Clinicopathological Characteristics and Prognostic Value of Dyslipidemia in IgA Nephropathy: A Retrospective Cohort Study

Clinicopathological Characteristics and Prognostic Value of Dyslipidemia in IgA Nephropathy: A Retrospective Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Clinicopathological Characteristics and Prognostic Value of Dyslipidemia in IgA Nephropathy: A Retrospective Cohort Study Yiping Ruan, Qiaoyun Huang, Fuyuan Hong, Miao Lin, Chen Wang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6539765/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 Dyslipidemia is common in chronic kidney disease (CKD) including IgA nephropathy (IgAN) and possibly associated with renal prognosis, but the value of dyslipidemia in IgAN remains insufficiently assessed. The objective of our study was to investigate clinicopathological characteristics and renal outcome in IgAN patients with dyslipidemia, and evaluate prognostic value of lipid abnormality. Methods This cohort study included 458 primary IgAN patients for a retrospective analysis. The clinicopathological features and renal outcome were recorded. In univariate and multivariate models, association between dyslipidemia and renal outcome, and dyslipidemia-associated pathological features were analyzed. Results Patients with dyslipidemia (defined as total cholesterol ≥ 5.2mmol/L, triglycerides ≥ 1.7mmol/L, or LDL-C ≥ 3.4mmol/L) presented elevated complement, and worse clinical characteristics with regard to blood pressure, proteinuria and kidney function, and glomerulosclerosis, tubular atrophy/interstitial fibrosis (T1-2), crescents, and vascular lesions were more common. By multivariate logistic regression, T1-2 and arterial intimal fibrosis were significantly associated with dyslipidemia. After a mean follow-up of 54.7 months, dyslipidemia (P = 0.001), especially abnormalities in total cholesterol (P = 0.016) and triglycerides (P = 0.001), was significantly associated with poorer renal survival, and renal survival was worse after lipid-lowering therapies. In addition to eGFR and arterial intimal fibrosis, dyslipidemia was an independent predictor for renal survival in multivariate Cox analyses (model 1: HR = 2.229, 95% CI = 1.146–4.336, P = 0.018; model 2: HR = 2.117, 95% CI = 1.082–4.145, P = 0.029). Conclusions IgAN patients with dyslipidemia presented more severe clinicopathological features. Tubular atrophy/interstitial fibrosis and arterio-/arteriolosclerosis were closely associated with dyslipidemia. Dyslipidemia not only indicated adverse renal outcomes, but also was an independent prognostic predictor. IgA nephropathy dyslipidemia characteristics outcome Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Immunoglobulin A nephropathy (IgAN), recognized as the leading cause of primary glomerular disease globally, manifests pathologically dominant IgA deposition in the glomerular mesangium. While regional disparities epidemiologically exist, IgAN accounts for nearly 50% of primary glomerular diseases in China and significantly elevates the risk of end stage renal disease (ESRD)[ 1 ]. Remarkably, ERSD occurs in approximately 40% of patients about 20 years after diagnosis[ 2 ]. Generally, levels of renal function and proteinuria in patients with chronic kidney disease (CKD) including IgAN can affect the levels and properties of circulating lipids including triglycerides, low-density lipoprotein cholesterol, and others [ 3 ]. Dyslipidemia is also a potentially modifiable cardiovascular risk factor for CKD. Accordingly, Kidney Disease: Improving Global Outcomes (KDIGO) released a comprehensive clinical practice guideline for lipid management in CKD. However, the association between dyslipidemia and renal outcomes in CKD, especially in IgAN, although previously suggested, was not well-established, and clinical studies failed to demonstrate significant protective benefits for renal prognosis from lipid-lowering therapies [ 4 – 6 ]. Moreover, the predictive value of dyslipidemia for renal outcomes in CKD remained unknown. Until now the role of dyslipidemia was not adequately evaluated in follow-up trials in IgAN. In this single-center, retrospective study, we utilized our cohort to investigate clinicopathological characteristics and renal outcomes in IgAN patients with dyslipidemia, as well as prognostic value of dyslipidemia, from Eastern China. Methods Patients A total of 458 patients (age > 14 years) with biopsy-confirmed primary IgAN from January 2010 to October 2021 were enrolled. Patients with fewer than eight glomeruli on the biopsy or with secondary causes of mesangial IgA deposits, such as Henoch-Schonlein purpura, systemic lupus erythematosus and liver disease, were excluded. Measurements Baseline demographic and clinicopathologic data collected at biopsy included as follow: age, gender, medical history, systolic and diastolic blood pressure, serum creatinine, blood urea nitrogen (BUN), serum albumin, uric acid, serum cholesterol, triglycerides, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), amount of red blood cell (RBC) in urine and proteinuria, serum IgA, C3, C4 and treatment modalities. Dyslipidemia was defined as total cholesterol ≥ 5.2mmol/L, serum triglycerides ≥ 1.7mmol/L, or LDL-C ≥ 3.4mmol/L. Hypertension was defined as blood pressure ≥ 140/90 mmHg; blood pressure measurements were repeated twice in a patient who was relaxed and sitting in a chair for > 5 min and in the patient’s right arm. Mean arterial pressure (MAP) was defined as a diastolic pressure plus one-third of the pulse pressure. Proteinuria was measured by a 24-h urine protein collection. Estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. Lipid-lowering therapy primarily involved statins, with fibrates reserved for hypertriglyceridemia. Treatment modalities of renin-angiotensin system inhibitors (RASi) included angiotensin-converting enzyme inhibitor (ACEI), angiotensin receptor blocker (ARB), or both. Patients received optimized supportive therapy with RAS blockade to achieve target blood pressure (urine albumin excretion < 30 mg/24 hours, BP ≤ 140/90mmHg; urine albumin excretion ≥ 30 mg/24 hours, BP ≤ 130/80mmHg). Immunosuppressive therapy was defined as receiving corticosteroids and/or immunosuppressants after biopsy. Corticosteroid therapy referred to oral prednisone (started at 0.5-1 mg/kg daily, tapering down within 6–8 months). Immunosuppressants indicated cyclophosphamide (used at a total dosage of 6 to 8 g within 6–8 months). Renal biopsy specimens from all patients were divided routinely for immunofluorescence microscopy, light microscopy and electron microscopy. The paraffin-embedded sections were stained with hematoxylin and eosin, periodic acid-Schiff, silver methenamine, and Masson’s trichrome. All renal biopsy results were reviewed independently by two renal pathologists according to the updated Oxford classification. Pathologic features of the updated Oxford classification were defined as follows: mesangial score of ≤ 0.5 (M0) or > 0.5 (M1); endocapillary hypercellularity absent (E0) or present (E1); segmental glomerulosclerosis absent (S0) or present (S1); tubular atrophy atrophy/interstitial fibrosis ≤ 25% (T0), 26–50% (T1) or > 50% (T2); and cellular/fibrocellular crescents absent (C0), present in at least 1 glomerulus (C1), in > 25% of glomeruli (C2). Pathological features also included global glomerulosclerosis, tuft necrosis, interstitial inflammation, arterial intimal fibrosis, arteriolar hyalinosis and IgA glomerulus immunofluorescence. Outcome The time of renal biopsy was defined as the starting point. The primary outcome of interest was a composite kidney outcome defined as: 1) End-stage renal disease (ESRD); or 2) > 50% decline in eGFR. ESRD was defined as eGFR < 15 ml/min per 1.73 m 2 or initiation of dialysis or transplantation. Statistical analysis Continuous variables were expressed as means ± standard deviation and analyzed with t-test or Mann-Whitney U-test. Categorical variables were described as frequency with percentages and analyzed using χ 2 test. Renal survival was analyzed with the Kaplan-Meier method compared by Log rank test. Associations between pathological features and dyslipidemia were also analyzed using univariate followed by multivariate logistic regression. The results were expressed as odds ratio (OR) with 95% confidence intervals (CIs). Univariate followed by multivariate Cox regression were used to determine the independent predictive value of dyslipidemia for renal survival. The clinicopathologic features were included in univariate Cox regression and then only the features significantly associated with renal survival were considered in multivariate regression. The results were expressed as hazard ratio (HR) with 95% confidence intervals (CIs). P < 0.05 was considered statistically significant. All statistical analysis was performed with SPSS 22.0 (SPSS Inc., Chicago, IL, USA). Results Clinical characteristics At the time of biopsy, the average age of 458 patients recruited in our study was 33.2 ± 10.0 years. Mean eGFR was 87.7 ± 33.7 ml/min per 1.73 m 2 . According to the Kidney Disease Outcomes Quality Initiative classification, 352(76.9%) patients had stage 1 or 2 chronic kidney disease and 79(17.2%) and 27(5.9%) patients had stages 3 and 4 chronic kidney disease, respectively. The mean follow-up time was 54.7 ± 20.4 months. During follow-up, the majority of patients received RASi. 137 (29.9%) patients received corticosteroids and 12 patients (2.6%) received immunosuppressants. Overall, 256 of 458 (55.9%) patients presented with dyslipidemia including 181 (39.5%) patients with total cholesterol ≥ 5.2mmol/L, 148 (32.3%) with triglycerides ≥ 1.7mmol/L, and 159 (34.7%) with LDL-C ≥ 3.4mmol/L. At the time of renal biopsy, except for HDL-C, the levels of lipids were higher in patients with dyslipidemia, including cholesterol (4.21 ± 0.55 vs 5.94 ± 1.58mmol/L, P < 0.001), triglycerides (0.95 ± 0.33 vs 2.28 ± 2.12mmol/L, P < 0.001) and LDL-C (2.60 ± 0.47 vs 3.68 ± 1.29mmol/L, P < 0.001). Patients with dyslipidemia presented with older age (30.2 ± 8.3 vs 35.5 ± 10.7years, P < 0.001), higher levels of blood pressure including systolic blood pressure (121.0 ± 16.3 vs 130.0 ± 19.0mmHg, P < 0.001), diastolic blood pressure (76.6 ± 11.8 vs 82.2 ± 13.4mmHg, P < 0.001) and MAP (91.4 ± 12.4 vs 98.1 ± 14.2mmHg, P < 0.001), serum creatinine (93.1 ± 58.6 vs 110.0 ± 60.5µmol/L, P < 0.001), BUN (5.91 ± 4.28 vs 6.92 ± 3.37mmol/L, P < 0.001), uric acid (337.1 ± 117.7 vs 394.4 ± 125.3mmol/L, P < 0.001), proteinuria (0.67 ± 0.89 vs 1.20 ± 1.21g/day, P < 0.001), C3 (0.96 ± 0.18 vs 1.08 ± 0.19g/L, P < 0.001), and C4 (0.22 ± 0.07 vs 0.25 ± 0.08g/L, P < 0.001) as well as lower levels of eGFR (96.5 ± 31.8 vs 80.8 ± 33.7 ml/min/1.73m 2 , P < 0.001) and serum albumin (40.4 ± 4.1 vs 38.1 ± 6.5g/L, P = 0.001). Proportion of patients with hypertension (P < 0.001) was higher in the group with dyslipidemia. There were no significant differences in gender, tonsillitis, hematuria, and serum IgA between two groups. Length of follow-up was similar, but compared with patients without dyslipidemia, patients with dyslipidemia received more corticosteroids (P < 0.001) and lipid-lowering therapies (P < 0.001), and similar immunosuppressants. The rate of renal end point was higher in the group with dyslipidemia (6.4% vs 16.0%, P = 0.002) (Table 1 ). Table 1 Clinical and pathological characteristics of IgA nephropathy patients with dyslipidemia No dyslipidemia (n = 202) Dyslipidemia (n = 256) p value a Age (years) 30.2 ± 8.3 35.5 ± 10.7 < 0.001 Male (n, %) 88, 43.6% 123, 48.0% 0.339 Tonsillitis (n, %) 44, 21.8% 38, 14.8% 0.054 Hypertension (n, %) 50, 24.8% 113, 44.1% < 0.001 Systolic BP (mmHg) 121.0 ± 16.3 130.0 ± 19.0 < 0.001 Diastolic BP (mmHg) 76.6 ± 11.8 82.2 ± 13.4 < 0.001 MAP (mmHg) 91.4 ± 12.4 98.1 ± 14.2 < 0.001 Uric acid (µmol/L) 337.1 ± 117.7 394.4 ± 125.3 < 0.001 Serum creatinine (µmol/L) 93.1 ± 58.6 110.0 ± 60.5 133µmol/L (n, %) 25, 12.4% 63, 24.6% 0.001 eGFR (ml/min/1.73 m 2 ) 96.5 ± 31.8 80.8 ± 33.7 < 0.001 BUN (mmol/L) 5.91 ± 4.28 6.92 ± 3.37 < 0.001 Proteinuria (g/day) 0.67 ± 0.89 1.20 ± 1.21 < 0.001 Serum albumin (g/L) 40.4 ± 4.1 38.1 ± 6.5 0.001 Cholesterol (mmol/L) 4.21 ± 0.55 5.94 ± 1.58 < 0.001 Triglycerides (mmol/L) 0.95 ± 0.33 2.28 ± 2.12 < 0.001 LDL-C (mmol/L) 2.60 ± 0.47 3.68 ± 1.29 < 0.001 HDL-C (mmol/L) 1.27 ± 0.31 1.27 ± 0.61 0.097 RBC in urine ≥ 2+ (n, %) 85, 42.1% 96, 37.5% 0.320 Serum IgA (g/L) 3.02 ± 0.91 2.94 ± 0.93 0.467 Serum C3 (g/L) 0.96 ± 0.18 1.08 ± 0.19 < 0.001 Serum C4 (g/L) 0.22 ± 0.07 0.25 ± 0.08 < 0.001 Length of follow-up (months) 56.4 ± 21.9 53.3 ± 19.2 0.087 Treatment Lipid-lowering therapy 0, 0% 67, 26.2% < 0.001 Steroids 37, 18.3% 100, 39.1% < 0.001 Immunosuppressant 5, 2.5% 7, 2.7% 0.863 ESRD or doubling serum creatinine (n, %) 13, 6.4% 41, 16.0% 0.002 Global glomerulosclerosis (n, %) 135, 66.8% 214, 83.6% < 0.001 Tuft necrosis (n, %) 18, 8.9% 27, 10.5% 0.559 Mesangial hypercellularity, M1 (n, %) 186, 92.1% 238, 93.0% 0.718 Endocapillary hypercellularity, E1 (n, %) 34, 16.8% 49, 19.1% 0.524 Segmental glomerulosclerosis, S1 (n, %) 81, 40.1% 136, 53.1% 0.006 Tubular atrophy/interstitial fibrosis, T (n, %) T0 163, 80.7% 161, 62.9% T1 37, 18.3% 87, 34.0% 25% 31, 15.3% 58, 22.7% Arterial intimal fibrosis (n, %) 52, 25.7% 111, 43.4% < 0.001 Arteriolar hyalinosis (n, %) 30, 14.9% 63, 24.6% 0.010 IgA glomerulus immunofluorescence (n, %) +/++ 111, 55.0% 141, 55.1% 0.978 +++/++++ 91, 45.0% 115, 44.9% BP, blood pressure; MAP, mean arterial pressure; BMI, body mass index; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; eGFR, estimated glomerular filtration rate; BUN, blood urea nitrogen; RBC, red blood cell; RAS, renin-angiotensin system; ESRD, end-stage renal disease. a p value: comparison between two groups. p < 0.05 was considered significant. Table 2. Associations between pathological features and dyslipidemia analyzed by logistic regression CI, confidence interval. p<0.05 was considered significant. a Multivariate model: All pathological parameters significantly associated with dyslipidemia were included. Pathological characteristics In general, pathological features, including global glomerulosclerosis (135, 66.8% vs 214, 83.6%, P < 0.001), segmental glomerulosclerosis (S1) (81, 40.1% vs 136, 53.1%, P = 0.006), tubular atrophy/interstitial fibrosis (T1/2) (T1: 37, 18.3% vs 87, 34.0%; T2: 2, 1.0% vs 8, 3.1%; P < 0.001), crescents (C1/2) (C1: 72, 35.6% vs 115, 44.9%; C2: 4, 2.0% vs 12, 4.7%; P = 0.022), arterial intimal fibrosis (52, 25.7% vs 111, 43.4%, P < 0.001), and arteriolar hyalinosis (30, 14.9% vs 63, 24.6%, P = 0.010) were more severe in patients with dyslipidemia. There was no difference in tuft necrosis, mesangial hypercellularity (M1), Endocapillary hypercellularity (E1), lymphocytes and monocytes infiltration, and IgA glomerulus immunofluorescence between two groups (Table 1 ). Dyslipidemia-associated pathological factors When analyzing associations between dyslipidemia and various pathological parameters, univariate logistic regression analysis showed that global glomerulosclerosis, S1, T1-2, C1-2, arterial intimal fibrosis and arteriolar hyalinosis were positively correlated with dyslipidemia. Then by multivariate logistic regression analysis, T1-2 (OR = 1.634, 95% CI = 1.015–2.632, P = 0.043), and arterial intimal fibrosis (OR = 1.609, 95% CI = 1.037–2.497, P = 0.034) remained to be significantly associated with dyslipidemia (Table 2 ). Renal outcome After a mean follow-up of 54.7 months, ESRD or > 50% decline in eGFR occurred in 54 patients (11.8% of all patients). No death was reported in all patients. Compared with the patients without dyslipidemia, the 5-year renal survival rate was significantly lower in these with dyslipidemia (93.3% vs 84.0%, log-rank P = 0.001) (Fig. 1). Furthermore, we found that the 5-year renal survival rate was significantly lower in patients with hypercholesterolemia (total cholesterol ≥ 5.2mmol/L) (90.6% vs 84.2%, log-rank test, P = 0.016) (Fig. 2) and hypertriglyceridemia (serum triglycerides ≥ 1.7mmol/L) (91.4% vs 81.4%, log-rank test, P = 0.001) (Fig. 3). However, there was no significant difference in renal survival between patients with and without elevated LDL-C level (LDL-C ≥ 3.4mmol/L) in the follow-up (log-rank P = 0.204) (Fig. 4). In addition, the renal survival was similar whether decreased HDL-C level (HDL-C < 1.0mmol/L) was present or not (log-rank P = 0.138). For patients with dyslipidemia, after lipid-lowering therapies the 5-year renal survival rate was significantly lower (86.4% vs 77.2%, log-rank test, P = 0.029) (Fig. 5). Adjusted predictive value of dyslipidemia Univariate analyses followed by multivariate analyses were performed to examine independently predictive value of dyslipidemia for renal outcome. Clinicopathological parameters were used in the univariate analyses: age, gender, MAP, initial eGFR, proteinuria, treatment modalities, MEST-C scores based on the updated Oxford classification, vascular lesions. In univariate Cox analyses, dyslipidemia (HR = 2.871, 95% CI = 1.533–5.376, P = 0.001), as well as age, MAP, eGFR, endocapillary hypercellularity (E1), tubular atrophy/interstitial fibrosis (T1-2), arterial intimal fibrosis and arteriolar hyalinosis were strongly associated with renal survival. Nevertheless, after adjusting for clinical parameters in multivariable model 1 and for clinicopathological parameters in multivariable model 2, dyslipidemia (model 1: HR = 2.229, 95% CI = 1.146–4.336, P = 0.018; model 2: HR = 2.117, 95% CI = 1.082–4.145, P = 0.029), as well as eGFR at baseline (model 1: HR = 0.980, 95% CI = 0.970–0.989, P < 0.001; model 2: HR = 0.983, 95% CI = 0.972–0.994, P = 0.004) and arterial intimal fibrosis (model 2: HR = 2.107, 95% CI = 1.111–3.994, P = 0.022), were still independent predictors for renal survival (Table 3). Table 3 Predictive value of dyslipidemia for renal survival by univariate and multivariate Cox regression Univariable model Multivariable model 1 a Multivariable model 2 b HR 95% CI p value HR 95% CI p value HR 95% CI p value Dyslipidemia 2.871 1.533–5.376 0.001 2.229 1.146–4.336 0.018 2.117 1.082–4.145 0.029 Age (years) 1.027 1.001–1.054 0.039 0.997 0.970–1.025 0.843 0.990 0.962–1.019 0.495 Male 1.597 0.933–2.733 0.088 MAP 1.021 1.004–1.039 0.016 0.996 0.976–1.016 0.684 0.994 0.974–1.015 0.577 eGFR (mL/min/1.73 m 2 ) 0.978 0.970–0.987 < 0.001 0.980 0.970–0.989 < 0.001 0.983 0.972–0.994 0.004 Proteinuria (g/day) 1.120 0.893–1.405 0.326 Steroids therapy 1.352 0.772–2.366 0.291 Immunosuppressant therapy c 0.897 0.124-6.500 0.914 M1 0.569 0.243–1.334 0.195 E1 2.119 1.111–4.044 0.023 1.555 0.799–3.024 0.193 S1 0.750 0.429–1.310 0.312 T1-2 2.502 1.459–4.291 < 0.001 0.844 0.414–1.721 0.641 C1-2 1.336 0.780–2.288 0.292 Arterial intimal fibrosis 3.718 2.139–6.461 < 0.001 2.107 1.111–3.994 0.022 Arteriolar hyalinosis 2.113 1.108–4.032 0.023 1.281 0.652–2.518 0.473 MAP, mean arterial pressure; eGFR, estimated glomerular filtration rate; M, mesangial hypercellularity; E, endocapillary hypercellularity; S, segmental glomerulosclerosis; T, tubular atrophy/interstitial fibrosis; C, crescents; CI, confidence interval. p < 0.05 was considered significant. a Multivariable model 1: All clinical parameters significantly associated with renal outcome were included. b Multivariable model 2: All clinical and pathologic parameters significantly associated with renal outcome were included. c Immunosuppressant referred to cyclophosphamide. In addition, the prognostic performance of various lipid profiles combinations was assessed in the univariable model and the same multivariable models for adjustment including: 1) abnormal levels of total cholesterol (≥ 5.2mmol/L) or triglycerides (≥ 1.7mmol/L) (univariable model: HR = 2.472, 95% CI = 1.375–4.444, P = 0.002; multivariable model 1: HR = 1.895, 95% CI = 1.013–3.545, P = 0.046; multivariable model 2: HR = 1.791, 95% CI = 0.949–3.378, P = 0.072); and 2) abnormal levels of total cholesterol (≥ 5.2mmol/L), triglycerides (≥ 1.7mmol/L), LDL-C (≥ 3.4mmol/L) or HDL-C (< 1.0mmol/L) (univariable model: HR = 2.708, 95% CI = 1.362–5.383, P = 0.004; multivariable model 1: HR = 1.942, 95% CI = 0.950–3.972, P = 0.069; multivariable model 2: HR = 2.001, 95% CI = 0.974–4.112, P = 0.059). Discussion Until now, only few studies had comprehensively investigated detailed clinicopathological characteristics and renal outcome in IgA nephropathy (IgAN) with dyslipidemia, and evaluate prognostic value of dyslipidemia. In this IgAN cohort, we found that at least 55.9% of patients had different types of dyslipidemia. If measured by identical diagnostic criteria about abnormal lipid profiles, the prevalence of dyslipidemia in our study was comparable to that in recent studies [ 7 , 8 ]. Older age, decreased eGFR and serum albumin, and high levels of blood pressure, proteinuria, uric acid and serum complements including C3 and C4, were more severe in patients with dyslipidemia. Dyslipidemia was associated with several chronic pathological lesions including global and segmental glomerulosclerosis, tubular atrophy/interstitial fibrosis, and arterio-/arteriolosclerosis (refer to arterial intimal fibrosis and arteriolar hyalinosis). Besides, crescents that had been observed to be partially irreversible in several studies were also associated with dyslipidemia. Multivariate logistic regression analysis revealed that vascular and tubulointerstitial lesions were pathological features independently associated with dyslipidemia. IgAN patients with dyslipidemia presented with more severe clinicopathological manifestations, most of which were generally consistent with previous reports despite different concerns in lipid profiles across these researches including ours [ 7 – 9 ]. Notably, elevated complement levels had been observed in IgAN patients with dyslipidemia [ 8 , 10 ]. For instance, serum complement C3, which were produced primarily by liver, and also in adipocytes, endothelial cells and activated macrophages, had been considered to be associated with metabolic disorders like dyslipidemia possibly because of overproduction and dysregulation of complement in adipose tissues [ 10 ]. On the other side, complement components, like C3 and C3a-desArg, were involved in lipid storage and energy homeostasis [ 11 ]. Given the renal pathogenicity of complement cascade activation and the involvement of alternative and mannose-binding lectin pathway in IgAN [ 12 ], dyslipidemia likely reflected complement activation. Furthermore, intrarenal vascular lesions and tubulointerstitial injury independently associated with dyslipidemia identified in this study might be relevant to complement activation [ 13 , 14 ]. However, although dyslipidemia was recognized to be involved in direct nephrotoxicity, inflammatory response and complement activation [ 15 , 16 ], taking into account the lack of improvement in renal outcomes with statin-based lipid-lowering therapies in prior studies [ 4 , 17 ], the specific mechanisms by which dyslipidemia aggravated the renal pathological alterations in IgAN remained incompletely understood. Despite heterogeneity in classification criteria for dyslipidemia across studies, accumulating evidence had suggested the association between diverse patterns of lipid abnormalities and renal outcomes in CKD, whereas large-scale studies in IgAN remained absent. The relevance of triglyceride and cholesterol abnormalities to renal outcomes was indicated in CKD [ 5 , 18 ] and IgAN [ 7 , 9 , 19 ] in previous and our studies. The value of LDL-C for renal prognosis was supported in CKD cohort [ 20 ] but controversial in IgAN. A study from China showed that elevated LDL-C level was a predictive factor for the prognosis of IgAN [ 21 ], which was contradictory according to our study. A U-shaped association was observed between serum HDL-C levels and adverse renal outcomes in large cohort of CKD from Korea [ 22 ]. However, to date the prognostic value of HDL-C in IgAN, which was not confirmed in our cohort, were unreported by analogous investigations. For children with IgAN, dyslipidemia was also considered to be a risk factor for progression in a retrospective cohort study [ 23 ]. Moreover, it should be noted that our study revealed differential independent predictive values of lipid profile combinations for renal prognosis. In our IgAN cohort, multivariable-adjusted models demonstrated that the combination of elevated triglycerides, cholesterol, and LDL-C could show the better independent predictive capacity for adverse renal outcomes. The differences in the prognostic predictive capabilities among distinct lipid profiles suggested their involvement of diverse mechanisms in complement activation. Unfortunately, our study found that lipid-lowering therapies for patients with dyslipidemia demonstrated no improvement in renal prognosis, which supported findings from prior researches [ 4 ]. These findings not only facilitated the application of dyslipidemia for prediction of renal prognosis, but also implied that dyslipidemia possibly was involved in complex immune-related mechanisms that were not yet fully elucidated in IgAN. Several limitations of this study should be recognized. First, given that IgAN was a long-term chronic renal disease, limited sample size might lead to some bias in our single-center retrospective cohort study. For instance, the association between lipid-lowering therapies and renal prognosis might be influenced. Second, as regard the mild clinicopathological features at onset and the influence of treatment on renal prognosis in patients with dyslipidemia, the follow-up time might be insufficient, so a significant difference was possibly missed in the renal survival, such as a possible difference between patients with and without elevated LDL-C. Thirdly, a limited number of patients received lipid-lowering therapies and immunosuppressants so it was difficult to explore their value in the limited analysis. Further well-designed multicenter cohort studies with longer regular follow-up and larger sample sizes, taking into account more clinicopathological parameters, were still necessary to confirm these disputed results. Conclusions IgAN patients with dyslipidemia presented more severe clinicopathological features. Tubular atrophy/interstitial fibrosis and arterio-/arteriolosclerosis were closely associated with dyslipidemia. Dyslipidemia not only indicated adverse renal outcomes, but also was an independent prognostic predictor. Abbreviations CKD Chronic kidney disease IgAN Immunoglobulin A nephropathy ESRD End stage renal disease KDIGO Kidney Disease: Improving Global Outcomes BUN Blood urea nitrogen HDL-C High-density lipoprotein cholesterol LDL-C Low-density lipoprotein cholesterol RBC Red blood cell MAP Mean arterial pressure eGFR Estimated glomerular filtration rate CKD-EPI Chronic Kidney Disease Epidemiology Collaboration RAS Renin-angiotensin system ACEI Angiotensin-converting enzyme inhibitor ARB Angiotensin receptor blocker OR Odds ratio CI Confidence interval HR Hazard ratio M Mesangial hypercellularity E Endocapillary hypercellularity S Segmental glomerulosclerosis T Tubular atrophy/interstitial fibrosis C Crescents Declarations Acknowledgments We gratefully acknowledge all the clinicians, pathologists, statisticians, and laboratory technicians who contributed to this study. Author contributions Conceptualization: Yiping Ruan, Xuejing Chen; Methodology: Yiping Ruan, Xuejing Chen, Qiaoyun Huang; Formal analysis: Fayang Lian, Qiaoyun Huang; Resources: Fuyuan Hong, Yiping Ruan, Xuejing Chen, Qiaoyun Huang; Data Curation, Qiaoyun Huang; Investigation: Miao Lin, Chen Wang, Fang Cao, Guokai Yang, Lanting Huang; Writing - original draft preparation: Yiping Ruan, Xuejing Chen; Writing - review and editing: Yiping Ruan, Xuejing Chen, Qiaoyun Huang; Supervision, Fuyuan Hong and Miao Lin; Funding acquisition, Xuejing Chen. All authors have read and agreed to the published version of the manuscript. Funding This work was sponsored by Natural Science Foundation of Fujian, China (Grant No. 2024J08255). Data availability The data that support the findings of this study are available from the corresponding author upon reasonable request. Ethics approval and consent to participate Ethical approval for this research in accordance with the Declaration of Helsinki was acquired from Fujian Provincial Hospital Medical Ethics Committee (Ethics number: 26-04-2023). The ethics committee have given approval for the study. The requirement for informed consent was waived due to the study’s retrospective design. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Rajasekaran A, Julian BA, Rizk DV. IgA Nephropathy: An Interesting Autoimmune Kidney Disease. Am J Med Sci. 2021;361(2):176-94. doi: 10.1016/j.amjms.2020.10.003. Zhang Z, Zhang Y, Zhang H. IgA Nephropathy: A Chinese Perspective. Glomerular Dis. 2022;2(1):30-41. doi: 10.1159/000520039. Ferro CJ, Mark PB, Kanbay M, Sarafidis P, Heine GH, Rossignol P, Massy ZA, et al. Lipid management in patients with chronic kidney disease. Nat Rev Nephrol. 2018;14(12):727-49. doi: 10.1038/s41581-018-0072-9. Su X, Zhang L, Lv J, Wang J, Hou W, Xie X, Zhang H. Effect of Statins on Kidney Disease Outcomes: A Systematic Review and Meta-analysis. Am J Kidney Dis. 2016;67(6):881-92. doi: 10.1053/j.ajkd.2016.01.016. Suh SH, Oh TR, Choi HS, Kim CS, Bae EH, Oh KH, Han SH, et al. Serum triglycerides level is independently associated with renal outcomes in patients with non-dialysis chronic kidney disease: Results from KNOW-CKD study. Front Nutr. 2022;9:1037618. doi: 10.3389/fnut.2022.1037618. Syrjänen J, Mustonen J, Pasternack A. Hypertriglyceridaemia and hyperuricaemia are risk factors for progression of IgA nephropathy. Nephrol Dial Transplant. 2000;15(1):34-42. doi: 10.1093/ndt/15.1.34. Liu S, Lu Z, Fu Z, Li H, Gui C, Deng Y. Clinicopathological Characteristics and Outcomes of Immunoglobulin A Nephropathy with Different Types of Dyslipidemia: A Retrospective Single-Center Study. Kidney Blood Press Res. 2023;48(1):186-93. doi: 10.1159/000529822. Liu B, Zhao L, Yang Q, Zha D, Si X. Hyperuricemia and hypertriglyceridemia indicate tubular atrophy/interstitial fibrosis in patients with IgA nephropathy and membranous nephropathy. Int Urol Nephrol. 2021;53(11):2321-32. doi: 10.1007/s11255-021-02844-4. Wang J, He L, Yan W, Peng X, He L, Yang D, Liu H, et al. The role of hypertriglyceridemia and treatment patterns in the progression of IgA nephropathy with a high proportion of global glomerulosclerosis. Int Urol Nephrol. 2020;52(2):325-35. doi: 10.1007/s11255-019-02371-3. Suzuki H, Ohsawa I, Kodama F, Nakayama K, Ohtani A, Onda K, Nagamachi S, et al. Fluctuation of serum C3 levels reflects disease activity and metabolic background in patients with IgA nephropathy. J Nephrol. 2013;26(4):708-15. doi: 10.5301/jn.5000278. Barbu A, Hamad OA, Lind L, Ekdahl KN, Nilsson B. The role of complement factor C3 in lipid metabolism. Mol Immunol. 2015;67(1):101-7. doi: 10.1016/j.molimm.2015.02.027. Stamellou E, Seikrit C, Tang S, Boor P, Tesař V, Floege J, Barratt J, et al. IgA nephropathy. Nat Rev Dis Primers. 2023;9(1):67. doi: 10.1038/s41572-023-00476-9. Faria B, Canão P, Cai Q, Henriques C, Matos AC, Poppelaars F, Gaya DCM, et al. Arteriolar C4d in IgA Nephropathy: A Cohort Study. Am J Kidney Dis. 2020;76(5):669-78. doi: 10.1053/j.ajkd.2020.03.017. Ruan Y, Hong F, Lin M, Wang C, Lian F, Cao F, Yang G, et al. Clinicopathological characteristics, risk factors and prognostic value of intrarenal vascular lesions in IgA nephropathy. Eur J Intern Med. 2023;117:91-7. doi: 10.1016/j.ejim.2023.07.007. de Vries MA, Klop B, Janssen HW, Njo TL, Westerman EM, Castro CM. Postprandial inflammation: targeting glucose and lipids. Adv Exp Med Biol. 2014;824:161-70. doi: 10.1007/978-3-319-07320-0_12. Suh SH, Kim SW. Dyslipidemia in Patients with Chronic Kidney Disease: An Updated Overview. Diabetes Metab J. 2023;47(5):612-29. doi: 10.4093/dmj.2023.0067. Hou W, Lv J, Perkovic V, Yang L, Zhao N, Jardine MJ, Cass A, et al. Effect of statin therapy on cardiovascular and renal outcomes in patients with chronic kidney disease: a systematic review and meta-analysis. Eur Heart J. 2013;34(24):1807-17. doi: 10.1093/eurheartj/eht065. Suh SH, Oh TR, Choi HS, Kim CS, Bae EH, Ma SK, Oh KH, et al. Non-High-Density Lipoprotein Cholesterol and Progression of Chronic Kidney Disease: Results from the KNOW-CKD Study. Nutrients. 2022;14(21)doi: 10.3390/nu14214704. Sági B, Vas T, Csiky B, Nagy J, Kovács TJ. Does Metabolic Syndrome and Its Components Have Prognostic Significance for Renal and Cardiovascular Outcomes in IgA Nephropathy? Biomedicines. 2024;12(6)doi: 10.3390/biomedicines12061250. Lee C, Park JT, Chang TI, Kang EW, Nam KH, Joo YS, Sung SA, et al. Low-density lipoprotein cholesterol levels and adverse clinical outcomes in chronic kidney disease: Results from the KNOW-CKD. Nutr Metab Cardiovasc Dis. 2022;32(2):410-9. doi: 10.1016/j.numecd.2021.09.037. Tian ZY, Li AM, Chu L, Hu J, Xie X, Zhang H. Prognostic value of low-density lipoprotein cholesterol in IgA nephropathy and establishment of nomogram model. Front Endocrinol (Lausanne). 2023;14:1037773. doi: 10.3389/fendo.2023.1037773. Nam KH, Chang TI, Joo YS, Kim J, Lee S, Lee C, Yun HR, et al. Association Between Serum High-Density Lipoprotein Cholesterol Levels and Progression of Chronic Kidney Disease: Results From the KNOW-CKD. J Am Heart Assoc. 2019;8(6):e11162. doi: 10.1161/JAHA.118.011162. Zhuang H, Lin Z, Zeng S, Jiang M, Chen L, Jiang X, Xu Y. Dyslipidemia may be a risk factor for progression in children with IgA nephropathy. Pediatr Nephrol. 2022;37(12):3147-56. doi: 10.1007/s00467-022-05480-x. 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. 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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-6539765","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":456187664,"identity":"4124d552-f0b7-4167-a0b9-32180326c2e9","order_by":0,"name":"Yiping Ruan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAnUlEQVRIiWNgGAWjYNCCCgk5eaIV84DJMxbGhg0kaWFsq0hkOECsFnuJ3GfShfMkEhgbmB8+ukGULRLpZtIzt0nksTOwGRvnEKcljU2ad5tEMWMDD5s0CVrmSCQ2HCBNSwNJWs48Y7aecUzC2LCZWL+wt6cx3i6oqZOTZ29++JgoLQwCCSzSYAYzUcpBgP8A82eiFY+CUTAKRsHIBACtLibloFCHagAAAABJRU5ErkJggg==","orcid":"","institution":"Fuzhou University Affiliated Provincial Hospital, Fujian Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yiping","middleName":"","lastName":"Ruan","suffix":""},{"id":456187665,"identity":"72c75358-ccfb-49b4-a0fb-90570b712179","order_by":1,"name":"Qiaoyun Huang","email":"","orcid":"","institution":"Fuzhou University Zhicheng College","correspondingAuthor":false,"prefix":"","firstName":"Qiaoyun","middleName":"","lastName":"Huang","suffix":""},{"id":456187666,"identity":"82d875a7-0c8d-4db1-add2-4f68eeafb21b","order_by":2,"name":"Fuyuan Hong","email":"","orcid":"","institution":"Fuzhou University Affiliated Provincial Hospital, Fujian Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fuyuan","middleName":"","lastName":"Hong","suffix":""},{"id":456187667,"identity":"9ae9c959-e7f0-4445-a794-f62289352d43","order_by":3,"name":"Miao Lin","email":"","orcid":"","institution":"Fuzhou University Affiliated Provincial Hospital, Fujian Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Miao","middleName":"","lastName":"Lin","suffix":""},{"id":456187668,"identity":"c37d62e6-942e-4ba7-a9fc-2e39ca13473f","order_by":4,"name":"Chen Wang","email":"","orcid":"","institution":"Fuzhou University Affiliated Provincial Hospital, Fujian Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Wang","suffix":""},{"id":456187669,"identity":"000e4b78-2621-4209-aca1-a0232cf13b12","order_by":5,"name":"Fayang Lian","email":"","orcid":"","institution":"Fuzhou University Affiliated Provincial Hospital, Fujian Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fayang","middleName":"","lastName":"Lian","suffix":""},{"id":456187670,"identity":"9ccdd4ec-9f77-4073-9079-137c3caf38bd","order_by":6,"name":"Fang Cao","email":"","orcid":"","institution":"Fuzhou University Affiliated Provincial Hospital, Fujian Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fang","middleName":"","lastName":"Cao","suffix":""},{"id":456187671,"identity":"e5c98a4a-97da-4487-ba8c-3676f8284377","order_by":7,"name":"Guokai Yang","email":"","orcid":"","institution":"Fuzhou University Affiliated Provincial Hospital, Fujian Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Guokai","middleName":"","lastName":"Yang","suffix":""},{"id":456187672,"identity":"e7a800fc-013b-4d4f-b616-1c5601f88cce","order_by":8,"name":"Lanting Huang","email":"","orcid":"","institution":"Fuzhou University Affiliated Provincial Hospital, Fujian Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lanting","middleName":"","lastName":"Huang","suffix":""},{"id":456187673,"identity":"9ca1a048-e173-4919-bc1a-3d0dbea43c02","order_by":9,"name":"Xuejing Chen","email":"","orcid":"","institution":"Fuzhou University Affiliated Provincial Hospital, Fujian Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xuejing","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-04-27 10:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6539765/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6539765/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82890547,"identity":"d5139d29-8476-48bf-85aa-fc1ab4456c59","added_by":"auto","created_at":"2025-05-16 12:09:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":373543,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier renal survival for IgA nephropathy patients with dyslipidemia. All patients were divided into 2 groups according to the presence or absence of hyperlipidemia. Compared with the patients without dyslipidemia, the renal survival rate in the patients with dyslipidemia was significantly lower (log-rank test, p=0.001).\u003c/p\u003e\n\u003cp\u003ep\u0026lt;0.05 was considered significant.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6539765/v1/86eeaf37789f6645dbbffc1b.png"},{"id":82890545,"identity":"4a33efc3-5688-4d22-83e9-1e336fa4b49a","added_by":"auto","created_at":"2025-05-16 12:09:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":291498,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier renal survival for IgA nephropathy patients with hypercholesterolemia. All patients were divided into 2 groups according to the presence or absence of hypercholesterolemia (total cholesterol ≥5.2mmol/L). Compared with the patients without hypercholesterolemia, the renal survival rate in the patients with hypercholesterolemia was significantly lower (log-rank test, p=0.016).\u003c/p\u003e\n\u003cp\u003ep\u0026lt;0.05 was considered significant.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6539765/v1/99ecbe4af0b6e9cdb87eccaf.png"},{"id":82890543,"identity":"799d704a-ff1a-403c-a23b-41e44a0c82c6","added_by":"auto","created_at":"2025-05-16 12:09:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":412864,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier renal survival for IgA nephropathy patients with hypertriglyceridemia. All patients were divided into 2 groups according to the presence or absence of hypertriglyceridemia (serum triglycerides ≥1.7mmol/L). Compared with the patients without hypertriglyceridemia, the renal survival rate in the patients with hypertriglyceridemia was significantly lower (log-rank test, p=0.001). p\u0026lt;0.05 was considered significant.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6539765/v1/552e586f9b1eac76b98645e0.png"},{"id":82890557,"identity":"72e0b27c-24e3-4f18-afd4-c8e16380eabb","added_by":"auto","created_at":"2025-05-16 12:09:26","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":249168,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier renal survival for IgA nephropathy patients with high low-density lipoprotein cholesterol (LDL-C). All patients were divided into 2 groups according to the presence or absence of elevated LDL-C level (LDL-C ≥3.4mmol/L). There was no significant difference in renal survival between two groups (log-rank test, p=0.204).\u003c/p\u003e\n\u003cp\u003ep\u0026lt;0.05 was considered significant.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6539765/v1/5b8d45eb3c1b5ebe8a4c20c3.png"},{"id":82890530,"identity":"cf0d05c4-e3b2-40f9-854c-16a66d468977","added_by":"auto","created_at":"2025-05-16 12:09:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":284193,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier renal survival for IgA nephropathy patients with dyslipidemia after lipid-lowering therapies. All patients were divided into 2 groups according to whether they received lipid-lowering therapies. Compared with the patients without lipid-lowering therapies, the renal survival rate in the patients with lipid-lowering therapies was significantly lower (log-rank test, p=0.029).\u003c/p\u003e\n\u003cp\u003ep\u0026lt;0.05 was considered significant.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6539765/v1/65efec50131e7034410b9007.png"},{"id":89517578,"identity":"7cb22c63-d265-4a3a-a8cd-a2190db2c1f8","added_by":"auto","created_at":"2025-08-20 20:23:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2392769,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6539765/v1/e98e87e7-1651-4f0a-9b0c-7a04155b3c16.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinicopathological Characteristics and Prognostic Value of Dyslipidemia in IgA Nephropathy: A Retrospective Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eImmunoglobulin A nephropathy (IgAN), recognized as the leading cause of primary glomerular disease globally, manifests pathologically dominant IgA deposition in the glomerular mesangium. While regional disparities epidemiologically exist, IgAN accounts for nearly 50% of primary glomerular diseases in China and significantly elevates the risk of end stage renal disease (ESRD)[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Remarkably, ERSD occurs in approximately 40% of patients about 20 years after diagnosis[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGenerally, levels of renal function and proteinuria in patients with chronic kidney disease (CKD) including IgAN can affect the levels and properties of circulating lipids including triglycerides, low-density lipoprotein cholesterol, and others [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Dyslipidemia is also a potentially modifiable cardiovascular risk factor for CKD. Accordingly, Kidney Disease: Improving Global Outcomes (KDIGO) released a comprehensive clinical practice guideline for lipid management in CKD. However, the association between dyslipidemia and renal outcomes in CKD, especially in IgAN, although previously suggested, was not well-established, and clinical studies failed to demonstrate significant protective benefits for renal prognosis from lipid-lowering therapies [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Moreover, the predictive value of dyslipidemia for renal outcomes in CKD remained unknown.\u003c/p\u003e \u003cp\u003eUntil now the role of dyslipidemia was not adequately evaluated in follow-up trials in IgAN. In this single-center, retrospective study, we utilized our cohort to investigate clinicopathological characteristics and renal outcomes in IgAN patients with dyslipidemia, as well as prognostic value of dyslipidemia, from Eastern China.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003ePatients\u003c/p\u003e \u003cp\u003eA total of 458 patients (age\u0026thinsp;\u0026gt;\u0026thinsp;14 years) with biopsy-confirmed primary IgAN from January 2010 to October 2021 were enrolled. Patients with fewer than eight glomeruli on the biopsy or with secondary causes of mesangial IgA deposits, such as Henoch-Schonlein purpura, systemic lupus erythematosus and liver disease, were excluded.\u003c/p\u003e \u003cp\u003eMeasurements\u003c/p\u003e \u003cp\u003eBaseline demographic and clinicopathologic data collected at biopsy included as follow: age, gender, medical history, systolic and diastolic blood pressure, serum creatinine, blood urea nitrogen (BUN), serum albumin, uric acid, serum cholesterol, triglycerides, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), amount of red blood cell (RBC) in urine and proteinuria, serum IgA, C3, C4 and treatment modalities.\u003c/p\u003e \u003cp\u003eDyslipidemia was defined as total cholesterol\u0026thinsp;\u0026ge;\u0026thinsp;5.2mmol/L, serum triglycerides\u0026thinsp;\u0026ge;\u0026thinsp;1.7mmol/L, or LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;3.4mmol/L. Hypertension was defined as blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140/90 mmHg; blood pressure measurements were repeated twice in a patient who was relaxed and sitting in a chair for \u0026gt;\u0026thinsp;5 min and in the patient\u0026rsquo;s right arm. Mean arterial pressure (MAP) was defined as a diastolic pressure plus one-third of the pulse pressure. Proteinuria was measured by a 24-h urine protein collection. Estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. Lipid-lowering therapy primarily involved statins, with fibrates reserved for hypertriglyceridemia. Treatment modalities of renin-angiotensin system inhibitors (RASi) included angiotensin-converting enzyme inhibitor (ACEI), angiotensin receptor blocker (ARB), or both. Patients received optimized supportive therapy with RAS blockade to achieve target blood pressure (urine albumin excretion\u0026thinsp;\u0026lt;\u0026thinsp;30 mg/24 hours, BP\u0026thinsp;\u0026le;\u0026thinsp;140/90mmHg; urine albumin excretion\u0026thinsp;\u0026ge;\u0026thinsp;30 mg/24 hours, BP\u0026thinsp;\u0026le;\u0026thinsp;130/80mmHg). Immunosuppressive therapy was defined as receiving corticosteroids and/or immunosuppressants after biopsy. Corticosteroid therapy referred to oral prednisone (started at 0.5-1 mg/kg daily, tapering down within 6\u0026ndash;8 months). Immunosuppressants indicated cyclophosphamide (used at a total dosage of 6 to 8 g within 6\u0026ndash;8 months).\u003c/p\u003e \u003cp\u003eRenal biopsy specimens from all patients were divided routinely for immunofluorescence microscopy, light microscopy and electron microscopy. The paraffin-embedded sections were stained with hematoxylin and eosin, periodic acid-Schiff, silver methenamine, and Masson\u0026rsquo;s trichrome. All renal biopsy results were reviewed independently by two renal pathologists according to the updated Oxford classification. Pathologic features of the updated Oxford classification were defined as follows: mesangial score of \u0026le;\u0026thinsp;0.5 (M0) or \u0026gt;\u0026thinsp;0.5 (M1); endocapillary hypercellularity absent (E0) or present (E1); segmental glomerulosclerosis absent (S0) or present (S1); tubular atrophy atrophy/interstitial fibrosis\u0026thinsp;\u0026le;\u0026thinsp;25% (T0), 26\u0026ndash;50% (T1) or \u0026gt;\u0026thinsp;50% (T2); and cellular/fibrocellular crescents absent (C0), present in at least 1 glomerulus (C1), in \u0026gt;\u0026thinsp;25% of glomeruli (C2). Pathological features also included global glomerulosclerosis, tuft necrosis, interstitial inflammation, arterial intimal fibrosis, arteriolar hyalinosis and IgA glomerulus immunofluorescence.\u003c/p\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003cp\u003eThe time of renal biopsy was defined as the starting point. The primary outcome of interest was a composite kidney outcome defined as: 1) End-stage renal disease (ESRD); or 2)\u0026thinsp;\u0026gt;\u0026thinsp;50% decline in eGFR. ESRD was defined as eGFR\u0026thinsp;\u0026lt;\u0026thinsp;15 ml/min per 1.73 m\u003csup\u003e2\u003c/sup\u003e or initiation of dialysis or transplantation.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables were expressed as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and analyzed with t-test or Mann-Whitney U-test. Categorical variables were described as frequency with percentages and analyzed using χ\u003csup\u003e2\u003c/sup\u003e test. Renal survival was analyzed with the Kaplan-Meier method compared by Log rank test. Associations between pathological features and dyslipidemia were also analyzed using univariate followed by multivariate logistic regression. The results were expressed as odds ratio (OR) with 95% confidence intervals (CIs). Univariate followed by multivariate Cox regression were used to determine the independent predictive value of dyslipidemia for renal survival. The clinicopathologic features were included in univariate Cox regression and then only the features significantly associated with renal survival were considered in multivariate regression. The results were expressed as hazard ratio (HR) with 95% confidence intervals (CIs). P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. All statistical analysis was performed with SPSS 22.0 (SPSS Inc., Chicago, IL, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eClinical characteristics\u003c/p\u003e \u003cp\u003eAt the time of biopsy, the average age of 458 patients recruited in our study was 33.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0 years. Mean eGFR was 87.7\u0026thinsp;\u0026plusmn;\u0026thinsp;33.7 ml/min per 1.73 m\u003csup\u003e2\u003c/sup\u003e. According to the Kidney Disease Outcomes Quality Initiative classification, 352(76.9%) patients had stage 1 or 2 chronic kidney disease and 79(17.2%) and 27(5.9%) patients had stages 3 and 4 chronic kidney disease, respectively. The mean follow-up time was 54.7\u0026thinsp;\u0026plusmn;\u0026thinsp;20.4 months. During follow-up, the majority of patients received RASi. 137 (29.9%) patients received corticosteroids and 12 patients (2.6%) received immunosuppressants.\u003c/p\u003e \u003cp\u003eOverall, 256 of 458 (55.9%) patients presented with dyslipidemia including 181 (39.5%) patients with total cholesterol\u0026thinsp;\u0026ge;\u0026thinsp;5.2mmol/L, 148 (32.3%) with triglycerides\u0026thinsp;\u0026ge;\u0026thinsp;1.7mmol/L, and 159 (34.7%) with LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;3.4mmol/L. At the time of renal biopsy, except for HDL-C, the levels of lipids were higher in patients with dyslipidemia, including cholesterol (4.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55 vs 5.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.58mmol/L, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), triglycerides (0.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33 vs 2.28\u0026thinsp;\u0026plusmn;\u0026thinsp;2.12mmol/L, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and LDL-C (2.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47 vs 3.68\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29mmol/L, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients with dyslipidemia presented with older age (30.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3 vs 35.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7years, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), higher levels of blood pressure including systolic blood pressure (121.0\u0026thinsp;\u0026plusmn;\u0026thinsp;16.3 vs 130.0\u0026thinsp;\u0026plusmn;\u0026thinsp;19.0mmHg, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), diastolic blood pressure (76.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8 vs 82.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4mmHg, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and MAP (91.4\u0026thinsp;\u0026plusmn;\u0026thinsp;12.4 vs 98.1\u0026thinsp;\u0026plusmn;\u0026thinsp;14.2mmHg, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), serum creatinine (93.1\u0026thinsp;\u0026plusmn;\u0026thinsp;58.6 vs 110.0\u0026thinsp;\u0026plusmn;\u0026thinsp;60.5\u0026micro;mol/L, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), BUN (5.91\u0026thinsp;\u0026plusmn;\u0026thinsp;4.28 vs 6.92\u0026thinsp;\u0026plusmn;\u0026thinsp;3.37mmol/L, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), uric acid (337.1\u0026thinsp;\u0026plusmn;\u0026thinsp;117.7 vs 394.4\u0026thinsp;\u0026plusmn;\u0026thinsp;125.3mmol/L, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), proteinuria (0.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89 vs 1.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21g/day, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), C3 (0.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18 vs 1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19g/L, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and C4 (0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07 vs 0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08g/L, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) as well as lower levels of eGFR (96.5\u0026thinsp;\u0026plusmn;\u0026thinsp;31.8 vs 80.8\u0026thinsp;\u0026plusmn;\u0026thinsp;33.7 ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and serum albumin (40.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1 vs 38.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5g/L, P\u0026thinsp;=\u0026thinsp;0.001). Proportion of patients with hypertension (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) was higher in the group with dyslipidemia. There were no significant differences in gender, tonsillitis, hematuria, and serum IgA between two groups.\u003c/p\u003e \u003cp\u003eLength of follow-up was similar, but compared with patients without dyslipidemia, patients with dyslipidemia received more corticosteroids (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and lipid-lowering therapies (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and similar immunosuppressants. The rate of renal end point was higher in the group with dyslipidemia (6.4% vs 16.0%, P\u0026thinsp;=\u0026thinsp;0.002) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical and pathological characteristics of IgA nephropathy patients with dyslipidemia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo dyslipidemia\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;202)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDyslipidemia\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;256)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep value\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88, 43.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123, 48.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTonsillitis (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44, 21.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38, 14.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50, 24.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113, 44.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic BP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121.0\u0026thinsp;\u0026plusmn;\u0026thinsp;16.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130.0\u0026thinsp;\u0026plusmn;\u0026thinsp;19.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic BP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91.4\u0026thinsp;\u0026plusmn;\u0026thinsp;12.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.1\u0026thinsp;\u0026plusmn;\u0026thinsp;14.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e337.1\u0026thinsp;\u0026plusmn;\u0026thinsp;117.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e394.4\u0026thinsp;\u0026plusmn;\u0026thinsp;125.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum creatinine (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93.1\u0026thinsp;\u0026plusmn;\u0026thinsp;58.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110.0\u0026thinsp;\u0026plusmn;\u0026thinsp;60.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum creatinine\u0026thinsp;\u0026gt;\u0026thinsp;133\u0026micro;mol/L (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25, 12.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63, 24.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR (ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96.5\u0026thinsp;\u0026plusmn;\u0026thinsp;31.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.8\u0026thinsp;\u0026plusmn;\u0026thinsp;33.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.91\u0026thinsp;\u0026plusmn;\u0026thinsp;4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.92\u0026thinsp;\u0026plusmn;\u0026thinsp;3.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProteinuria (g/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum albumin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.28\u0026thinsp;\u0026plusmn;\u0026thinsp;2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.68\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBC in urine\u0026thinsp;\u0026ge;\u0026thinsp;2+ (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85, 42.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96, 37.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum IgA (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.467\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum C3 (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum C4 (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of follow-up (months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.4\u0026thinsp;\u0026plusmn;\u0026thinsp;21.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.3\u0026thinsp;\u0026plusmn;\u0026thinsp;19.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipid-lowering therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0, 0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67, 26.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSteroids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37, 18.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100, 39.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunosuppressant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5, 2.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7, 2.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESRD or doubling serum creatinine (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13, 6.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41, 16.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal glomerulosclerosis (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135, 66.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e214, 83.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTuft necrosis (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18, 8.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27, 10.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.559\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMesangial hypercellularity, M1 (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e186, 92.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e238, 93.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.718\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndocapillary hypercellularity, E1 (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34, 16.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49, 19.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSegmental glomerulosclerosis, S1 (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81, 40.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136, 53.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTubular atrophy/interstitial fibrosis, T (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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e163, 80.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e161, 62.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37, 18.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87, 34.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2, 1.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8, 3.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrescents (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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126, 62.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129, 50.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72, 35.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115, 44.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4, 2.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12, 4.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocytes and monocytes infiltration (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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026thinsp;\u0026minus;\u0026thinsp;25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e171, 84.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e198, 77.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31, 15.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58, 22.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArterial intimal fibrosis (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52, 25.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111, 43.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArteriolar hyalinosis (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30, 14.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63, 24.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIgA glomerulus immunofluorescence (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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e+/++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111, 55.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141, 55.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e+++/++++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91, 45.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115, 44.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eBP, blood pressure; MAP, mean arterial pressure; BMI, body mass index; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; eGFR, estimated glomerular filtration rate; BUN, blood urea nitrogen; RBC, red blood cell; RAS, renin-angiotensin system; ESRD, end-stage renal disease.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003ea\u003c/sup\u003e p value: comparison between two groups. p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003eTable 2. Associations between pathological features and dyslipidemia analyzed by logistic regression\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003eCI, confidence interval. p\u0026lt;0.05 was considered significant.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Multivariate model: All pathological parameters significantly associated with dyslipidemia were included.\u003c/p\u003e\n \u003cp\u003ePathological characteristics\u003c/p\u003e \u003cp\u003eIn general, pathological features, including global glomerulosclerosis (135, 66.8% vs 214, 83.6%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), segmental glomerulosclerosis (S1) (81, 40.1% vs 136, 53.1%, P\u0026thinsp;=\u0026thinsp;0.006), tubular atrophy/interstitial fibrosis (T1/2) (T1: 37, 18.3% vs 87, 34.0%; T2: 2, 1.0% vs 8, 3.1%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), crescents (C1/2) (C1: 72, 35.6% vs 115, 44.9%; C2: 4, 2.0% vs 12, 4.7%; P\u0026thinsp;=\u0026thinsp;0.022), arterial intimal fibrosis (52, 25.7% vs 111, 43.4%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and arteriolar hyalinosis (30, 14.9% vs 63, 24.6%, P\u0026thinsp;=\u0026thinsp;0.010) were more severe in patients with dyslipidemia. There was no difference in tuft necrosis, mesangial hypercellularity (M1), Endocapillary hypercellularity (E1), lymphocytes and monocytes infiltration, and IgA glomerulus immunofluorescence between two groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDyslipidemia-associated pathological factors\u003c/p\u003e \u003cp\u003eWhen analyzing associations between dyslipidemia and various pathological parameters, univariate logistic regression analysis showed that global glomerulosclerosis, S1, T1-2, C1-2, arterial intimal fibrosis and arteriolar hyalinosis were positively correlated with dyslipidemia. Then by multivariate logistic regression analysis, T1-2 (OR\u0026thinsp;=\u0026thinsp;1.634, 95% CI\u0026thinsp;=\u0026thinsp;1.015\u0026ndash;2.632, P\u0026thinsp;=\u0026thinsp;0.043), and arterial intimal fibrosis (OR\u0026thinsp;=\u0026thinsp;1.609, 95% CI\u0026thinsp;=\u0026thinsp;1.037\u0026ndash;2.497, P\u0026thinsp;=\u0026thinsp;0.034) remained to be significantly associated with dyslipidemia (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRenal outcome\u003c/p\u003e\n\u003cp\u003eAfter a mean follow-up of 54.7 months, ESRD or \u0026gt;\u0026thinsp;50% decline in eGFR occurred in 54 patients (11.8% of all patients). No death was reported in all patients. Compared with the patients without dyslipidemia, the 5-year renal survival rate was significantly lower in these with dyslipidemia (93.3% vs 84.0%, log-rank P\u0026thinsp;=\u0026thinsp;0.001) (Fig.\u0026nbsp;1). Furthermore, we found that the 5-year renal survival rate was significantly lower in patients with hypercholesterolemia (total cholesterol\u0026thinsp;\u0026ge;\u0026thinsp;5.2mmol/L) (90.6% vs 84.2%, log-rank test, P\u0026thinsp;=\u0026thinsp;0.016) (Fig.\u0026nbsp;2) and hypertriglyceridemia (serum triglycerides\u0026thinsp;\u0026ge;\u0026thinsp;1.7mmol/L) (91.4% vs 81.4%, log-rank test, P\u0026thinsp;=\u0026thinsp;0.001) (Fig.\u0026nbsp;3). However, there was no significant difference in renal survival between patients with and without elevated LDL-C level (LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;3.4mmol/L) in the follow-up (log-rank P\u0026thinsp;=\u0026thinsp;0.204) (Fig.\u0026nbsp;4). In addition, the renal survival was similar whether decreased HDL-C level (HDL-C\u0026thinsp;\u0026lt;\u0026thinsp;1.0mmol/L) was present or not (log-rank P\u0026thinsp;=\u0026thinsp;0.138). For patients with dyslipidemia, after lipid-lowering therapies the 5-year renal survival rate was significantly lower (86.4% vs 77.2%, log-rank test, P\u0026thinsp;=\u0026thinsp;0.029) (Fig.\u0026nbsp;5).\u003c/p\u003e\n\u003cp\u003eAdjusted predictive value of dyslipidemia\u003c/p\u003e\n\u003cp\u003eUnivariate analyses followed by multivariate analyses were performed to examine independently predictive value of dyslipidemia for renal outcome. Clinicopathological parameters were used in the univariate analyses: age, gender, MAP, initial eGFR, proteinuria, treatment modalities, MEST-C scores based on the updated Oxford classification, vascular lesions. In univariate Cox analyses, dyslipidemia (HR\u0026thinsp;=\u0026thinsp;2.871, 95% CI\u0026thinsp;=\u0026thinsp;1.533\u0026ndash;5.376, P\u0026thinsp;=\u0026thinsp;0.001), as well as age, MAP, eGFR, endocapillary hypercellularity (E1), tubular atrophy/interstitial fibrosis (T1-2), arterial intimal fibrosis and arteriolar hyalinosis were strongly associated with renal survival. Nevertheless, after adjusting for clinical parameters in multivariable model 1 and for clinicopathological parameters in multivariable model 2, dyslipidemia (model 1: HR\u0026thinsp;=\u0026thinsp;2.229, 95% CI\u0026thinsp;=\u0026thinsp;1.146\u0026ndash;4.336, P\u0026thinsp;=\u0026thinsp;0.018; model 2: HR\u0026thinsp;=\u0026thinsp;2.117, 95% CI\u0026thinsp;=\u0026thinsp;1.082\u0026ndash;4.145, P\u0026thinsp;=\u0026thinsp;0.029), as well as eGFR at baseline (model 1: HR\u0026thinsp;=\u0026thinsp;0.980, 95% CI\u0026thinsp;=\u0026thinsp;0.970\u0026ndash;0.989, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; model 2: HR\u0026thinsp;=\u0026thinsp;0.983, 95% CI\u0026thinsp;=\u0026thinsp;0.972\u0026ndash;0.994, P\u0026thinsp;=\u0026thinsp;0.004) and arterial intimal fibrosis (model 2: HR\u0026thinsp;=\u0026thinsp;2.107, 95% CI\u0026thinsp;=\u0026thinsp;1.111\u0026ndash;3.994, P\u0026thinsp;=\u0026thinsp;0.022), were still independent predictors for renal survival (Table\u0026nbsp;3).\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003ePredictive value of dyslipidemia for renal survival by univariate and multivariate Cox regression\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eUnivariable model\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eMultivariable model 1 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eMultivariable model 2 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDyslipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.533\u0026ndash;5.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.146\u0026ndash;4.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.082\u0026ndash;4.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.001\u0026ndash;1.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.970\u0026ndash;1.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.962\u0026ndash;1.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.495\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.933\u0026ndash;2.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.004\u0026ndash;1.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.976\u0026ndash;1.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.974\u0026ndash;1.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeGFR (mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.970\u0026ndash;0.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.970\u0026ndash;0.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.972\u0026ndash;0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProteinuria (g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.893\u0026ndash;1.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSteroids therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.772\u0026ndash;2.366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImmunosuppressant therapy \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.124-6.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.243\u0026ndash;1.334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.111\u0026ndash;4.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.799\u0026ndash;3.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.429\u0026ndash;1.310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT1-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.459\u0026ndash;4.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.414\u0026ndash;1.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.641\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC1-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.780\u0026ndash;2.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArterial intimal fibrosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.139\u0026ndash;6.461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.111\u0026ndash;3.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArteriolar hyalinosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.108\u0026ndash;4.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.652\u0026ndash;2.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\"\u003eMAP, mean arterial pressure; eGFR, estimated glomerular filtration rate; M, mesangial hypercellularity; E, endocapillary hypercellularity; S, segmental glomerulosclerosis; T, tubular atrophy/interstitial fibrosis; C, crescents; CI, confidence interval. p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\"\u003e\u003csup\u003ea\u003c/sup\u003e Multivariable model 1: All clinical parameters significantly associated with renal outcome were included.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\"\u003e\u003csup\u003eb\u003c/sup\u003e Multivariable model 2: All clinical and pathologic parameters significantly associated with renal outcome were included.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\"\u003e\u003csup\u003ec\u003c/sup\u003e Immunosuppressant referred to cyclophosphamide.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIn addition, the prognostic performance of various lipid profiles combinations was assessed in the univariable model and the same multivariable models for adjustment including: 1) abnormal levels of total cholesterol (\u0026ge;\u0026thinsp;5.2mmol/L) or triglycerides (\u0026ge;\u0026thinsp;1.7mmol/L) (univariable model: HR\u0026thinsp;=\u0026thinsp;2.472, 95% CI\u0026thinsp;=\u0026thinsp;1.375\u0026ndash;4.444, P\u0026thinsp;=\u0026thinsp;0.002; multivariable model 1: HR\u0026thinsp;=\u0026thinsp;1.895, 95% CI\u0026thinsp;=\u0026thinsp;1.013\u0026ndash;3.545, P\u0026thinsp;=\u0026thinsp;0.046; multivariable model 2: HR\u0026thinsp;=\u0026thinsp;1.791, 95% CI\u0026thinsp;=\u0026thinsp;0.949\u0026ndash;3.378, P\u0026thinsp;=\u0026thinsp;0.072); and 2) abnormal levels of total cholesterol (\u0026ge;\u0026thinsp;5.2mmol/L), triglycerides (\u0026ge;\u0026thinsp;1.7mmol/L), LDL-C (\u0026ge;\u0026thinsp;3.4mmol/L) or HDL-C (\u0026lt;\u0026thinsp;1.0mmol/L) (univariable model: HR\u0026thinsp;=\u0026thinsp;2.708, 95% CI\u0026thinsp;=\u0026thinsp;1.362\u0026ndash;5.383, P\u0026thinsp;=\u0026thinsp;0.004; multivariable model 1: HR\u0026thinsp;=\u0026thinsp;1.942, 95% CI\u0026thinsp;=\u0026thinsp;0.950\u0026ndash;3.972, P\u0026thinsp;=\u0026thinsp;0.069; multivariable model 2: HR\u0026thinsp;=\u0026thinsp;2.001, 95% CI\u0026thinsp;=\u0026thinsp;0.974\u0026ndash;4.112, P\u0026thinsp;=\u0026thinsp;0.059).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eUntil now, only few studies had comprehensively investigated detailed clinicopathological characteristics and renal outcome in IgA nephropathy (IgAN) with dyslipidemia, and evaluate prognostic value of dyslipidemia. In this IgAN cohort, we found that at least 55.9% of patients had different types of dyslipidemia. If measured by identical diagnostic criteria about abnormal lipid profiles, the prevalence of dyslipidemia in our study was comparable to that in recent studies [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Older age, decreased eGFR and serum albumin, and high levels of blood pressure, proteinuria, uric acid and serum complements including C3 and C4, were more severe in patients with dyslipidemia. Dyslipidemia was associated with several chronic pathological lesions including global and segmental glomerulosclerosis, tubular atrophy/interstitial fibrosis, and arterio-/arteriolosclerosis (refer to arterial intimal fibrosis and arteriolar hyalinosis). Besides, crescents that had been observed to be partially irreversible in several studies were also associated with dyslipidemia. Multivariate logistic regression analysis revealed that vascular and tubulointerstitial lesions were pathological features independently associated with dyslipidemia. IgAN patients with dyslipidemia presented with more severe clinicopathological manifestations, most of which were generally consistent with previous reports despite different concerns in lipid profiles across these researches including ours [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNotably, elevated complement levels had been observed in IgAN patients with dyslipidemia [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. For instance, serum complement C3, which were produced primarily by liver, and also in adipocytes, endothelial cells and activated macrophages, had been considered to be associated with metabolic disorders like dyslipidemia possibly because of overproduction and dysregulation of complement in adipose tissues [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. On the other side, complement components, like C3 and C3a-desArg, were involved in lipid storage and energy homeostasis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Given the renal pathogenicity of complement cascade activation and the involvement of alternative and mannose-binding lectin pathway in IgAN [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], dyslipidemia likely reflected complement activation. Furthermore, intrarenal vascular lesions and tubulointerstitial injury independently associated with dyslipidemia identified in this study might be relevant to complement activation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, although dyslipidemia was recognized to be involved in direct nephrotoxicity, inflammatory response and complement activation [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], taking into account the lack of improvement in renal outcomes with statin-based lipid-lowering therapies in prior studies [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], the specific mechanisms by which dyslipidemia aggravated the renal pathological alterations in IgAN remained incompletely understood.\u003c/p\u003e \u003cp\u003eDespite heterogeneity in classification criteria for dyslipidemia across studies, accumulating evidence had suggested the association between diverse patterns of lipid abnormalities and renal outcomes in CKD, whereas large-scale studies in IgAN remained absent. The relevance of triglyceride and cholesterol abnormalities to renal outcomes was indicated in CKD [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and IgAN [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] in previous and our studies. The value of LDL-C for renal prognosis was supported in CKD cohort [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] but controversial in IgAN. A study from China showed that elevated LDL-C level was a predictive factor for the prognosis of IgAN [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], which was contradictory according to our study. A U-shaped association was observed between serum HDL-C levels and adverse renal outcomes in large cohort of CKD from Korea [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, to date the prognostic value of HDL-C in IgAN, which was not confirmed in our cohort, were unreported by analogous investigations. For children with IgAN, dyslipidemia was also considered to be a risk factor for progression in a retrospective cohort study [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Moreover, it should be noted that our study revealed differential independent predictive values of lipid profile combinations for renal prognosis. In our IgAN cohort, multivariable-adjusted models demonstrated that the combination of elevated triglycerides, cholesterol, and LDL-C could show the better independent predictive capacity for adverse renal outcomes. The differences in the prognostic predictive capabilities among distinct lipid profiles suggested their involvement of diverse mechanisms in complement activation. Unfortunately, our study found that lipid-lowering therapies for patients with dyslipidemia demonstrated no improvement in renal prognosis, which supported findings from prior researches [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These findings not only facilitated the application of dyslipidemia for prediction of renal prognosis, but also implied that dyslipidemia possibly was involved in complex immune-related mechanisms that were not yet fully elucidated in IgAN.\u003c/p\u003e \u003cp\u003eSeveral limitations of this study should be recognized. First, given that IgAN was a long-term chronic renal disease, limited sample size might lead to some bias in our single-center retrospective cohort study. For instance, the association between lipid-lowering therapies and renal prognosis might be influenced. Second, as regard the mild clinicopathological features at onset and the influence of treatment on renal prognosis in patients with dyslipidemia, the follow-up time might be insufficient, so a significant difference was possibly missed in the renal survival, such as a possible difference between patients with and without elevated LDL-C. Thirdly, a limited number of patients received lipid-lowering therapies and immunosuppressants so it was difficult to explore their value in the limited analysis. Further well-designed multicenter cohort studies with longer regular follow-up and larger sample sizes, taking into account more clinicopathological parameters, were still necessary to confirm these disputed results.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIgAN patients with dyslipidemia presented more severe clinicopathological features. Tubular atrophy/interstitial fibrosis and arterio-/arteriolosclerosis were closely associated with dyslipidemia. Dyslipidemia not only indicated adverse renal outcomes, but also was an independent prognostic predictor.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCKD Chronic kidney disease\u003c/p\u003e\n\u003cp\u003eIgAN Immunoglobulin A nephropathy\u003c/p\u003e\n\u003cp\u003eESRD End stage renal disease\u003c/p\u003e\n\u003cp\u003eKDIGO Kidney Disease: Improving Global Outcomes\u003c/p\u003e\n\u003cp\u003eBUN Blood urea nitrogen\u003c/p\u003e\n\u003cp\u003eHDL-C High-density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eLDL-C Low-density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eRBC Red blood cell\u003c/p\u003e\n\u003cp\u003eMAP Mean arterial pressure\u003c/p\u003e\n\u003cp\u003eeGFR Estimated glomerular filtration rate\u003c/p\u003e\n\u003cp\u003eCKD-EPI Chronic Kidney Disease Epidemiology Collaboration\u003c/p\u003e\n\u003cp\u003eRAS Renin-angiotensin system\u003c/p\u003e\n\u003cp\u003eACEI Angiotensin-converting enzyme inhibitor\u003c/p\u003e\n\u003cp\u003eARB Angiotensin receptor blocker\u003c/p\u003e\n\u003cp\u003eOR Odds ratio\u003c/p\u003e\n\u003cp\u003eCI Confidence interval\u003c/p\u003e\n\u003cp\u003eHR Hazard ratio\u003c/p\u003e\n\u003cp\u003eM Mesangial hypercellularity\u003c/p\u003e\n\u003cp\u003eE Endocapillary hypercellularity\u003c/p\u003e\n\u003cp\u003eS Segmental glomerulosclerosis\u003c/p\u003e\n\u003cp\u003eT Tubular atrophy/interstitial fibrosis\u003c/p\u003e\n\u003cp\u003eC Crescents\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge all the clinicians, pathologists, statisticians, and laboratory technicians who contributed to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Yiping Ruan, Xuejing Chen; Methodology: Yiping Ruan, Xuejing Chen, Qiaoyun Huang; Formal analysis: Fayang Lian, Qiaoyun Huang; Resources: Fuyuan Hong, Yiping Ruan, Xuejing Chen, Qiaoyun Huang; Data Curation, Qiaoyun Huang; Investigation: Miao Lin, Chen Wang, Fang Cao, Guokai Yang, Lanting Huang; Writing - original draft preparation: Yiping Ruan, Xuejing Chen; Writing - review and editing: Yiping Ruan, Xuejing Chen, Qiaoyun Huang; Supervision, Fuyuan Hong and Miao Lin; Funding acquisition, Xuejing Chen. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was sponsored by Natural Science Foundation of Fujian, China (Grant No. 2024J08255).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for this research in accordance with the Declaration of Helsinki was acquired from Fujian Provincial Hospital Medical Ethics Committee (Ethics number: 26-04-2023). The ethics committee have given approval for the study. The requirement for informed consent was waived due to the study\u0026rsquo;s retrospective design.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRajasekaran A, Julian BA, Rizk DV. IgA Nephropathy: An Interesting Autoimmune Kidney Disease. Am J Med Sci. 2021;361(2):176-94. doi: 10.1016/j.amjms.2020.10.003.\u003c/li\u003e\n\u003cli\u003eZhang Z, Zhang Y, Zhang H. IgA Nephropathy: A Chinese Perspective. Glomerular Dis. 2022;2(1):30-41. doi: 10.1159/000520039.\u003c/li\u003e\n\u003cli\u003eFerro CJ, Mark PB, Kanbay M, Sarafidis P, Heine GH, Rossignol P, Massy ZA, et al. Lipid management in patients with chronic kidney disease. 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Non-High-Density Lipoprotein Cholesterol and Progression of Chronic Kidney Disease: Results from the KNOW-CKD Study. Nutrients. 2022;14(21)doi: 10.3390/nu14214704.\u003c/li\u003e\n\u003cli\u003eS\u0026aacute;gi B, Vas T, Csiky B, Nagy J, Kov\u0026aacute;cs TJ. Does Metabolic Syndrome and Its Components Have Prognostic Significance for Renal and Cardiovascular Outcomes in IgA Nephropathy? Biomedicines. 2024;12(6)doi: 10.3390/biomedicines12061250.\u003c/li\u003e\n\u003cli\u003eLee C, Park JT, Chang TI, Kang EW, Nam KH, Joo YS, Sung SA, et al. Low-density lipoprotein cholesterol levels and adverse clinical outcomes in chronic kidney disease: Results from the KNOW-CKD. Nutr Metab Cardiovasc Dis. 2022;32(2):410-9. doi: 10.1016/j.numecd.2021.09.037.\u003c/li\u003e\n\u003cli\u003eTian ZY, Li AM, Chu L, Hu J, Xie X, Zhang H. Prognostic value of low-density lipoprotein cholesterol in IgA nephropathy and establishment of nomogram model. Front Endocrinol (Lausanne). 2023;14:1037773. doi: 10.3389/fendo.2023.1037773.\u003c/li\u003e\n\u003cli\u003eNam KH, Chang TI, Joo YS, Kim J, Lee S, Lee C, Yun HR, et al. Association Between Serum High-Density Lipoprotein Cholesterol Levels and Progression of Chronic Kidney Disease: Results From the KNOW-CKD. J Am Heart Assoc. 2019;8(6):e11162. doi: 10.1161/JAHA.118.011162.\u003c/li\u003e\n\u003cli\u003eZhuang H, Lin Z, Zeng S, Jiang M, Chen L, Jiang X, Xu Y. Dyslipidemia may be a risk factor for progression in children with IgA nephropathy. Pediatr Nephrol. 2022;37(12):3147-56. doi: 10.1007/s00467-022-05480-x.\u003c/li\u003e\n\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":"IgA nephropathy, dyslipidemia, characteristics, outcome","lastPublishedDoi":"10.21203/rs.3.rs-6539765/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6539765/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDyslipidemia is common in chronic kidney disease (CKD) including IgA nephropathy (IgAN) and possibly associated with renal prognosis, but the value of dyslipidemia in IgAN remains insufficiently assessed. The objective of our study was to investigate clinicopathological characteristics and renal outcome in IgAN patients with dyslipidemia, and evaluate prognostic value of lipid abnormality.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis cohort study included 458 primary IgAN patients for a retrospective analysis. The clinicopathological features and renal outcome were recorded. In univariate and multivariate models, association between dyslipidemia and renal outcome, and dyslipidemia-associated pathological features were analyzed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePatients with dyslipidemia (defined as total cholesterol\u0026thinsp;\u0026ge;\u0026thinsp;5.2mmol/L, triglycerides\u0026thinsp;\u0026ge;\u0026thinsp;1.7mmol/L, or LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;3.4mmol/L) presented elevated complement, and worse clinical characteristics with regard to blood pressure, proteinuria and kidney function, and glomerulosclerosis, tubular atrophy/interstitial fibrosis (T1-2), crescents, and vascular lesions were more common. By multivariate logistic regression, T1-2 and arterial intimal fibrosis were significantly associated with dyslipidemia. After a mean follow-up of 54.7 months, dyslipidemia (P\u0026thinsp;=\u0026thinsp;0.001), especially abnormalities in total cholesterol (P\u0026thinsp;=\u0026thinsp;0.016) and triglycerides (P\u0026thinsp;=\u0026thinsp;0.001), was significantly associated with poorer renal survival, and renal survival was worse after lipid-lowering therapies. In addition to eGFR and arterial intimal fibrosis, dyslipidemia was an independent predictor for renal survival in multivariate Cox analyses (model 1: HR\u0026thinsp;=\u0026thinsp;2.229, 95% CI\u0026thinsp;=\u0026thinsp;1.146\u0026ndash;4.336, P\u0026thinsp;=\u0026thinsp;0.018; model 2: HR\u0026thinsp;=\u0026thinsp;2.117, 95% CI\u0026thinsp;=\u0026thinsp;1.082\u0026ndash;4.145, P\u0026thinsp;=\u0026thinsp;0.029).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIgAN patients with dyslipidemia presented more severe clinicopathological features. Tubular atrophy/interstitial fibrosis and arterio-/arteriolosclerosis were closely associated with dyslipidemia. Dyslipidemia not only indicated adverse renal outcomes, but also was an independent prognostic predictor.\u003c/p\u003e","manuscriptTitle":"Clinicopathological Characteristics and Prognostic Value of Dyslipidemia in IgA Nephropathy: A Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-16 12:09:01","doi":"10.21203/rs.3.rs-6539765/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":"17bbdd23-e820-424a-988a-f5a56297a580","owner":[],"postedDate":"May 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-20T20:23:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-16 12:09:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6539765","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6539765","identity":"rs-6539765","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}