Serum total cholesterol serves as an independent risk factor for the progression of disease in idiopathic membranous nephropathy.

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Nan Chang, Yue Wang, Xueli Bai, Fulu Chu, Yuanquan Si, Yajuan Shen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5341556/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background This study sought to uncover potential risk factors linked to disease development by analyzing the medical and renal histology features of individuals with idiopathic membranous nephropathy associated with nephrotic syndrome. Methods Our retrospective research involved 373 patients who met the specified inclusion criteria and had a kidney biopsy diagnosis between January 2016 and August 2023. The crowds recorded the clinical and pathological characteristics at baseline and assessed the outcomes during the follow-up period. Researchers used a binary logistic regression analysis to identify the risk factors associated with disease progression in individuals with membranous nephropathy. We categorized the patients into two distinct groups: those with progressing renal disease and those without. Results Thirty-six (9.65%) people experienced nephropathy progression following an average follow-up period of 15 (inter-quartile range 9,24) months. Serum total cholesterol levels had a substantial negative connection with albumin, as evidenced by Spearman's rho = -0.39 ( p < 0.001). The ROC curve for serum total cholesterol indicated a sensitivity of 69.4% and a specificity of 76.9% in predicting nephropathy development. The area beneath the curve was 0.789 ( p < 0.001, 95% CI: 0.725–0.852). Logistic multivariate analysis revealed that total cholesterol levels in the blood (OR = 1.554, 95% CI: 1.294–1.861, p < 0.001) constitute an independent risk factor for nephropathy development. Conclusion In patients with membranous nephropathy and nephrotic syndrome, serum total cholesterol levels act as a separate danger indicator for disease advancement. Membranous nephropathy serum total cholesterol cohort study risk factor Figures Figure 1 Figure 2 Figure 3 Background The autoimmune condition known as membrane nephropathy (MN) is non-inflammatory. When you look at MN under a microscope, you can see that electron-dense immune deposits build up under the epithelium. This makes the membrane thicken and spicules grow. Immunoglobulin G (IgG), related antigens, complement, and membrane assault complexes make up these immunological deposits [ 1 ]. MN falls into one of two groups: secondary or idiopathic. We refer to cases with an unclear cause as idiopathic membranous nephropathy (IMN). In contrast, other factors including systemic lupus erythematosus, infections, neoplasms, pharmaceuticals, and toxins lead to secondary membranous nephropathy. Typically, doctors diagnose MN between the ages of 50 and 60, but it can manifest at any age. It is more prevalent in men and often has a lengthy natural course, with a gender ratio of 2:1. The incidence of MN has significantly increased in China, with patients presenting at a younger age, possibly due to prolonged exposure to rising PM2.5 concentrations. About thirty percent of such individuals will advance to renal failure in its final stages within a few years [ 2 , 3 ]. Lipid metabolism's significance in the emergence of renal disorders has drawn more attention in recent years. Lipids generate a substantial amount of adenosine triphosphate (ATP), which is essential for maintaining the physiological functions of the kidneys, including glomerular filtration, hormone synthesis, and tubular reabsorption [ 4 ]. When lipid environments and intracellular components are out of balance, it's called lipotoxicity. This can cause intracellular messenger pathways to fire in the wrong way, organelle dysfunction, chronic inflammation, and even cell death [ 5 ]. Research has shown that the more dyslipidemic components there are, the higher the chance of developing chronic kidney disease. Despite multivariate adjustment, our findings indicate that low high-density lipoprotein cholesterol (HDL-C) and hypercholesterolaemia correlate with an elevated incidence of albuminuria and that hypercholesterolaemia substantially reduces estimated glomerular filtration rate (eGFR) [ 6 ]. The high triglyceride group in IgA nephropathy had worse global and segmental sclerosis, pericardial adhesions, and thylakoid dilation and proliferation. A logistic regression analysis revealed that the high triglyceride group had an independently greater risk of glomerulosclerosis than the normal triglyceride group (OR = 1.791, 95% CI: 1.111–2.887, p = 0.017). Additionally, triglyceride-lowering therapy may help prevent the progression of glomerulosclerosis [ 7 ]. Moreover, longitudinal investigations of IMN with biopsy-proven results showed the strongest correlation between non-HDL cholesterol levels in the lipid profile and proteinuria. Elevated levels of cholesterol and non-HDL cholesterol at the study's onset were associated with an increased likelihood of chronic proteinuria in individuals with IMN [ 8 ]. Studies on how blood total cholesterol contributes to the development of IMN linked to nephrotic syndrome are scarce. To thoroughly evaluate the clinical and pathological characteristics of IMN patients with nephrotic syndrome and their progress under various therapies, it is crucial to rigorously analyze and identify potential hazards and factors influencing the course of IMN. This analysis is clinically significant for assessing the course of IMN patients and guiding the choice of suitable and customized treatment regimens. Materials and methods Patients Study participants were chosen from patients diagnosed with IMN via renal biopsy at the Department of Nephrology,Provincial Hospital,Shandong First Medical University from January 2016 to August 2023. The research received approval from the Medical Ethics Committee of Shandong First Medical University's Shandong Provincial Hospital (SZRJJ: NO. 2021-017) and strictly complied with established medical ethics norms. The following requirements have to be fulfilled by every patient: (1) age ≥ 18 years; (2) newly diagnosed IMN confirmed by renal biopsy; (3) Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula-calculated estimated glomerular filtration rate (eGFR) > 15 mL/min/1.73 m²; (4) A minimum observation period of six months; (5) Nephrotic proteinuria is characterised by serum albumin levels 3.5 g/d. The exclusion criteria included the following: (1) secondary illnesses, such as autoimmune disorders, malignancies, various infections, and hepatitis B virus (HBV) infection; (2) incomplete baseline data, such as serum creatinine levels and 24-hour urine protein quantification; (3) the use of immunosuppressants within the six months prior to admission. Prior research serves as the theoretical or reference foundation for selection criteria [ 9 , 10 ]. The criteria for the inclusion and exclusion of the investigation's participants (Fig. 1). Clinical and laboratory parameters We gleaned anthropometric parameters like systolic and diastolic blood pressure, along with general demographic information like age and sex, from digital health records. Before their initial admission for a kidney biopsy, all patients underwent blood drawing for biochemical analysis. The lab data included blood urea nitrogen (BUN), serum complement C1q, eGFR, albumin (ALB), total cholesterol (TC), triglycerides (TG), serum low-density lipoprotein (LDL), serum high-density lipoprotein (HDL), fasting blood glucose (FBG), anti-phospholipase A2 receptor antibody (Anti-PLA2R), proteinuria, serum complement C3, serum complement C4, free light chain kappa (KAP), free light chain lambda (LAM), and the ratio of free light chain KAP to free light chain LAM. The Sysmex UF-5000 urinary sediment analyzer used the principles of flow cytometry and electrical impedance analysis to identify microscopic hematuria. We detected the anti-phospholipase A2 receptor antibody using flow fluorescence (Tesmi-F3999), and obtained all other data from the aforementioned experiments using the Beckman Coulter AU5800. CKD-EPI developed a formula to calculate the estimated glomerular filtration rate (eGFR) [ 11 ]. Pathological data We examined the tissue samples from each kidney biopsy patient using immunofluorescence staining, electron microscopy, and standard light microscopy. Two experienced pathologists at our hospital evaluated the results. We used the Ehrenreich-Churg criteria to categorize the pathological staging into stages I, II, III, and IV. When we observe two periods, we select the longer one. We qualitatively assessed the levels of C3, IgA, IgM, IgG, and C1q deposition based on fluorescence intensity. Some important features of focal segmental glomerulosclerosis (FSGS) lesions are a small rise in thylakoid stroma and glomerular adhesion, along with a pattern of damage to the glomeruli that can be seen under a light microscope. Treatment and Outcome Treatment and Outcome As per the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines, patients get supportive and immunosuppressive therapy contingent upon the severity of proteinuria, renal function, and duration of monitoring. Supportive therapy includes medications such as diuretics, anticoagulants, renin-angiotensin system (RAS) blockers, and famotidine. Immunosuppressive therapy consists of corticosteroids, cyclophosphamide, tacrolimus, cyclosporine, and rituximab. This investigation aimed to assess the course of renal disease, defined by a decline in eGFR above 30% or the development of end stage renal disease (ESRD). The cohort without renal disease progression exhibited a reduction in glomerular filtration rate of ≤ 30% from baseline [ 12 ]. Patients in complete remission (CR) were required to exhibit minimal proteinuria (less than 0.3 g/d) and normal serum albumin and creatinine levels. partial remission (PR) was characterised by normal serum albumin (≥ 3.5 g/dL), stable renal function, and urine protein levels ranging from 0.3 to 3.5 g/24 hours, which were at least 50% reduced from baseline. Both complete and partial remission were classified as remission. No remission (NR) was identified by any of the following criteria: (1) proteinuria levels of 3.5 g/24 hours or higher, (2) proteinuria with less than a 50% reduction from baseline, or (3) Serum albumin levels < 3.5 g/dL [ 13 ]. Statistical analysis Data analysis was performed utilizing SPSS version 26.0, a statistical software. Data for continuous variables exhibiting irregular distributions are presented using the median and interquartile range (IQR). The data are displayed as counts and proportions for categorical variables. Univariate comparisons were performed utilizing the Mann-Whitney test for non-normally distributed data and the χ² test, commonly referred to as Fisher's exact test, for categorical variables. Correlations between baseline serum total cholesterol and clinical parameters were analyzed using Spearman correlation. To identify independent risk factors, we performed one-way logistic regression analyses and incorporated variables with a p-value below 0.1 into multifactorial logistic regression analysis. We evaluated the predictive significance of serum total cholesterol levels for the progression of IMN with nephrotic syndrome using receiver operating characteristic (ROC) curves. Statistical significance was defined as P-values below 0.05. The aim of this study was to assess the course of renal disease, defined by a decline in eGFR above 30% or the emergence of End Stage Renal Disease (ESRD). After excluding variables with more than 10% missing data, the proportion of missing data for the remaining variables was less than 10%. The median of the non-missing values was used to interpolate the missing values. Plots were all created with GraphPad Prism 9.0. Results Characteristics of the cohort The investigation included 373 participants following the application of exclusion criteria (Fig. 1). At the time of kidney biopsy, the average age of patients in this cohort was 49 years (inter-quartile range 39, 56), with a male to female ratio of 1.94:1. 316 (84.7%) patients received immunosuppressive medication at the start of the monitoring period, while 57 (15.3%) patients received supportive care. Following an average monitoring period of 15 (inter-quartile range 9, 24) months, 36 (9.65%) patients the designated objective for renal disease, whereas 337 (90.35%) patients did not. As compared to the starting point of the two groups, there were big differences ( p < 0.05) in systolic blood pressure, serum total cholesterol, serum LDL cholesterol, serum complement C1q, free light chain KAP, free light chain LAM, and urine protein remission. Patients with progressive nephropathy exhibited a median systolic blood pressure of 142 (inter-quartile range 127.5, 153.5) mmHg, a median serum total cholesterol of 9.74 (inter-quartile range 8.52, 10.89) mmol/L, a median serum LDL of 6.17 (inter-quartile range 5.11, 7.51) mmol/L, a median serum complement C1q of 223.1 (inter-quartile range 192, 238.3) mg/L, a median free light chain KAP of 31.75 (inter-quartile range 26.93, 40.67) mg/L, a median free light chain LAM of 44.1 (inter-quartile range 32.3, 52.73) mg/L, and a urinary protein remission rate of 52.8% (19/36).The patients in the non-progressing nephropathy group, on the other hand, had a median systolic blood pressure of 135 (inter-quartile range 123, 148) mmHg, a median serum total cholesterol of 7.67 (inter-quartile range 6.40, 8.87) mmol/L, a median serum LDL of 4.92 (inter-quartile range 4.0, 6.02) mmol/L, a median serum complement C1q of 200 (inter-quartile range 173, 225.99) mg/L, a median free light chain KAP of 29.1 (inter-quartile range 23.15, 36.4) mg/L, a median free light chain LAM of 35.1 (inter-quartile range 28.6 44.1) mg/L, and a 67.9% remission rate for urinary protein (Table 1, attachment Table − 1). Comparing the Pathological Features of the Two Patient Groups The glomerulosclerosis rate was significantly higher in the nephropathy development cohort compared to the clinical symptoms observed in both categories (p < 0.05). However, the binary logistic univariate analysis did not find the rate of sclerosis as a predictor for renal progression (OR: 17.647; 95% CI: 0.445-699.233; p = 0.126). The pathomorphological staging of the two groups of patients with IMN did not differ significantly. Both groups had a lot of stages I and II lesions and were not as bad. This finding is consistent with a lower rate of nephropathy progression (9.65%) observed during follow-up. Different between the two subgroups (p < 0.05) was also seen in the serum levels of component C1q, which were significantly higher in the group whose nephropathy was getting worse. However, the deposition of C1q in renal tissue showed no substantial distinction between the two groups (p > 0.05) (Table 2 ). Table 2 Comparison of Pathological Features Between the Two Patient Groups Total cohort Kidney disease Progression No kidney disease progression p value GER n(%) 2.63%(0,6.45%) 3.6(0,7.69%) 2.44%(0,5.97%) 0.033 C3 deposits n(%) 283(75.9%) 27(75.0%) 256(76%) 0.849 IgG deposits n(%) 357(95.7%) 34(94.4%) 323(95.8%) 0.966 C1q deposits n(%) 106(28.4%) 10(27.8%) 96(28.5%) 0.851 Morphological staging n(%) 0.335 I + II 369(98.9%) 35(97.2%) 334(99.1%) III + IV 4(1.1%) 1(2.8%) 3(0.9%) Lesions of FSGS n(%) 56(14.9%) 7(19.4%) 48(14.2%) 0.407 GER: glomerulosclerosis rate; FSGS: focal segmental glomerulosclerosis ;Data represent the median and interquartile range, as well as counts and percentages. Correlation between baseline serum total cholesterol levels and clinical parameters Serum total cholesterol levels had a positive connection with serum urea nitrogen (r = 0.18, p < 0.01) and serum complement C4 (r = 0.17, p < 0.01). In contrast, serum total cholesterol levels had a negative correlation with serum albumin (r = -0.39, p < 0.05), serum glomerular filtration rate (r = -0.12, p < 0.05), and serum IgG (r = -0.25, p < 0.01). Serum total cholesterol, 24-hour urine protein, systolic blood pressure, free light chain Kappa (KAP), free light chain Lambda (LAM), and serum complement C3 exhibited no significant correlation with each other (p > 0.05) (Fig. 2). Clinical risk factors for kidney disease progression To further examine the association between baseline data at renal biopsy and the progression of renal disease, we included univariate factors with p < 0.1: systolic blood pressure, age, serum total cholesterol, antiphospholipase A2 receptor (PLA2R) antibody, serum complement C3, free light chain KAP, and free light chain LAM in the bivariate logistic multifactorial analysis. The findings demonstrated that serum total cholesterol was an important predictor for the decline of renal function (p < 0.001, OR 1.554, 95% CI: 1.294–1.866) (Table 3 ). Because the two groups had different starting blood total cholesterol levels, we used the Receiver Operating Characteristic (ROC) curve to look at how serum total cholesterol could be used to predict how quickly nephropathy would get worse. To see how nephropathy would get worse, the area under the ROC curve for serum total cholesterol was 0.789 ( p < 0.001, 95% CI: 0.725–0.852), which means it was 69.4% sensitive and 76.9% specific. We established the optimal cut-off value of baseline total blood cholesterol for distinguishing nephropathy development at 9.02 mmol/L through the calculation of the ideal Youden index (Fig. 3). Table 3 Binary Logistic Regression Analysis of Risk Factors for the Progression of Kidney Disease Univariate Multifactorial r p r p SBP 1.02 0.029 1.012 0.211 Age 1.027 0.081 1.013 0.414 TC 1.601 < 0.001 1.554 < 0.001 C3 4.157 0.063 3.018 0.167 FL-KAP 1.027 0.01 1.001 0.969 FL-LAM 1.03 < 0.001 1.021 0.255 Anti-PLA2R antibody 1.001 0.074 1.001 0.194 SBP: systolic blood pressure;TC: total cholesterol; C3: serum complement C3; FL-KAP: free light-chain KAP; FL-LAM: free light-chain LAM; Discussion We looked back at 373 patients who had nephrotic proteinuria and idiopathic membranous nephropathy. We examined the link between serum total cholesterol levels measured during kidney biopsy and the progression of patients illness. Our research showed that high serum total cholesterol at the start of the disease is a separate risk factor for kidney damage getting worse in IMN. This was shown by multifactorial logistic analysis (OR 1.554, 95% CI: 1.294–1.866, p < 0.001). The total serum cholesterol level is associated with the progression of nephropathy among individuals with idiopathic membranous nephropathy and nephrotic syndrome. The suggested mechanism involves too much cholesterol building up because of problems with reverse cholesterol transport. This hurts podocytes and renal tubular cells, leading to mitochondrial stress, inflammation, actin cytoskeletal remodeling, insulin resistance, endoplasmic reticulum stress, and finally, cell death [ 14 , 15 ]. Impaired podocytes exacerbate the glomerular inflammatory environment by expressing receptors for inflammatory cytokines and producing chemokines. By altering the distribution of cholesterol and the makeup of lipoproteins in plasma, tissues, and cellular organelles, inflammatory stress disturbs lipid homeostasis. This process speeds up the growth of glomerulosclerosis and renal fibrosis. It also damages podocytes more and makes them make more lipids [ 16 ]. This study evaluated the baseline features at renal biopsy in patients with two distinct outcomes of membranous nephropathy. The two groups did not differ in the beginning 24-hour protein levels in the urine; however, the group that developed nephropathy at follow-up had a lower urinary protein remission rate, indicating a significant difference between the groups (p = 0.034). Chronic proteinuria is believed to correlate with a heightened risk of renal insufficiency, consistent with prior perspectives [ 17 , 18 ]. The histologic staging of renal biopsies remains contentious in forecasting renal prognosis. In the middle of 1999, Marx et al. found that membranous renal stage III/IV had a higher risk ratio of 5.3 (p = 0.002) than stage I/II end-stage renal disease (ESRD). Earlier and later studies have found that there isn't a statistically significant link between different stages of disease and the prognosis of the kidneys. This is probably because histologic severity includes more than just the condition of the glomerular basement membrane; it also includes segmental glomerular sclerosis and tubulointerstitial damage [ 19 , 20 ]. Other research, on the other hand, shows that FSGS lesions, excluding atypical lesions, can predict the progression of IMN to a 50% drop in the estimated rate of glomerular filtration or end-stage renal disease. This may be ascribed to the disturbance induced by unusual focal segmental lesions and the significant variation in the prevalence of FSGS lesions among patients with IMN in studies [ 21 – 23 ]. One advantage of our study is that it is the first to examine the correlation between blood cholesterol levels and the progression of nephropathy in patients with IMN. The convenience of specimen collection, the simple testing procedure, and the quick reporting of results are some advantages of assessing total serum cholesterol. This study still has a number of shortcomings. First of all, it was a single-center retrospective study. Regional limitations resulted in a relatively homogeneous patient sample, creating selection bias and restricting the data's applicability to patients with membranous nephropathy in other areas. Furthermore, all of the patients came from a single Chinese healthcare facility. Therefore, to confirm our findings, a prospective, multicenter investigation with a larger sample size is required. Conclusions Our study indicates that baseline blood cholesterol is an independent risk factor for the advancement of nephropathy in patients with IMN. Consequently, blood total cholesterol levels in IMN patients warrant scrutiny. Abbreviations MN Membrane nephropathy IgG Immunoglobulin G IMN Idiopathic membranous nephropathy ATP Adenosine triphosphate HDL-C High-density lipoprotein cholesterol CKD-EPI Chronic Kidney Disease Epidemiology Collaboration eGFR Estimated glomerular filtration rate HBV Hepatitis B virus BUN Blood urea nitrogen ALB Albumin TC Total cholesterol , TG Triglycerides LDL Low-density lipoprotein HDL High-density lipoprotein , FBG Fasting blood glucose Anti-PLA2R Anti-phospholipase A2 receptor antibody KAP Free light chain kappa LAM Free light chain lambda FSGS Focal segmental glomerulosclerosis KDIGO Kidney disease: improving global outcomes RAS Renin-angiotensin system ESRD End Stage Renal Disease CR Complete remission PR Partial remission NR No remission IQR Interquartile range ROC Receiver operating characteristic Declarations Acknowledgments We would like to thank all of the patients who took part in this research. Author contributions N.C, data curation, investigation, study design, writing original draft. Y.W, data curation, formal analysis, software. X.L.B, data analysis, andrevision of the manuscript. F.L.C, Y.Q.S and Y.J.S made significant contributions to the conception and design. All authors read and approved the published version of the manuscript. Funding The Shandong Province Natural Science Foundation (ZR2021MH295) and a cross-sectional thrombocytopenia project (1665387000050) provided funding for this work. Data availability statement On request, data will be made available. Ethics approval and consent to participate Every method employed in this study conformed with the 1964 Declaration of Helsinki and its subsequent amendments, as well as the ethical criteria established by the School of Medicine Institutional Research Committee at the Provincial Hospital (SZRJJ: NO. 2021-017). Each patient provided their informed consent. Consent for publication Not applicable. 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Yamaguchi M, et al. Urinary protein and renal prognosis in idiopathic membranous nephropathy: a multicenter retrospective cohort study in Japan. Ren Fail. 2018;40(1):435–41. Marx BE, Marx M. Prediction in idiopathic membranous nephropathy. Kidney Int. 1999;56(2):666–73. Tsai S-F, Wu M-J, Chen C-H. Low serum C3 level, high neutrophil-lymphocyte-ratio, and high platelet-lymphocyte-ratio all predicted poor long-term renal survivals in biopsy-confirmed idiopathic membranous nephropathy. Sci Rep. 2019;9(1):6209. He H-G, 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. BMC Nephrol. 2019;20(1):328. Heeringa SF, et al. Focal segmental glomerulosclerosis is not a sufficient predictor of renal outcome in patients with membranous nephropathy. Nephrology. Volume 22. Dialysis, Transplantation: Official Publication of the European Dialysis and Transplant Association - European Renal Association; 2007. pp. 2201–7. 8. Sun M, et al. Clinical characteristics and prognosis of patients with idiopathic membranous nephropathy with kidney tubulointerstitial damage. Ren Fail. 2023;45(1):2205951. Tables Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.xls 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5341556","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":375222419,"identity":"22912bcd-3f23-483a-b0df-c6e61963c3a7","order_by":0,"name":"Nan Chang","email":"","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University","correspondingAuthor":false,"prefix":"","firstName":"Nan","middleName":"","lastName":"Chang","suffix":""},{"id":375222431,"identity":"ec8a3e05-ba7f-4551-be98-bae1a62cfd4e","order_by":1,"name":"Yue Wang","email":"","orcid":"","institution":"Shandong Provincial Hospital 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13:53:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5341556/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5341556/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70381230,"identity":"6e6fd6a9-0461-4d88-aab9-0568294e3361","added_by":"auto","created_at":"2024-12-02 16:06:04","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":154207,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure1FlowchartforPatientInclusion.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5341556/v1/b6d60a52f619de1a79749dce.jpg"},{"id":70380997,"identity":"a1776031-167f-4599-aa3d-3f0aabfaa037","added_by":"auto","created_at":"2024-12-02 15:58:01","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":225965,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure2SpearmanrCorrelationofData1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5341556/v1/fc81902e2d1ad6a3042225b1.jpg"},{"id":70381228,"identity":"bb4c68b9-d248-4df4-9bc6-342f1e486444","added_by":"auto","created_at":"2024-12-02 16:06:02","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":49843,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure3ROCcurveROCofData1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5341556/v1/862cbabbff4872f0a9e69234.jpg"},{"id":72782823,"identity":"ec13a58c-3674-4e93-b779-488c3d471c37","added_by":"auto","created_at":"2025-01-02 06:24:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":971405,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5341556/v1/560d0527-653c-436f-8d63-cf2a41c2debc.pdf"},{"id":70380995,"identity":"9a1b57a8-2871-4522-b95f-abc8c574c097","added_by":"auto","created_at":"2024-12-02 15:58:01","extension":"xls","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":26624,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.xls","url":"https://assets-eu.researchsquare.com/files/rs-5341556/v1/b241f04cafb1d34d9c0e6782.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"Serum total cholesterol serves as an independent risk factor for the progression of disease in idiopathic membranous nephropathy.","fulltext":[{"header":"Background","content":"\u003cp\u003eThe autoimmune condition known as membrane nephropathy (MN) is non-inflammatory. When you look at MN under a microscope, you can see that electron-dense immune deposits build up under the epithelium. This makes the membrane thicken and spicules grow. Immunoglobulin G (IgG), related antigens, complement, and membrane assault complexes make up these immunological deposits [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. MN falls into one of two groups: secondary or idiopathic. We refer to cases with an unclear cause as idiopathic membranous nephropathy (IMN). In contrast, other factors including systemic lupus erythematosus, infections, neoplasms, pharmaceuticals, and toxins lead to secondary membranous nephropathy. Typically, doctors diagnose MN between the ages of 50 and 60, but it can manifest at any age. It is more prevalent in men and often has a lengthy natural course, with a gender ratio of 2:1. The incidence of MN has significantly increased in China, with patients presenting at a younger age, possibly due to prolonged exposure to rising PM2.5 concentrations. About thirty percent of such individuals will advance to renal failure in its final stages within a few years [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLipid metabolism's significance in the emergence of renal disorders has drawn more attention in recent years. Lipids generate a substantial amount of adenosine triphosphate (ATP), which is essential for maintaining the physiological functions of\u003c/p\u003e \u003cp\u003ethe kidneys, including glomerular filtration, hormone synthesis, and tubular reabsorption [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. When lipid environments and intracellular components are out of balance, it's called lipotoxicity. This can cause intracellular messenger pathways to fire in the wrong way, organelle dysfunction, chronic inflammation, and even cell death [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Research has shown that the more dyslipidemic components there are, the higher the chance of developing chronic kidney disease. Despite multivariate adjustment, our findings indicate that low high-density lipoprotein cholesterol (HDL-C) and hypercholesterolaemia correlate with an elevated incidence of albuminuria and that hypercholesterolaemia substantially reduces estimated glomerular filtration rate (eGFR) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The high triglyceride group in IgA nephropathy had worse global and segmental sclerosis, pericardial adhesions, and thylakoid dilation and proliferation. A logistic regression analysis revealed that the high triglyceride group had an independently greater risk of glomerulosclerosis than the normal triglyceride group (OR\u0026thinsp;=\u0026thinsp;1.791, 95% CI: 1.111\u0026ndash;2.887, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017). Additionally, triglyceride-lowering therapy may help prevent the progression of glomerulosclerosis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Moreover, longitudinal investigations of IMN with biopsy-proven results showed the strongest correlation between non-HDL cholesterol levels in the lipid profile and proteinuria. Elevated levels of cholesterol and non-HDL cholesterol at the study's onset were associated with an increased likelihood of chronic proteinuria in individuals with IMN [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStudies on how blood total cholesterol contributes to the development of IMN linked to nephrotic syndrome are scarce. To thoroughly evaluate the clinical and pathological characteristics of IMN patients with nephrotic syndrome and their progress under various therapies, it is crucial to rigorously analyze and identify potential hazards and factors influencing the course of IMN. This analysis is clinically significant for assessing the course of IMN patients and guiding the choice of suitable and customized treatment regimens.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003e Study participants were chosen from patients diagnosed with IMN via renal biopsy at the Department of Nephrology,Provincial Hospital,Shandong First Medical University from January 2016 to August 2023. The research received approval from the Medical Ethics Committee of Shandong First Medical University's Shandong Provincial Hospital (SZRJJ: NO. 2021-017) and strictly complied with established medical ethics norms. The following requirements have to be fulfilled by every patient: (1) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; (2) newly diagnosed IMN confirmed by renal biopsy; (3) Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula-calculated estimated glomerular filtration rate (eGFR)\u0026thinsp;\u0026gt;\u0026thinsp;15 mL/min/1.73 m\u0026sup2;; (4) A minimum observation period of six months; (5) Nephrotic proteinuria is characterised by serum albumin levels\u0026thinsp;\u0026lt;\u0026thinsp;30 g/L and 24-hour urine protein\u0026thinsp;\u0026gt;\u0026thinsp;3.5 g/d. The exclusion criteria included the following: (1) secondary illnesses, such as autoimmune disorders, malignancies, various infections, and hepatitis B virus (HBV) infection; (2) incomplete baseline data, such as serum creatinine levels and 24-hour urine protein quantification; (3) the use of immunosuppressants within the six months prior to admission. Prior research serves as the theoretical or reference foundation for selection criteria [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The criteria for the inclusion and exclusion of the investigation's participants (Fig.\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClinical and laboratory parameters\u003c/h3\u003e\n\u003cp\u003eWe gleaned anthropometric parameters like systolic and diastolic blood pressure, along with general demographic information like age and sex, from digital health records. Before their initial admission for a kidney biopsy, all patients underwent blood drawing for biochemical analysis. The lab data included blood urea nitrogen (BUN), serum complement C1q, eGFR, albumin (ALB), total cholesterol (TC), triglycerides (TG), serum low-density lipoprotein (LDL), serum high-density lipoprotein (HDL), fasting blood glucose (FBG), anti-phospholipase A2 receptor antibody (Anti-PLA2R), proteinuria, serum complement C3, serum complement C4, free light chain kappa (KAP), free light chain lambda (LAM), and the ratio of free light chain KAP to free light chain LAM. The Sysmex UF-5000 urinary sediment analyzer used the principles of flow cytometry and electrical impedance analysis to identify microscopic hematuria. We detected the anti-phospholipase A2 receptor antibody using flow fluorescence (Tesmi-F3999), and obtained all other data from the aforementioned experiments using the Beckman Coulter AU5800. CKD-EPI developed a formula to calculate the estimated glomerular filtration rate (eGFR) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003ePathological data\u003c/h3\u003e\n\u003cp\u003eWe examined the tissue samples from each kidney biopsy patient using immunofluorescence staining, electron microscopy, and standard light microscopy. Two experienced pathologists at our hospital evaluated the results. We used the Ehrenreich-Churg criteria to categorize the pathological staging into stages I, II, III, and IV. When we observe two periods, we select the longer one. We qualitatively assessed the levels of C3, IgA, IgM, IgG, and C1q deposition based on fluorescence intensity. Some important features of focal segmental glomerulosclerosis (FSGS) lesions are a small rise in thylakoid stroma and glomerular adhesion, along with a pattern of damage to the glomeruli that can be seen under a light microscope.\u003c/p\u003e\n\u003ch3\u003eTreatment and Outcome\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eTreatment and Outcome\u003c/div\u003e \u003cp\u003e As per the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines, patients get supportive and immunosuppressive therapy contingent upon the severity of proteinuria, renal function, and duration of monitoring. Supportive therapy includes medications such as diuretics, anticoagulants, renin-angiotensin system (RAS) blockers, and famotidine. Immunosuppressive therapy consists of corticosteroids, cyclophosphamide, tacrolimus, cyclosporine, and rituximab.\u003c/p\u003e \u003cp\u003eThis investigation aimed to assess the course of renal disease, defined by a decline in eGFR above 30% or the development of end stage renal disease (ESRD). The cohort without renal disease progression exhibited a reduction in glomerular filtration rate of \u0026le;\u0026thinsp;30% from baseline [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Patients in complete remission (CR) were required to exhibit minimal proteinuria (less than 0.3 g/d) and normal serum albumin and creatinine levels. partial remission (PR) was characterised by normal serum albumin (\u0026ge;\u0026thinsp;3.5 g/dL), stable renal function, and urine protein levels ranging from 0.3 to 3.5 g/24 hours, which were at least 50% reduced from baseline. Both complete and partial remission were classified as remission. No remission (NR) was identified by any of the following criteria: (1) proteinuria levels of 3.5 g/24 hours or higher, (2) proteinuria with less than a 50% reduction from baseline, or (3) Serum albumin levels\u0026thinsp;\u0026lt;\u0026thinsp;3.5 g/dL [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData analysis was performed utilizing SPSS version 26.0, a statistical software. Data for continuous variables exhibiting irregular distributions are presented using the median and interquartile range (IQR). The data are displayed as counts and proportions for categorical variables. Univariate comparisons were performed utilizing the Mann-Whitney test for non-normally distributed data and the χ\u0026sup2; test, commonly referred to as Fisher's exact test, for categorical variables. Correlations between baseline serum total cholesterol and clinical parameters were analyzed using Spearman correlation. To identify independent risk factors, we performed one-way logistic regression analyses and incorporated variables with a p-value below 0.1 into multifactorial logistic regression analysis. We evaluated the predictive significance of serum total cholesterol levels for the progression of IMN with nephrotic syndrome using receiver operating characteristic (ROC) curves. Statistical significance was defined as P-values below 0.05. The aim of this study was to assess the course of renal disease, defined by a decline in eGFR above 30% or the emergence of End Stage Renal Disease (ESRD). After excluding variables with more than 10% missing data, the proportion of missing data for the remaining variables was less than 10%. The median of the non-missing values was used to interpolate the missing values. Plots were all created with GraphPad Prism 9.0.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the cohort\u003c/h2\u003e \u003cp\u003eThe investigation included 373 participants following the application of exclusion criteria (Fig.\u0026nbsp;1). At the time of kidney biopsy, the average age of patients in this cohort was 49 years (inter-quartile range 39, 56), with a male to female ratio of 1.94:1. 316 (84.7%) patients received immunosuppressive medication at the start of the monitoring period, while 57 (15.3%) patients received supportive care. Following an average monitoring period of 15 (inter-quartile range 9, 24) months, 36 (9.65%) patients the designated objective for renal disease, whereas 337 (90.35%) patients did not.\u003c/p\u003e \u003cp\u003eAs compared to the starting point of the two groups, there were big differences (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in systolic blood pressure, serum total cholesterol, serum LDL cholesterol, serum complement C1q, free light chain KAP, free light chain LAM, and urine protein remission. Patients with progressive nephropathy exhibited a median systolic blood pressure of 142 (inter-quartile range 127.5, 153.5) mmHg, a median serum total cholesterol of 9.74 (inter-quartile range 8.52, 10.89) mmol/L, a median serum LDL of 6.17 (inter-quartile range 5.11, 7.51) mmol/L, a median serum complement C1q of 223.1 (inter-quartile range 192, 238.3) mg/L, a median free light chain KAP of 31.75 (inter-quartile range 26.93, 40.67) mg/L, a median free light chain LAM of 44.1 (inter-quartile range 32.3, 52.73) mg/L, and a urinary protein remission rate of 52.8% (19/36).The patients in the non-progressing nephropathy group, on the other hand, had a median systolic blood pressure of 135 (inter-quartile range 123, 148) mmHg, a median serum total cholesterol of 7.67 (inter-quartile range 6.40, 8.87) mmol/L, a median serum LDL of 4.92 (inter-quartile range 4.0, 6.02) mmol/L, a median serum complement C1q of 200 (inter-quartile range 173, 225.99) mg/L, a median free light chain KAP of 29.1 (inter-quartile range 23.15, 36.4) mg/L, a median free light chain LAM of 35.1 (inter-quartile range 28.6 44.1) mg/L, and a 67.9% remission rate for urinary protein (Table\u0026nbsp;1, attachment Table \u0026minus;\u0026thinsp;1).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eComparing the Pathological Features of the Two Patient Groups\u003c/h3\u003e\n\u003cp\u003eThe glomerulosclerosis rate was significantly higher in the nephropathy development cohort compared to the clinical symptoms observed in both categories (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, the binary logistic univariate analysis did not find the rate of sclerosis as a predictor for renal progression (OR: 17.647; 95% CI: 0.445-699.233; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.126). The pathomorphological staging of the two groups of patients with IMN did not differ significantly. Both groups had a lot of stages I and II lesions and were not as bad. This finding is consistent with a lower rate of nephropathy progression (9.65%) observed during follow-up. Different between the two subgroups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was also seen in the serum levels of component C1q, which were significantly higher in the group whose nephropathy was getting worse. However, the deposition of C1q in renal tissue showed no substantial distinction between the two groups (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Pathological Features Between the Two Patient Groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal cohort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKidney disease Progression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo kidney disease progression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGER n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.63%(0,6.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.6(0,7.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.44%(0,5.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.033\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC3 deposits n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e283(75.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27(75.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e256(76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIgG deposits n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e357(95.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34(94.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e323(95.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC1q deposits n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e106(28.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10(27.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96(28.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMorphological staging n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u0026thinsp;+\u0026thinsp;II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e369(98.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35(97.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e334(99.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u0026thinsp;+\u0026thinsp;IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4(1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1(2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLesions of FSGS n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56(14.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7(19.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48(14.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.407\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eGER: glomerulosclerosis rate; FSGS: focal segmental glomerulosclerosis ;Data represent the median and interquartile range, as well as counts and percentages.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation between baseline serum total cholesterol levels and clinical parameters\u003c/h2\u003e \u003cp\u003eSerum total cholesterol levels had a positive connection with serum urea nitrogen (r\u0026thinsp;=\u0026thinsp;0.18, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and serum complement C4 (r\u0026thinsp;=\u0026thinsp;0.17, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In contrast, serum total cholesterol levels had a negative correlation with serum albumin (r = -0.39, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), serum glomerular filtration rate (r = -0.12, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and serum IgG (r = -0.25, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Serum total cholesterol, 24-hour urine protein, systolic blood pressure, free light chain Kappa (KAP), free light chain Lambda (LAM), and serum complement C3 exhibited no significant correlation with each other (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eClinical risk factors for kidney disease progression\u003c/h2\u003e \u003cp\u003eTo further examine the association between baseline data at renal biopsy and the progression of renal disease, we included univariate factors with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1: systolic blood pressure, age, serum total cholesterol, antiphospholipase A2 receptor (PLA2R) antibody, serum complement C3, free light chain KAP, and free light chain LAM in the bivariate logistic multifactorial analysis. The findings demonstrated that serum total cholesterol was an important predictor for the decline of renal function (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, OR 1.554, 95% CI: 1.294\u0026ndash;1.866) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Because the two groups had different starting blood total cholesterol levels, we used the Receiver Operating Characteristic (ROC) curve to look at how serum total cholesterol could be used to predict how quickly nephropathy would get worse. To see how nephropathy would get worse, the area under the ROC curve for serum total cholesterol was 0.789 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 95% CI: 0.725\u0026ndash;0.852), which means it was 69.4% sensitive and 76.9% specific. We established the optimal cut-off value of baseline total blood cholesterol for distinguishing nephropathy development at 9.02 mmol/L through the calculation of the ideal Youden index (Fig.\u0026nbsp;3).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBinary Logistic Regression Analysis of Risk Factors for the Progression of Kidney Disease\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultifactorial\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.414\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFL-KAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFL-LAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-PLA2R\u003c/p\u003e \u003cp\u003eantibody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSBP: systolic blood pressure;TC: total cholesterol; C3: serum complement C3; FL-KAP: free light-chain KAP; FL-LAM: free light-chain LAM;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe looked back at 373 patients who had nephrotic proteinuria and idiopathic membranous nephropathy. We examined the link between serum total cholesterol levels measured during kidney biopsy and the progression of patients illness. Our research showed that high serum total cholesterol at the start of the disease is a separate risk factor for kidney damage getting worse in IMN. This was shown by multifactorial logistic analysis (OR 1.554, 95% CI: 1.294\u0026ndash;1.866, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eThe total serum cholesterol level is associated with the progression of nephropathy among individuals with idiopathic membranous nephropathy and nephrotic syndrome. The suggested mechanism involves too much cholesterol building up because of problems with reverse cholesterol transport. This hurts podocytes and renal tubular cells, leading to mitochondrial stress, inflammation, actin cytoskeletal remodeling, insulin resistance, endoplasmic reticulum stress, and finally, cell death [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Impaired podocytes exacerbate the glomerular inflammatory environment by expressing receptors for inflammatory cytokines and producing chemokines. By altering the distribution of cholesterol and the makeup of lipoproteins in plasma, tissues, and cellular organelles, inflammatory stress disturbs lipid homeostasis. This process speeds up the growth of glomerulosclerosis and renal fibrosis. It also damages podocytes more and makes them make more lipids [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study evaluated the baseline features at renal biopsy in patients with two distinct outcomes of membranous nephropathy. The two groups did not differ in the beginning 24-hour protein levels in the urine; however, the group that developed nephropathy at follow-up had a lower urinary protein remission rate, indicating a significant difference between the groups (p\u0026thinsp;=\u0026thinsp;0.034). Chronic proteinuria is believed to correlate with a heightened risk of renal insufficiency, consistent with prior perspectives [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The histologic staging of renal biopsies remains contentious in forecasting renal prognosis. In the middle of 1999, Marx et al. found that membranous renal stage III/IV had a higher risk ratio of 5.3 (p\u0026thinsp;=\u0026thinsp;0.002) than stage I/II end-stage renal disease (ESRD). Earlier and later studies have found that there isn't a statistically significant link between different stages of disease and the prognosis of the kidneys. This is probably because histologic severity includes more than just the condition of the glomerular basement membrane; it also includes segmental glomerular sclerosis and tubulointerstitial damage [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Other research, on the other hand, shows that FSGS lesions, excluding atypical lesions, can predict the progression of IMN to a 50% drop in the estimated rate of glomerular filtration or end-stage renal disease. This may be ascribed to the disturbance induced by unusual focal segmental lesions and the significant variation in the prevalence of FSGS lesions among patients with IMN in studies [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOne advantage of our study is that it is the first to examine the correlation between blood cholesterol levels and the progression of nephropathy in patients with IMN. The convenience of specimen collection, the simple testing procedure, and the quick reporting of results are some advantages of assessing total serum cholesterol. This study still has a number of shortcomings. First of all, it was a single-center retrospective study. Regional limitations resulted in a relatively homogeneous patient sample, creating selection bias and restricting the data's applicability to patients with membranous nephropathy in other areas. Furthermore, all of the patients came from a single Chinese healthcare facility. Therefore, to confirm our findings, a prospective, multicenter investigation with a larger sample size is required.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study indicates that baseline blood cholesterol is an independent risk factor for the advancement of nephropathy in patients with IMN. Consequently, blood total cholesterol levels in IMN patients warrant scrutiny.\u003c/p\u003e"},{"header":"Abbreviations","content":" \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eMN\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eMembrane nephropathy\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eIgG\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eImmunoglobulin G\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eIMN\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eIdiopathic membranous nephropathy\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eATP\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eAdenosine triphosphate\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eHDL-C\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eHigh-density lipoprotein cholesterol\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eCKD-EPI\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eChronic Kidney Disease Epidemiology Collaboration\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eeGFR\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eEstimated glomerular filtration rate\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eHBV\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eHepatitis B virus\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eBUN\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eBlood urea nitrogen\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eALB\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eAlbumin\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eTC\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eTotal cholesterol ,\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eTG\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eTriglycerides\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eLDL\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eLow-density lipoprotein\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eHDL\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eHigh-density lipoprotein ,\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eFBG\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eFasting blood glucose\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAnti-PLA2R\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eAnti-phospholipase A2 receptor antibody\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eKAP\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eFree light chain kappa\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eLAM\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eFree light chain lambda\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eFSGS\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eFocal segmental glomerulosclerosis\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eKDIGO\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eKidney\u0026nbsp;disease: improving global outcomes\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eRAS\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eRenin-angiotensin system\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eESRD\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eEnd Stage Renal Disease\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eCR\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eComplete remission\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePR\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ePartial remission\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eNR\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo remission\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eIQR\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eInterquartile range\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eROC\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eReceiver operating characteristic\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank all of the patients who took part in this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN.C, data curation, investigation, study design, writing original draft. Y.W, data curation, formal analysis, software. X.L.B, data analysis, andrevision of the manuscript. F.L.C, Y.Q.S and Y.J.S made significant contributions to the conception and design. All authors read and approved the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Shandong Province Natural Science Foundation (ZR2021MH295) and a cross-sectional thrombocytopenia project (1665387000050) provided funding for this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOn request, data will be made available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEvery method employed in this study conformed with the 1964 Declaration of Helsinki and its subsequent amendments, as well as the ethical criteria established by the School of Medicine Institutional Research Committee at the Provincial Hospital (SZRJJ: NO. 2021-017). Each patient provided their informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRonco P, et al. Membranous nephropathy. Nat Reviews Disease Primers. 2021;7(1):69.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoxha E, Reinhard L, Stahl RAK. Membranous nephropathy: new pathogenic mechanisms and their clinical implications. Nat Rev Nephrol. 2022;18(7):466\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu R, et al. Spectrum of biopsy proven renal diseases in Central China: a 10-year retrospective study based on 34,630 cases. Sci Rep. 2020;10(1):10994.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeyer-Schwesinger C. The ins-and-outs of podocyte lipid metabolism. Kidney Int. 2020;98(5):1087\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRen L, et al. The role of lipotoxicity in kidney disease: From molecular mechanisms to therapeutic prospects. Volume 161. Biomedicine \u0026amp; Pharmacotherapy\u0026thinsp;=\u0026thinsp;Biomedecine \u0026amp; Pharmacotherapie; 2023. p. 114465.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuh SH, Kim SW. Dyslipidemia in Patients with Chronic Kidney Disease: An Updated Overview. Diabetes Metabolism J. 2023;47(5):612\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoi WJ et al. Hypertriglyceridemia Is Associated with More Severe Histological Glomerulosclerosis in IgA Nephropathy. J Clin Med, 2021. 10(18).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong L, et al. Utility of non-HDL-C in predicting proteinuria remission of idiopathic membranous nephropathy: a retrospective cohort study. Lipids Health Dis. 2021;20(1):122.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo J, et al. Clinicopathological Characteristics and Outcomes of PLA2R-Associated Membranous Nephropathy in Seropositive Patients Without PLA2R Staining on Kidney Biopsy. Am J Kidney Diseases: Official J Natl Kidney Foundation. 2022;80(3):364\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKDIGO 2021 Clinical Practice Guideline for the Management of Glomerular Diseases. Kidney Int, 2021. 100(4S).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevey AS, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevey AS et al. Change in Albuminuria and GFR as End Points for Clinical Trials in Early Stages of CKD: A Scientific Workshop Sponsored by the National Kidney Foundation in Collaboration With the US Food and Drug Administration and European Medicines Agency. Am J Kidney Diseases: Official J Natl Kidney Foundation, 2020. 75(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChapter 7. : Idiopathic membranous nephropathy. Kidney Int Supplements. 2012;2(2):186\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuan XZ, Varghese Z, Moorhead JF. An update on the lipid nephrotoxicity hypothesis. Nat Rev Nephrol. 2009;5(12):713\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMitrofanova A, Merscher S, Fornoni A. Kidney lipid dysmetabolism and lipid droplet accumulation in chronic kidney disease. Nat Rev Nephrol. 2023;19(10):629\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo Z, et al. Interplay of lipid metabolism and inflammation in podocyte injury. Metab Clin Exp. 2024;150:155718.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePalmer BF. Change in albuminuria as a surrogate endpoint for cardiovascular and renal outcomes in patients with diabetes. Volume 25. Diabetes, Obesity \u0026amp; Metabolism,; 2023. pp. 1434\u0026ndash;43. 6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamaguchi M, et al. Urinary protein and renal prognosis in idiopathic membranous nephropathy: a multicenter retrospective cohort study in Japan. Ren Fail. 2018;40(1):435\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarx BE, Marx M. Prediction in idiopathic membranous nephropathy. Kidney Int. 1999;56(2):666\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsai S-F, Wu M-J, Chen C-H. Low serum C3 level, high neutrophil-lymphocyte-ratio, and high platelet-lymphocyte-ratio all predicted poor long-term renal survivals in biopsy-confirmed idiopathic membranous nephropathy. Sci Rep. 2019;9(1):6209.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe H-G, 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. BMC Nephrol. 2019;20(1):328.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeeringa SF, et al. \u003cem\u003eFocal segmental glomerulosclerosis is not a sufficient predictor of renal outcome in patients with membranous nephropathy.\u003c/em\u003e Nephrology. Volume 22. Dialysis, Transplantation: Official Publication of the European Dialysis and Transplant Association - European Renal Association; 2007. pp. 2201\u0026ndash;7. 8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun M, et al. Clinical characteristics and prognosis of patients with idiopathic membranous nephropathy with kidney tubulointerstitial damage. Ren Fail. 2023;45(1):2205951.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\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":"Membranous nephropathy, serum total cholesterol, cohort study, risk factor","lastPublishedDoi":"10.21203/rs.3.rs-5341556/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5341556/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis study sought to uncover potential risk factors linked to disease development by analyzing the medical and renal histology features of individuals with idiopathic membranous nephropathy associated with nephrotic syndrome.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e Our retrospective research involved 373 patients who met the specified inclusion criteria and had a kidney biopsy diagnosis between January 2016 and August 2023. The crowds recorded the clinical and pathological characteristics at baseline and assessed the outcomes during the follow-up period. Researchers used a binary logistic regression analysis to identify the risk factors associated with disease progression in individuals with membranous nephropathy. We categorized the patients into two distinct groups: those with progressing renal disease and those without.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThirty-six (9.65%) people experienced nephropathy progression following an average follow-up period of 15 (inter-quartile range 9,24) months. Serum total cholesterol levels had a substantial negative connection with albumin, as evidenced by Spearman's rho = -0.39 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The ROC curve for serum total cholesterol indicated a sensitivity of 69.4% and a specificity of 76.9% in predicting nephropathy development. The area beneath the curve was 0.789 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 95% CI: 0.725\u0026ndash;0.852). Logistic multivariate analysis revealed that total cholesterol levels in the blood (OR\u0026thinsp;=\u0026thinsp;1.554, 95% CI: 1.294\u0026ndash;1.861, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) constitute an independent risk factor for nephropathy development.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn patients with membranous nephropathy and nephrotic syndrome, serum total cholesterol levels act as a separate danger indicator for disease advancement.\u003c/p\u003e","manuscriptTitle":"Serum total cholesterol serves as an independent risk factor for the progression of disease in idiopathic membranous nephropathy.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-02 15:57:57","doi":"10.21203/rs.3.rs-5341556/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":"1d3683ff-9d63-4399-92aa-6cc23b44da5a","owner":[],"postedDate":"December 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-02T06:24:00+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-02 15:57:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5341556","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5341556","identity":"rs-5341556","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}