibodies. Pathologically, PMN is distinguished by the extensive accumulation of immune complexes in the subepithelial space\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e,The target antigens most frequently implicated in PMN are PLA2R and THSD7A\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. The prognostic outcome of PMN exhibits variability, with an estimated 20 to 35 percent of patients experiencing spontaneous remission within the initial two to three years of diagnosis, approximately 15 to 30 percent encountering relapse during remission, and 30 to 40 percent progressing to end-stage renal disease (ESRD) within a decade\u003csup\u003e[\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. These outcomes may be influenced by factors such as environmental conditions, air pollution levels, ethnicity, regional economic status, gender, age, renal histopathology, baseline renal function, levels of PLA2R antibodies, extent of proteinuria, and specific target antigens\u003csup\u003e[\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSeveral studies have demonstrated variations in the prevalence of glomerular diseases across different geographical and ethnic populations\u003csup\u003e[\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Additionally, disparities in economic development, educational resources, and healthcare infrastructure exist between regions within China. For example, Shenzhen City in Guangdong Province stands out as a special economic development zone with a consistently high GDP ranking among Chinese provinces, and abundant educational resources. Comparing a significant number of high-level hospitals and medical research institutions in one region with the remote mountainous area of Hechi City, Guangxi Zhuang Autonomous Region, characterized by a predominantly Zhuang population and limited economic development, educational resources, and medical services, offers valuable insights for the prevention, diagnosis, and treatment of PMN. Prior research indicates that the prevalence of membranous nephropathy in Hechi, Guangxi Province accounts for 59.43% of primary glomerular diseases, surpassing rates observed in other geographical areas\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Additionally, a significant proportion of membranous nephropathy patients in this region belong to the Zhuang ethnic group, exhibit poor renal function, and display prominent pathological segmental sclerosis lesions\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Furthermore, elderly individuals with membranous nephropathy in this region tend to experience diminished renal function and severe renal pathological damage\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Nonetheless, there remains a dearth of research regarding the influence of various geographic regions on the epidemiological features and prognosis of PMN. Therefore, this study aims to examine the epidemiological characteristics and prognostic variances among PMN patients in different regions, with the goal of informing future preventive interventions, resource allocation decisions, and the development of health policies for chronic kidney disease. Ultimately, these efforts seek to enhance prevention and treatment strategies for individuals with PMN.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eStudy population\u003c/p\u003e \u003cp\u003ePatients admitted to Shenzhen Second People's Hospital between 2008 to 2020, as well as Hechi People's Hospital between 2013 to 2021 and were diagnosed with membranous nephropathy by renal puncture biopsy were continuously included, were consistently enrolled in the study from June 2013 to September 2021. Exclusion criteria encompassed individuals under the age of 14, the number of glomeruli was less than 8, patients diagnosed with secondary membranous nephropathy, including lupus nephritis, viral hepatitis, and malignancy. For the cross-sectional investigation, individuals lacking pertinent clinical or pathological information, and those with less than 6 months of follow-up or fewer follow-up visits. The study population was stratified into two groups based on distinct geographical regions.See Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003emethod\u003c/p\u003e \u003cp\u003eAll data were collected by accessing the electronic medical record system and pathology reports.\u003c/p\u003e \u003cp\u003eClinical data\u003c/p\u003e \u003cp\u003eGender, age, ethnicity, body mass index, history of hypertension, history of diabetes, eGFR (MRDR formula), serum albumin, total cholesterol, white blood cells, neutrophils, lymphocytes, and 24-hour urine protein quantification were were collected at the time of the first renal biopsy. The kidney biopsy tissues of the selected candidates were sent to Guangzhou Jinyu Laboratory Center for light microscopy, immunofluorescence and electron microscopy, the light microscope specimens were stained with HE, PAS, PASM and Masson, and the immunofluorescence specimens were stained with IgG, IgA, IgM, C3, C1q, Fib, IgG1, IgG4 and PLA2R, and the electron microscope specimens were examined with ultrathin sections of toluidine blue stain, and the data of patients with ball sclerosis, segmental sclerosis and renal tubular injury were collected. Renal endpoint was defined as a 30% reduction in eGFR from baseline, an eGFR\u0026thinsp;\u0026lt;\u0026thinsp;15 ml min-1 1.73 m-2, initiation of dialysis, or kidney transplantation. Retrospective collection and recording of follow-up blood creatinine levels and test times post-renal biopsy were conducted. Follow-up data were compiled, with a median time to reach the specified renal follow-up time of 6 months, and follow-up concluded on June 30, 2023.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical data analysis was carried out by Yili software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.empowerstats.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.empowerstats.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The normally distributed continuous data were presented in the form of \u0026plusmn;\u0026thinsp;s, and the t-test was used for comparison between groups. The non-compliance normal distribution of continuous data was expressed as M(1/4, 3/4), and the Wilcoxon rank-sum test was used for comparison between the two groups. Count data were expressed in N (%), and comparisons between the two groups were performed by chi-square test or exact probability. Univariate and multivariate Cox analysis, KM survival curve analysis and interaction test analysis were used to explore the regional differences in epidemiological characteristics and prognosis of PMN patients. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was statistically significant.\u003c/p\u003e \u003c/div\u003e\u003cp\u003eEthical approval\u003c/p\u003e\n\u003cp\u003eThe research adhered to the principles outlined in the Declaration of Helsinki and received approval from the Medical Ethics Committee of Hechi People\u0026apos;s Hospital (Ethics Batch No. 2020022) and the Ethics Committee of Shenzhen Second People\u0026apos;s Hospital (Ethics Batch No. 201408186).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTable 1 showed that patients from Guangxi exhibited advanced age, a higher proportion of male individuals, elevated systolic and diastolic blood pressure levels, increased serum PLA2R antibody concentration, uric acid, total cholesterol, and urine protein levels, as well as decreased hemoglobin, serum albumin, and baseline eGFR compared to patients from other regions. The thickness of basement membrane, the proportion of spherical sclerosis, the proportion of focal segmental sclerosis, the proportion of renal arteriole wall thickening, the proportion of tubular atrophy, the proportion of interstitial inflammatory cell infiltration, and the proportion of renal PLA2R deposition 2+~3+ were higher in Guangxi area.\u003c/p\u003e\n\u003cp\u003eTable 1 Baseline data for PMN patients in different regions\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.018348623853214%\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.220183486238533%\"\u003e\n \u003cp\u003eGuangdong region group\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003en=300）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003eGuangxi region group\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003en=362）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.761467889908257%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.018348623853214%\"\u003e\n \u003cp\u003eGender (male%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.220183486238533%\"\u003e\n \u003cp\u003e183 (61.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e226 (62.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.761467889908257%\"\u003e\n \u003cp\u003e0.706\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.018348623853214%\"\u003e\n \u003cp\u003eAges\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.220183486238533%\"\u003e\n \u003cp\u003e46.0 \u0026plusmn; 14.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e50.4 \u0026plusmn;\u0026nbsp;13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.761467889908257%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.018348623853214%\"\u003e\n \u003cp\u003eSystolic pressure,mmhg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.220183486238533%\"\u003e\n \u003cp\u003e130.4 \u0026plusmn; 22.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e143.4 \u0026plusmn; 23.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.761467889908257%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.018348623853214%\"\u003e\n \u003cp\u003eDiastolic pressure,mmhg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.220183486238533%\"\u003e\n \u003cp\u003e85.2 \u0026plusmn; 17.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e87.3 \u0026plusmn; 13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.761467889908257%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.018348623853214%\"\u003e\n \u003cp\u003eSerum PLA2R,ng/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.220183486238533%\"\u003e\n \u003cp\u003e122.7 \u0026plusmn; 285.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e157.1 \u0026plusmn; 268.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.761467889908257%\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.018348623853214%\"\u003e\n \u003cp\u003eHemoglobin,g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.220183486238533%\"\u003e\n \u003cp\u003e132.3 \u0026plusmn; 19.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e126.8 \u0026plusmn; 21.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.761467889908257%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.018348623853214%\"\u003e\n \u003cp\u003eAlbumin,g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.220183486238533%\"\u003e\n \u003cp\u003e26.9 \u0026plusmn; 7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e24.7 \u0026plusmn; 5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.761467889908257%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.018348623853214%\"\u003e\n \u003cp\u003eTotal cholesterol,mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.220183486238533%\"\u003e\n \u003cp\u003e7.1 \u0026plusmn; 2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e8.3 \u0026plusmn; 2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.761467889908257%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.018348623853214%\"\u003e\n \u003cp\u003e24-hour urine protein quantification,g\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.220183486238533%\"\u003e\n \u003cp\u003e5034.7 \u0026plusmn; 4505.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e5.5 \u0026plusmn; 3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.761467889908257%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.018348623853214%\"\u003e\n \u003cp\u003eUric acid,umol/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.220183486238533%\"\u003e\n \u003cp\u003e388.4 \u0026plusmn; 95.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e416.4 \u0026plusmn; 126.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.761467889908257%\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.018348623853214%\"\u003e\n \u003cp\u003eeGFR[ml\u0026middot;min-1\u0026middot;(1.73 m2)-1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.220183486238533%\"\u003e\n \u003cp\u003e110.6 \u0026plusmn; 35.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e98.7 \u0026plusmn; 43.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.761467889908257%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.018348623853214%\"\u003e\n \u003cp\u003eThickness of basement membrane，nm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.220183486238533%\"\u003e\n \u003cp\u003e1192.0 \u0026plusmn; 456.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e1301.6 \u0026plusmn; 447.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.761467889908257%\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eProportion of spherical sclerosis，%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.8 \u0026plusmn; 11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.6 \u0026plusmn; 11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eProportion of focal segmental sclerosis，%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.9 \u0026plusmn; 3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.9 \u0026plusmn; 6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRenal arteriole wall thickening\u0026nbsp;，n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e165 (55.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e271 (74.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTubular atrophy，n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e153 (51.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e220 (60.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInterstitial inflammatory cell infiltrate\u0026nbsp;，n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e219 (73.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e307 (84.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRenal PLA2R deposition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNegative，n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14 (7.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12 (6.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWeak positive，n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15 (8.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e1+，n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e112 (60.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e73 (37.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e2+，n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26 (14.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e87 (44.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e3+，n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17 (9.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21 (10.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: P\u0026lt;0.05 was statistically significant.\u003c/p\u003e\n\u003cp\u003eTable 2 showed that region, age, systolic blood pressure, diastolic blood pressure, serum PLA2R tibody, albumin, total cholesterol, 24-hour urine protein quantification, uric acid, eGFR, spherical sclerosis ratio, focal segmental sclerosis ratio, renal arteriolar wall thickening, renal tubular atrophy, and interstitial inflammatory cell infiltration were the relevant influencing factors for the 30% decrease in eGFR in PMN patients.\u003c/p\u003e\n\u003cp\u003eTable 2 Univariate COX analysis of renal endpoints\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.634408602150536%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.118279569892472%\"\u003e\n \u003cp\u003eStatistics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.164874551971327%\"\u003e\n \u003cp\u003edecreased by\u0026nbsp;30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.082437275985663%\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.634408602150536%\"\u003e\n \u003cp\u003ePatients in Guangxi\u0026nbsp;,n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.118279569892472%\"\u003e\n \u003cp\u003e362 (54.68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.164874551971327%\"\u003e\n \u003cp\u003e14.78 (9.62, 22.72)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.082437275985663%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.634408602150536%\"\u003e\n \u003cp\u003eGender (male%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.118279569892472%\"\u003e\n \u003cp\u003e409 (61.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.164874551971327%\"\u003e\n \u003cp\u003e1.16 (0.84, 1.60)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.082437275985663%\"\u003e\n \u003cp\u003e0.3821\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.634408602150536%\"\u003e\n \u003cp\u003eAges\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.118279569892472%\"\u003e\n \u003cp\u003e48.37 \u0026plusmn; 14.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.164874551971327%\"\u003e\n \u003cp\u003e1.03 (1.02, 1.04)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.082437275985663%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.634408602150536%\"\u003e\n \u003cp\u003eSystolic pressure,mmhg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.118279569892472%\"\u003e\n \u003cp\u003e137.51 \u0026plusmn; 24.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.164874551971327%\"\u003e\n \u003cp\u003e1.02 (1.01, 1.02)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.082437275985663%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.634408602150536%\"\u003e\n \u003cp\u003eDiastolic \u0026nbsp;pressure,mmhg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.118279569892472%\"\u003e\n \u003cp\u003e86.37 \u0026plusmn; 15.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.164874551971327%\"\u003e\n \u003cp\u003e1.02 (1.01, 1.03)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.082437275985663%\"\u003e\n \u003cp\u003e0.0036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.634408602150536%\"\u003e\n \u003cp\u003eSerum PLA2R antibody\u0026nbsp;,ng/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.118279569892472%\"\u003e\n \u003cp\u003e144.73 \u0026plusmn; 274.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.164874551971327%\"\u003e\n \u003cp\u003e1.00 (1.00, 1.00)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.082437275985663%\"\u003e\n \u003cp\u003e0.0108\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.634408602150536%\"\u003e\n \u003cp\u003eHemoglobin,g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.118279569892472%\"\u003e\n \u003cp\u003e129.27 \u0026plusmn; 20.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.164874551971327%\"\u003e\n \u003cp\u003e0.99 (0.99, 1.00)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.082437275985663%\"\u003e\n \u003cp\u003e0.0609\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.634408602150536%\"\u003e\n \u003cp\u003eAlbumin,g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.118279569892472%\"\u003e\n \u003cp\u003e25.68 \u0026plusmn; 6.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.164874551971327%\"\u003e\n \u003cp\u003e0.94 (0.92, 0.97)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.082437275985663%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.634408602150536%\"\u003e\n \u003cp\u003eTotal cholesterol,mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.118279569892472%\"\u003e\n \u003cp\u003e7.77 \u0026plusmn; 2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.164874551971327%\"\u003e\n \u003cp\u003e1.11 (1.04, 1.18)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.082437275985663%\"\u003e\n \u003cp\u003e0.0015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.634408602150536%\"\u003e\n \u003cp\u003e24-hour urine protein quantification,g\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.118279569892472%\"\u003e\n \u003cp\u003e2284.63 \u0026plusmn; 3931.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.164874551971327%\"\u003e\n \u003cp\u003e1.00 (1.00, 1.00)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.082437275985663%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.634408602150536%\"\u003e\n \u003cp\u003eUric acid,umol/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.118279569892472%\"\u003e\n \u003cp\u003e403.73 \u0026plusmn; 114.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.164874551971327%\"\u003e\n \u003cp\u003e1.00 (1.00, 1.00)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.082437275985663%\"\u003e\n \u003cp\u003e0.0325\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.634408602150536%\"\u003e\n \u003cp\u003eeGFR[ml\u0026middot;min-1\u0026middot;(1.73 m\u003csup\u003e2\u003c/sup\u003e)-1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.118279569892472%\"\u003e\n \u003cp\u003e103.96 \u0026plusmn; 40.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.164874551971327%\"\u003e\n \u003cp\u003e0.99 (0.99, 1.00)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.082437275985663%\"\u003e\n \u003cp\u003e0.0067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.634408602150536%\"\u003e\n \u003cp\u003eD-dimer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.118279569892472%\"\u003e\n \u003cp\u003e1.97 \u0026plusmn; 4.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.164874551971327%\"\u003e\n \u003cp\u003e1.03 (0.98, 1.07)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.082437275985663%\"\u003e\n \u003cp\u003e0.2187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.634408602150536%\"\u003e\n \u003cp\u003eProportion of spherical sclerosis,%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.118279569892472%\"\u003e\n \u003cp\u003e7.23 \u0026plusmn; 11.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.164874551971327%\"\u003e\n \u003cp\u003e1.02 (1.01, 1.04)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.082437275985663%\"\u003e\n \u003cp\u003e0.0018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.634408602150536%\"\u003e\n \u003cp\u003eProportion of focal segmental sclerosis,%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.118279569892472%\"\u003e\n \u003cp\u003e2.01 \u0026plusmn; 5.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.164874551971327%\"\u003e\n \u003cp\u003e1.04 (1.01, 1.08)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.082437275985663%\"\u003e\n \u003cp\u003e0.0036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.634408602150536%\"\u003e\n \u003cp\u003eRenal arteriolar wall thickening\u0026nbsp;.n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.118279569892472%\"\u003e\n \u003cp\u003e436 (65.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.164874551971327%\"\u003e\n \u003cp\u003e2.08 (1.47, 2.94)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.082437275985663%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.634408602150536%\"\u003e\n \u003cp\u003eTubular atrophy,n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.118279569892472%\"\u003e\n \u003cp\u003e373 (56.34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.164874551971327%\"\u003e\n \u003cp\u003e1.79 (1.30, 2.47)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.082437275985663%\"\u003e\n \u003cp\u003e0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.634408602150536%\"\u003e\n \u003cp\u003eInterstitial inflammatory cell infiltration,n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.118279569892472%\"\u003e\n \u003cp\u003e526 (79.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.164874551971327%\"\u003e\n \u003cp\u003e2.35 (1.53, 3.59)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.082437275985663%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: P\u0026lt;0.05 was statistically significant.\u003c/p\u003e\n\u003cp\u003eTable 3 showed that following adjustment for potential confounding variables including gender, age, ethnicity, history of hypertension, history of diabetes, body mass index, hemoglobin, serum albumin, total cholesterol, eGFRMDRD, proportion of globular sclerosis, and proportion of focal segmental sclerosis, distinct geographical regions emerged as significant independent risk factors for a 30% reduction in eGFR among PMN patients. Specifically, the risk of reaching the renal endpoint in the Guangxi region was 7.84 times greater compared to the Guangdong region (OR=7.84, 95% CI 4.94-12.44, P\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003eTable 3\u0026nbsp;Multivariate COX analysis of renal endpoints\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"574\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.56445993031359%\" valign=\"top\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.714285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eModel I（HR ，95%CI，P）\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.721254355400696%\" valign=\"top\"\u003e\n \u003cp\u003eModel\u0026nbsp;II（HR ，95%CI，P）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.56445993031359%\" valign=\"top\"\u003e\n \u003cp\u003eGeographical grouping\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.714285714285715%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.721254355400696%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.56445993031359%\" valign=\"top\"\u003e\n \u003cp\u003eGuangdong Group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.714285714285715%\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.721254355400696%\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.56445993031359%\" valign=\"top\"\u003e\n \u003cp\u003eGuangxi Group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.714285714285715%\"\u003e\n \u003cp\u003e14.78(9.62, 22.72)\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.721254355400696%\"\u003e\n \u003cp\u003e7.84 (4.94, 12.44) \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.56445993031359%\"\u003e\n \u003cp\u003eSystolic pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.714285714285715%\"\u003e\n \u003cp\u003e1.01 (1.00, 1.01) 0.0275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.721254355400696%\"\u003e\n \u003cp\u003e1.00 (1.00, 1.01) 0.5669\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.56445993031359%\"\u003e\n \u003cp\u003eTotal cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.714285714285715%\"\u003e\n \u003cp\u003e1.07 (1.02, 1.12) 0.0070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.721254355400696%\"\u003e\n \u003cp\u003e1.01 (0.95, 1.07) 0.7940\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.56445993031359%\"\u003e\n \u003cp\u003eD-dimer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.714285714285715%\"\u003e\n \u003cp\u003e1.02 (1.00, 1.04) 0.0553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.721254355400696%\"\u003e\n \u003cp\u003e1.01 (0.98, 1.04) 0.4445\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote 1: Model I: No adjustment for confounding factors, Model II: Adjusted for confounding factors: gender, age, ethnicity, history of hypertension, history of diabetes, body mass index, hemoglobin, serum albumin, total cholesterol, eGFRMDRD, proportion of globular sclerosis, proportion of focal segmental sclerosis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNote 2: P\u0026lt;0.05 was statistically significant.\u003c/p\u003e\n\u003cp\u003eFigure 2 showed that the proportion of kidney endpoints reached was lower in the Guangdong group than in the Guangxi group, and the 1st, 3th, and 5th year renal survival rates were 90.8%, 85.6%, and 82.5%, and the 1st, 3th, and 5th year renal survival rates in the Guangxi group were 52.2%, 42.8%, and 27.3%. The difference was statistically significant (P\u0026lt;0.0001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInteraction test analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 4 \u0026nbsp;showed that \u0026nbsp;there are significant differences in the effects of different regions on the incidence of renal endpoints in different eGFR classifications, and there are certain interactions.\u003c/p\u003e\n\u003cp\u003eTable 4\u0026nbsp;Analysis of the interaction test\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.75134168157424%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.847942754919499%\"\u003e\n \u003cp\u003eeGFR ertile\u003c/p\u003e\n \u003cp\u003e（mL/min/1.73 m\u0026sup2;）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200357781753134%\"\u003e\n \u003cp\u003eModel I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200357781753134%\"\u003e\n \u003cp\u003eModel II\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.75134168157424%\"\u003e\n \u003cp\u003eGuangdong group\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.847942754919499%\"\u003e\n \u003cp\u003eLow（＜30）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200357781753134%\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200357781753134%\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.75134168157424%\"\u003e\n \u003cp\u003eGuangxi group\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.847942754919499%\"\u003e\n \u003cp\u003eLow（＜30）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200357781753134%\"\u003e\n \u003cp\u003e2.89 (1.67, 4.98) 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200357781753134%\"\u003e\n \u003cp\u003e3.86 (2.02, 7.37) \u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.75134168157424%\"\u003e\n \u003cp\u003eGuangdong group\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.847942754919499%\"\u003e\n \u003cp\u003eMiddle（\u0026ge;30，＜90）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200357781753134%\"\u003e\n \u003cp\u003e0.41 (0.18, 0.94) 0.0359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200357781753134%\"\u003e\n \u003cp\u003e0.48 (0.19, 1.21) 0.1190\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.75134168157424%\"\u003e\n \u003cp\u003eGuangxi group\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.847942754919499%\"\u003e\n \u003cp\u003eMiddle（\u0026ge;30，＜90）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200357781753134%\"\u003e\n \u003cp\u003e3.18 (1.83, 5.53) \u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200357781753134%\"\u003e\n \u003cp\u003e4.71 (2.33, 9.54) \u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.75134168157424%\"\u003e\n \u003cp\u003eGuangdong group\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.847942754919499%\"\u003e\n \u003cp\u003eHigh（\u0026ge;90）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200357781753134%\"\u003e\n \u003cp\u003e0.24 (0.10, 0.58) 0.0016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200357781753134%\"\u003e\n \u003cp\u003e0.30 (0.09, 0.96) 0.0425\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.75134168157424%\"\u003e\n \u003cp\u003eGuangxi group\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.847942754919499%\"\u003e\n \u003cp\u003eHigh（\u0026ge;90）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200357781753134%\"\u003e\n \u003cp\u003e2.97 (1.68, 5.23) 0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200357781753134%\"\u003e\n \u003cp\u003e4.51 (1.85, 11.00) 0.0009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.75134168157424%\"\u003e\n \u003cp\u003eP interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.847942754919499%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200357781753134%\"\u003e\n \u003cp\u003e0.0053 (0.0014 #)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200357781753134%\"\u003e\n \u003cp\u003e0.0258 (0.0082 #)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModel I: No adjustment for confounders; Model II: Adjusted for confounders: gender, age, ethnicity, history of hypertension, history of diabetes, body mass index, hemoglobin, serum albumin, total cholesterol, eGFRMDRD, proportion of globular sclerosis, proportion of focal segmental sclerosis.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eGeographical disparities refer to variations among distinct geographical regions across a range of dimensions, such as economic, cultural, social structure, education and political systems. These disparities can give rise to unique social environments that impact individuals nutritional status, living conditions, educational opportunities, and access to healthcare services. Consequently, these factors can contribute to imbalances in individuals physiological functioning, organ systems, and social behaviors, rendering them susceptible to chronic diseases. Patzer RE et al \u003csup\u003e[25]\u003c/sup\u003e found that different socioeconomic levels in the United States have a clear correlation with the incidence and progression of chronic kidney disease, which may be related to mediating risk factors such as diet, obesity, diabetes, hypertension, the interaction between educational attainment and health literacy, among other things. Hossain et al \u003csup\u003e[26]\u003c/sup\u003e found in a clinical study in the United Kingdom that lower socioeconomic levels were independently associated with the prevalence of proteinuria, the rate of progression of chronic kidney disease and the increased risk of progression to end-stage renal disease. A study in New Zealand identified Māori and Pacific Islanders living in New Zealand with higher poverty rates and relatively low socioeconomic status, and a much higher prevalence of microalbuminuria compared with people of European descent in New Zealand \u003csup\u003e[27]\u003c/sup\u003e. Several studies in several socioeconomically developed countries have also shown that delayed referral to specialized nephrology is associated with increased morbidity and mortality in chronic kidney disease\u003csup\u003e[28-30]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThis multicenter retrospective study aimed to investigate the potential correlation between PMN and geographical location. The findings revealed a significantly lower renal survival rate among PMN patients in the Guangxi group compared to those in the Guangdong Shenzhen groups. Additionally, the risk of reaching a renal endpoint was found to be 7.84 times higher in the Guangxi group compared to the Guangdong group (OR=7.84, 95%CI 4.94~12.44, P\u0026lt;0.001). The findings of a survey on glomerular diseases in Asian, African, and Eastern European nations align with the conclusions of this study, indicating that individuals in low-income environments may experience delays in accessing immunosuppressive therapy due to financial constraints, limited national resources, low rates of renal biopsy, and restricted availability of renal pathology techniques\u003csup\u003e[31]\u003c/sup\u003e. Canney M et al\u003csup\u003e[32]\u003c/sup\u003e studied socioeconomic status and the incidence of glomerular diseases, including membranous nephropathy, IgA nephropathy, FSGS, ANCA-associated nephropathy, and lupus nephritis, and found that socioeconomic status was negatively correlated with ANCA-associated nephropathy and lupus nephritis, but no significant association was found with membranous nephropathy and IgA nephropathy, which was inconsistent with the conclusions of this study, and may be related to race, ethnicity, genetic influence, and exposure to risk factors in the study samples.\u003c/p\u003e\n\u003cp\u003eHeeringa et al\u003csup\u003e[33]\u003c/sup\u003e noted that baseline eGFR renal function is an independent risk factor for PMN disease progression. Zhang et al\u003csup\u003e[34]\u003c/sup\u003e showed that baseline serum uric acid is an independent risk factor for poor prognosis of PMN. Zhang et al\u003csup\u003e[35]\u003c/sup\u003e showed that the severity of renal pathology, particularly interstitial inflammatory cell infiltration, globular sclerosis, and vascular lesions, are risk factors for poor prognosis of PMN. Chen et al\u003csup\u003e[36]\u003c/sup\u003e showed that patients with PMN with hypertension had more severe clinical and pathological manifestations and a worse renal prognosis than those without hypertension, and diastolic blood pressure and mean arterial pressure were independent risk factors for disease progression in patients with idiopathic MN with hypertension. Compared with the Guangdong group, this study also found that compared with the Guangdong group, the Guangxi group had a low baseline eGFR, high blood uric acid and cholesterol levels, a high proportion of hypertension and diabetes history, a deviation in blood pressure control, and pathological results suggesting interstitial inflammatory cell infiltration, globular sclerosis and other diseases.\u003c/p\u003e\n\u003cp\u003eIn conclusion, the findings of this study indicate that patients diagnosed with MN in the Hechi group in Guangxi exhibited more pronounced clinical and pathological manifestations and a poorer renal prognosis compared to those in the Shenzhen group in Guangdong. This is a very valuable research direction, but the article also has some disadvantages, regional differences by what mechanism to affect the onset, progression and prognosis of PMN patients, is not clear, such as socio-economic situation, genetic susceptibility, environmental factors, growth and development, dietary factors, hypertension and diabetes, education and health literacy, healthcare resources, etc., there is no specific data or in-depth analysis to support the exact association of these factors with PMN. Elucidating these mechanisms may contribute to the prevention and treatment of PMN, as well as early intervention in public health, and also have reference significance for the allocation of limited medical resources and health policy formulation for chronic kidney disease. Therefore, we need to further expand the sample size in the future to explore the association between geography and chronic kidney disease in a multi-level context.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the Shenzhen Science and Technology R\u0026amp;D Fund (No. JCYJ20190806163801637) and the Shenzhen Medical Key Discipline Construction Fund (No. SZXK009).\u003c/p\u003e\n\u003ch2\u003eAuthor contributions\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eMeifang Shang, Shengchun Wu，Yuan Cheng, Dongli Qi, Ji Cen, Xun Qin, and Qijun Wan participated in the investigation of patients and the analysis of data, Meifang Shang wrote the paper, and Zhe Wei planned and was responsible for these studies.\u003c/p\u003e\n\u003ch2\u003eConflict of Interest Statement\u003c/h2\u003e\n\u003cp\u003e\u0026nbsp;All authors declare no conflicts of interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to the retrospective nature of the study, waived the need of obtaining informed consent.\u003c/p\u003e\n\u003ch2\u003eData availability statement\u003c/h2\u003e\n\u003cp\u003eThe datasets used and analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article [and its supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe requirement for obtaining informed consent is exempted due to the retrospective nature of the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRonco P, Debiec H. Pathophysiological advances in membranous nephropathy: time for a shift in patient's care. Lancet. 2015;385(9981):1983-92. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(15)60731-0\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(15)60731-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 26090644.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFogo AB, Lusco MA, Najafian B, Alpers CE. AJKD Atlas of Renal Pathology: Membranous Nephropathy. Am J Kidney Dis. 2015;66(3):e15-7. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1053/j.ajkd.2015.07.006\u003c/span\u003e\u003cspan address=\"10.1053/j.ajkd.2015.07.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 26300203.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGu Y, Xu H, Tang D. Mechanisms of Primary Membranous Nephropathy. Biomolecules. 2021;11(4):513. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/biom11040513\u003c/span\u003e\u003cspan address=\"10.3390/biom11040513\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 33808418; PMCID: PMC8065962.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeri KC, Blumenthal S, Kulkarni V, Beck L, Chongkrairatanakul T. Primary membranous nephropathy: comprehensive review and historical perspective. Postgrad Med J. 2019;95(1119):23\u0026ndash;31. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/postgradmedj-2018-135729\u003c/span\u003e\u003cspan address=\"10.1136/postgradmedj-2018-135729\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2019 Jan 25. PMID: 30683678.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlassock RJ. Diagnosis and natural course of membranous nephropathy.Semin Nephrol.2003;23(4):324\u0026thinsp;\u0026ndash;\u0026thinsp;32.doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s0270-9295(03)00049-4\u003c/span\u003e\u003cspan address=\"10.1016/s0270-9295(03)00049-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 12923720.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFervenza FC, Sethi S, Specks U. Idiopathic membranous nephropathy: diagnosis and treatment. Clin J Am Soc Nephrol. 2008;3(3):905\u0026thinsp;\u0026ndash;\u0026thinsp;19. doi: 10.2215/CJN.04321007. Epub 2008 Jan 30. PMID: 18235148.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCattran DC. Outcomes research in glomerulonephritis. Semin Nephrol. 2003;23(4):340\u0026thinsp;\u0026ndash;\u0026thinsp;54. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s0270-9295(03)00062-7\u003c/span\u003e\u003cspan address=\"10.1016/s0270-9295(03)00062-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 12923722.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNoel LH, Zanetti M, Droz D, Barbanel C. Long-term prognosis of idiopathic membranous glomerulonephritis. Study of 116 untreated patients. Am J Med. 1979;66(1):82\u0026ndash;90. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/0002-9343(79)90486-8\u003c/span\u003e\u003cspan address=\"10.1016/0002-9343(79)90486-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 420255.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu X, Wang G, Chen N, Lu T, Nie S, Xu G, Zhang P, Luo Y, Wang Y, Wang X, Schwartz J, Geng J, Hou FF. Long-Term Exposure to Air Pollution and Increased Risk of Membranous Nephropathy in China. J Am Soc Nephrol. 2016;27(12):3739\u0026ndash;3746. doi: 10.1681/ASN.2016010093. Epub 2016 Jun 30. PMID: 27365535; PMCID: PMC5118492.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDonadio JV Jr, Torres VE, Velosa JA, Wagoner RD, Holley KE, Okamura M, Ilstrup DM, Chu CP. Idiopathic membranous nephropathy: the natural history of untreated patients. Kidney Int. 1988;33(3):708\u0026thinsp;\u0026ndash;\u0026thinsp;15. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/ki.1988.56\u003c/span\u003e\u003cspan address=\"10.1038/ki.1988.56\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 3367560.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchieppati A, Mosconi L, Perna A, Mecca G, Bertani T, Garattini S, Remuzzi G. Prognosis of untreated patients with idiopathic membranous nephropathy. N Engl J Med. 1993;329(2):85\u0026thinsp;\u0026ndash;\u0026thinsp;9. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJM199307083290203\u003c/span\u003e\u003cspan address=\"10.1056/NEJM199307083290203\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 8510707.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen Y, Tang L, Feng Z, et al. Pathological predictors of renal outcomes in nephrotic idiopathic membranous nephropathy with decreased renal function[J]. J Nephrol, 2014, 27(3):307\u0026ndash;316.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDumoulin A, Hill G S, Montseny J J, et al. Clinical and morphological prognostic factors in membranous nephropathy: significance of focal segmental glomerulosclerosis[J]. Am J Kidney Dis, 2003, 41(1):38\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeeringa S F, Branten A J, Deegens J K, et al. Focal segmental glomerulosclerosis is not a sufficient predictor of renal outcome in patients with membranous nephropathy[J]. Nephrol Dial Transplant, 2007, 22(8):2201\u0026ndash;2207.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Damme B, Tardanico R, Vanrenterghem Y, et al. Adhesions, focal sclerosis, protein crescents, and capsular lesions in membranous nephropathy[J]. J Pathol, 1990, 161(1):47\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe H G, Wu C Q, Ye K, et al. Focal segmental glomerulosclerosis, excluding atypical lesion, is a predictor of renal outcome in patients with membranous nephropathy: a retrospective analysis of 716 cases[J]. BMC Nephrol, 2019, 20(1):328.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi J, Chen B, Gao C, et al. Clinical and pathological features of idiopathic membranous nephropathy with focal segmental sclerosis[J]. BMC Nephrol, 2019, 20(1):467.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD'Agati V D, Fogo A B, Bruijn J A, et al. Pathologic classification of focal segmental glomerulosclerosis: a working proposal[J]. Am J Kidney Dis, 2004, 43(2):368\u0026ndash;382.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcGrogan A, Franssen CF, de Vries CS: The incidence of primary glomerulonephritis worldwide: a systematic review of the literature. Nephrol Dial Transplant 26: 414\u0026ndash;430, 2011 [PubMed] [Google Scholar]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZuo L, Wang M: Beijing Blood Purification Quality Control and Improvement Center: Current status of maintenance hemodialysis in Beijing, China. Kidney Int Suppl 3: 167\u0026ndash;169, 2013 [PMC free article] [PubMed] [Google Scholar]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWakai K, Nakai S, Kikuchi K, Iseki K, Miwa N, Masakane I, Wada A, Shinzato T, Nagura Y, Akiba T: Trends in incidence of end-stage renal disease in Japan, 1983\u0026ndash;2000: age-adjusted and age-specific rates by gender and cause. Nephrol Dial Transplant 19: 2044\u0026ndash;2052, 2004 [PubMed] [Google Scholar]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCen Ji, Hu Haofei, Cheng Yuan, et al.. The analysis of the pathology information and national characteristics of the single centers in Guangxi multi -ethnic groups [J]. Journal of Chengdu Medical College\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQin Xun, Qi Dongli, Cen Ji, Hu Haofei, Cheng Yuan, Wan Qijun, Wei Yan. Analysis of clinical and kidney pathological characteristics of different ethnicic membrane kidney disease [J]. System Medicine, 2022,12: 54\u0026ndash;58\u0026thinsp;+\u0026thinsp;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQin Xun, Qi Dongli, Cen Ji, Hu Haofei, Cheng Yuan, Wan Qijun, Wei Yan. Analysis of clinical and kidney pathological characteristics of specialized membrane kidney disease in different ages [J]. 04: 261\u0026ndash;264.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatzer RE, McClellan WM. Influence of race, ethnicity and socioeconomic status on kidney disease. Nat Rev Nephrol. 2012;8(9):533\u0026ndash;41. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nrneph.2012.117\u003c/span\u003e\u003cspan address=\"10.1038/nrneph.2012.117\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2012 Jun 26. PMID: 22735764; PMCID: PMC3950900.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHossain MP, Palmer D, Goyder E, El Nahas AM. Association of deprivation with worse outcomes in chronic kidney disease: findings from a hospital-based cohort in the United Kingdom. Nephron Clin Pract. 2012;120:c59\u0026ndash;c70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollins JF. Kidney disease in Maori and Pacific people in New Zealand. Clin Nephrol. 2010;74(Suppl. 1):S61\u0026ndash;S65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiez-Roux AV. Bringing context back into epidemiology: variables and fallacies in multilevel analysis. Am J Public Health. 1998;88:216\u0026ndash;222. [PMC free article] [PubMed] [Google Scholar]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWard MM. Access to care and the incidence of end-stage renal disease due to diabetes. Diabetes Care. 2009;32:1032\u0026ndash;1036. [PMC free article] [PubMed] [Google Scholar]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKershaw KN, et al. Metropolitan-level racial residential segregation and black-white disparities in hypertension. Am J Epidemiol. 2011;174:537\u0026ndash;545. [PMC free article] [PubMed] [Google Scholar]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamachandran R, Sulaiman S, Chauhan P, Ulasi I, Onu U, Villaneuva R, Alam MR, Akhtar F, Vincent L, Aulakh GS, Sutranto AL, Zakharova E, Jha V. Challenges in Diagnosis and Management of Glomerular Disease in Resource-Limited Settings. Kidney Int Rep. 2022;7(10):2141\u0026ndash;2149. doi: 10.1016/j.ekir.2022.07.002. PMID: 36217525; PMCID: PMC9546742.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCanney M, Induruwage D, Sahota A, McCrory C, Hladunewich MA, Gill J, Barbour SJ. Socioeconomic Position and Incidence of Glomerular Diseases. Clin J Am Soc Nephrol. 2020;15(3):367\u0026ndash;374. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2215/CJN.08060719\u003c/span\u003e\u003cspan address=\"10.2215/CJN.08060719\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2020 Feb 20. PMID: 32079609; PMCID: PMC7057310.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeeringa SF, Branten AJ, Deegens JK, et al. Focal segmental glomerulosclerosis is not a sufficient predictor of renal outcome in patients with membranous nephropathy [J]. Nephrol Dial Transplant, 2007, 22(8) : 2201\u0026ndash;2207.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang J, Pan M, Zhang J, et al. Serum uric acid is an independ- ent predictor of renal outcomes in patients with idiopathic mem- branous nephropathy [J]. Int Urol Nephrol, 2019, 51 (10) : 1797\u0026ndash;1804.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang XD, Cui Z, Zhang MF. Clinical implications of pathologi- cal features of primary mem branous nephropathy [J]. BMC Nephrol, 2018, 19(1) : 215\u0026ndash;224.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCHEN Jia, CHEN Yuan, HU Haofei, QI Dongli, GUAN Mijie, WAN Qijun. Clinicopathologic characteristics and prognosis of idiopathic membranous nephropathy with hypertension[J]. Chinese Journal of Nephrology,2021,08:677\u0026ndash;681.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Primary membranous nephropathy, geography, epidemiological features, prognosis","lastPublishedDoi":"10.21203/rs.3.rs-4279443/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4279443/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePrimary membranous nephropathy (PMN) is the predominant histological subtype of nephrotic syndrome in adults, exhibiting a wide range of prognostic outcomes. This study employed a retrospective cohort design to investigate patients with confirmed PMN diagnosis via renal biopsy at the Second People's Hospital of Shenzhen, Guangdong Province between January 2008 and March 2020, as well as at Hechi People's Hospital from June 2013 to September 2021. Utilizing regression analysis, survival analysis, and cross-examination, the study aimed to compare epidemiological characteristics and prognostic indicators among PMN patients across different geographic regions. The study included a cohort of 662 patients diagnosed with PMN, with 362 (54.7%) of these patients residing in Guangxi. Patients in Guangxi exhibited characteristics such as advanced age, a higher proportion of males, elevated systolic and diastolic blood pressure, increased levels of serum PLA2R antibody concentration, uric acid, total cholesterol, and urine protein, as well as lower levels of hemoglobin, serum albumin, and baseline estimated glomerular filtration rate (eGFR). Results from multivariate Cox analysis indicated that regional disparities were identified as independent risk factors for a 30% decline in eGFR among PMN patients, with the risk of reaching renal endpoint being 7.84 times higher in Guangxi compared to Guangdong. The KM curve analysis indicated a significantly higher incidence of reaching the renal endpoint in Guangxi (P\u0026lt;0.0001). Furthermore, the interaction test revealed significant regional variations in the impact on renal endpoint incidence across different eGFR quantiles, suggesting a notable interaction effect.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusion: The clinical and pathological manifestations of primary membranous nephropathy (PMN) patients in Guangxi are more severe and have a worse renal prognosis compared to those in Shenzhen, Guangdong. Therefore, it is crucial to prioritize the allocation of limited medical resources and implement early intervention strategies in the formulation of chronic kidney disease health policies to effectively prevent and treat PMN in this region.\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Contrasting Prognoses: Regional Disparities in Primary Membranous Nephropathy Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-07 19:18:33","doi":"10.21203/rs.3.rs-4279443/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"47e24584-0405-4215-a87d-f86a91697e7f","owner":[],"postedDate":"May 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":31518925,"name":"Health sciences/Nephrology/Kidney diseases/Glomerular diseases/Membranous nephropathy"},{"id":31518926,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2024-10-17T07:09:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-07 19:18:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4279443","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4279443","identity":"rs-4279443","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}