Looking for a new role of known players: the additional value of plasmatic C3 and C4 in predicting IgA Nephropathy prognosis, an observational study

Looking for a new role of known players: the additional value of plasmatic C3 and C4 in predicting IgA Nephropathy prognosis, an observational 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 Article Looking for a new role of known players: the additional value of plasmatic C3 and C4 in predicting IgA Nephropathy prognosis, an observational study Edoardo Tringali, Daniele Vetrano, Francesco Tondolo, Federica Maritati, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4344779/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Introduction IgA Nephropathy (IgAN) is the prevalent glomerular disease worldwide. Complement system activation is crucial in its pathogenesis. Few studies correlated serum C3 and C4 with disease activity and prognosis. Methods This retrospective monocentric study investigated the prognostic value of serum complement in patients with IgAN. Primary outcome was defined as 40% decline in eGFR or onset of kidney failure. The aim was to evaluate whether the addition of serum C3 and C4 to established predictive models, including one based on variables related to chronic kidney disease (CKD) progression and another incorporating variables from the International IgA Prediction Tool (IntIgAPT), enhances the accuracy of outcome prediction. Results 101 patients were stratified according to baseline C3 levels in three groups (Low, Medium and High). During a median 54.28 months follow-up, the Low group exhibited higher primary outcome incidence (16.3 events vs 2.9 and 1.7 events x 100 pts/year, p = 0.0026). Model-1 (M1), consisting of CKD progression variables, and Model-3 (M3), comprising IntIgANPT variables, were implemented with baseline C3 and C4 to form Model-2 (M2) and Model-4 (M4), respectively. M2 demonstrated improved predictive performance over M1 showing higher discrimination (lower AIC and BIC, higher C-index and NR2). Similarly, M4 outperformed M3 showing enhanced outcome prediction when adding C3 and C4. Conclusion Inclusion of serum C3 and C4 can enhance prediction accuracy of already existing prognostic models. Specifically, lower C3 and higher C4 levels were associated with poorer prognosis in IgAN, characterizing a more 'Complement-Pathic' subset of patients. Health sciences/Nephrology Health sciences/Nephrology/Kidney diseases/Glomerular diseases/Iga nephropathy Complement C3 C4 IgA Nephropathy MEST Glomerulonephritis Figures Figure 1 Figure 2 1. Introduction IgA Nephropathy (IgAN) is the most common glomerular disease worldwide, accounting almost for 22 and 11% of diagnoses in Europe and North America, respectively [ 1 ]. Clinical manifestations, disease course and response to immunosuppressive treatment are extremely heterogeneous, with a 10-year risk of kidney failure (KF) varying between 5% and 60% [ 2 , 3 ]. According to the current guidelines [ 4 ], after the biopsy-confirmed diagnosis of IgAN, disease prognosis should be evaluated by the MEST-C system, the histologic score including mesangial hypercellularity (M), endocapillary hypercellularity (E), segmental sclerosis (S), interstitial fibrosis-tubular atrophy (T) and crescents (C) [ 5 ]. The histology findings have been recently incorporated in a prognostic score (International IgAN Prediction Tool [IntIgAPT]) together with clinical and biochemical variables collected at the time of biopsy. This system, validated in more than 4000 patients, may predict the risk of disease progression at 5 years (50% of estimated Glomerular Filtration Rate [eGFR] decline or KF), helping subsequent treatment strategy [ 6 – 8 ]. However, neither this calculator nor the MEST-C score can be used to determine the likely impact of any treatment regime on disease course. Thus, treatment strategy is based on the severity of proteinuria and estimated eGFR. To improve risk stratification of patients is an urgent need in nephrology research and can go alongside with the motivated development of novel reliable serum and urine prognostic biomarkers [ 9 ]. Complement system (CS) activation is crucial in the pathogenesis of IgAN. Both the alternative (AP) and lectin pathways (LP) can be activated, resulting in the production of anaphylatoxins, as well as the formation of the membrane attack complex. This leads to the stimulation of mesangial cells to produce inflammatory mediators and matrix proteins [ 10 , 11 ]. The CS can be activated both locally, as demonstrated by the presence of C3 deposits alongside IgA in over 90% of patients, and systemically, with elevated levels of subproducts of C3 found in affected patients [ 12 ]. So far, the role of CS has been mainly investigated histologically, genetically, and biochemically concerning the byproducts of cascade activation and detectable metabolites in tissues, blood, and urine. To date, only few studies have correlated serum C3 and C4 fractions with disease activity and prognosis [ 13 , 14 ]. Considering the pivotal role of CS in disease initiation and progression, we present results from a longitudinal Caucasian-prevalent cohort. This study assesses the supplementary value of incorporating serum C3 and C4 levels at the time of IgAN diagnosis into established prognostic models for evaluating prognosis. 2. Materials and Methods 2.1 Patients and treatments We retrospectively reviewed clinical records of all patients with a histology-proven diagnosis of IgAN referred at our unit (Sant’Orsola University Hospital, Bologna) from Jan-2009 to Dec-2022. Inclusion criteria were: I) renal biopsy scored according to Oxford MEST-C scoring system; II) availability of anamnestic information, blood pressure (BP) measures, data related to renal function (serum creatinine and eGFR), urinary exams (chemical-physical urine examination, 24-hour proteinuria [uProt]), serum protein profile (serum albumin and total protein), serum complement levels (C3 and C4), and immunoglobulin (Ig) levels of IgA, IgG, and IgM at the time of renal biopsy; III) follow up duration of at least 12 months; IV) at least 18 years at diagnosis. Exclusion criteria were: I) KF at diagnosis, II) over-imposed nephropathy. Treatment was administrated according to current clinical practice and guidelines recommendations. Optimized first-line included management of BP and other cardiovascular risk factors, lifestyle modification and maximally tolerated dose of Renin-Angiotensin-Aldosterone-System inhibitors (RAASi) and Sodium/Glucose Cotransporter-2 inhibitors (SGLT-2i). High-risk patients (i.e. severe proteinuria and/or impaired eGFR) were offered a personalized course of immunosuppression therapy, for example steroids, both systemic (Manno or Pozzi regimens) and local (Budesonide). Subjects underwent regular follow-up in outpatient nephrology clinic (in our center) comprehensive of blood and urine tests and outcome measurement. The study was performed in accordance with the declaration of Helsinki and the protocol was approved by the ethical committee of the Sant’Orsola University Hospital of Bologna (Protocol number 420/2018/Oss/AOUBo). Patients provided informed consent for study participation. 2.2 Data collection At the time of admission for kidney biopsy, a thorough medical and pharmacological history of the patients was conducted. Additionally, BP measurements were taken using a standardized office method. Furthermore, patients underwent both blood and urine laboratory tests. All data were collected in electronic spreadsheet. Diagnosis-subsequent pharmacological treatment regimen, with particular focus on supportive – (i.e., RAASi) and immunosuppressive treatment (e.g., steroids) - were also obtained. After obtaining the diagnosis all patients underwent regular follow-up in outpatient nephrology clinic (in our center) comprehensive of blood and urine tests and outcome measurement. 2.3 Kidney biopsies Kidney biopsies were performed in the suspicion of kidney disease (e.g., urinary abnormalities and/or functional impairment) after written consent of the patient and in accordance with current clinical practice (percutaneous ultrasound-guided approach). Histological samples were processed and evaluated by local nephropathologist under light microscopy, immunofluorescence, and electron microscopy. 2.4 Study aim The main aim of this retrospective observational single-center study is to investigate the additional prognostic value of serum C3 and C4, when added to the already known prognostic variables, on IgAN prognosis. The primary outcome was a kidney outcome defined as 40% decline in eGFR and/or the onset of KF. Specifically, the aim of this study is to assess whether the addition of serum complement to currently used predictive models of chronic kidney disease (CKD) progression can improve the prediction of events. Secondly, we investigated the interaction of serum complement with other baseline clinical features on the primary endpoint. 2.5 Statistical analysis Descriptive statistics were reported as means ± standard deviations (SD) or median and interquartile range (IQR) for continuous variables, according to distribution. Categorical variables were reported as percentages (%). To the aim of the present work, patients were stratified according to baseline serum C3 levels (Low 140 mg/dl). Comparison between groups were tested by means of ANOVA or Kruskal-Wallis test for continuous variables according to distribution. Categorical variables were compared using the Chi-squared test. Linear associations between C3, C4 and other continuous baseline variables involved in IgAN pathophysiology were plotted graphically and tested with Pearson coefficients. Multivariate linear regressions were used to assess correlation between C3 and C4 with eGFR. The slope of the regression line (beta coefficient, [β]) and its corresponding p-value were used to assess the strength and significance of the linear association. Median follow-up was computed by means of inverse Kaplan-Meier approach. Patients were followed until the onset of the primary outcome or until the last follow-up visit in nephrology clinic. Patients lost to follow up were right censored at the time of the last outpatient visit. For the survival analysis we observed sufficient events to compute the incidence rate of outcome. To assess the additional prognostic value of serum complement in comparison with the ‘already used models’ we adopted a logistic regression approach. As ‘already used models’, we selected: a first one used in clinical practice based on classical variables of kidney disease progression namely age, BP, eGFR, proteinuria [ 15 ]; a second one including the variables of the IntIgAPT. For each comparison, we first built a model without C3 and C4 and then added these two variables to build a new model. We computed the measures of goodness of fit: Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), Nagelkerke R 2 test (NR 2 ) that depicts the % explained variation of the outcome based on the set of variables included, and Likelihood Ratio Test (LRT); discrimination with the c-index. A p-value of < 0.05 was considered statistically significant. Statistical analysis was performed using R version 4.0.3 (R Core Team, 2020). 3. Results 3.1 Patients characteristics stratified by C3 groups 101 patients were included in the cohort. 90% were Caucasian and 65.3% were male with a mean age of 42.6 years. As reported in Table 1 , mean eGFR and median proteinuria levels at baseline were 70.4 ml/min/1.73m² and 1.04 g/24h respectively. Mean serum C3 was 114.9 mg/dL. Table 1 Basal characteristics stratified by serum C3. High ≥ 140 N = 14 Medium 90–140 N = 72 Low ≤ 90 N = 15 Overall N = 101 p-value Male gender n ( %) 12 (85.7%) 46 (63.9%) 8 (53.3%) 66 (65.3%) 0.185 Age at biopsy years (± SD) 46.29 (9.45) 41.15 (14.44) 46.13 (13.01) 42.60 (13.73) 0.249 Hypertension n ( %) 12 (85.7%) 37 (51.4%) 11 (73.3%) 60 (59.4%) 0.028 SBP mmHg (± SD) 133.57 (14.99) 122.83 (16.15) 129.00 (14.04) 125.26 (16.06) 0.044 DBP mmHg (± SD) 80.71 (9.17) 76.79 (11.08) 79.33 (8.84) 77.72 (10.54) 0.365 Diabetes n ( %) 3 (21.4%) 3 (4.2%) 1 (6.7%) 7 (6.9%) 0.04 CVD n ( %) 3 (21.4%) 7 (9.7%) 3 (20.0%) 13 (12.9%) 0.324 Smoke n ( %) 0.396 Ex 2 (14.3%) 3 (4.2%) 1 (6.7%) 6 (5.9%) Current 2 (14.3%) 7 (9.7%) 2 (13.3%) 11 (10.9%) eGFR mL/min/1.73m 2 (± SD) 68.64 (23.96) 75.67 (36.17) 47.00 (31.81) 70.44 (35.33) 0.015 uProt g/24h [IQR] 1.81 [1.15, 4.10] 0.75 [0.30, 1.60] 1.46 [0.88, 3.18] 1.04 [0.38, 1.90] 0.004 Tot.protein g/L (± SD) 6.91 (1.23) 6.72 (0.68) 6.41 (0.91) 6.70 (0.78) 0.437 Albumin g/L (± SD) 3.98 (0.84) 3.95 (0.70) 3.59 (0.72) 3.91 (0.72) 0.410 C4 mg/dL (± SD) 40.71 (8.65) 33.56 (9.48) 27.40 (10.64) 33.63 (10.11) 0.001 IgG mg/dL (± SD) 1086.36 (325.43) 985.40 (267.29) 956.20 (363.41) 995.45 (291.51) 0.429 IgA mg/dL [IQR] 263.50 [220.50, 460.25] 302.00 [236.00, 371.00] 273.00 [217.50, 411.00] 294.00 [229.75, 377.75] 0.975 IgM mg/dL (± SD) 86.43 (38.67) 100.49 (43.30) 119.33 (65.64) 101.39 (47.19) 0.166 CD4/CD8 ratio (± SD) 1.74 (0.64) 1.72 (0.60) 1.78 (0.52) 1.73 (0.59) 0.907 Immunosuppression n (%) 12 (85.7%) 49 (68.1%) 11 (73.3%) 72 (71.3%) 0.479 Outcome n ( %) 1 (7.1%) 7 (10.9%) 6 (40.0%) 14 (15.1%) 0.013 Time to outcome months (± SD) 51.53 (18.03) 45.57 (34.04) 29.39 (20.01) 43.86 (30.75) 0.049 Abbreviations: SD, standard deviation; IQR, interquartile range; SBP, systolic blood pressure; DBP, diastolic blood pressure; CVD, cardiovascular disease; eGFR, estimated glomerular filtration rate; uProt, 24h urinary protein. Stratifying by serum C3 levels (Table 1 ), we didn’t find significant differences in gender or age. High and Low groups had a significantly higher prevalence of hypertension (85.7% and 73.3% vs 51.4%, p = 0.028) with a significant higher systolic blood pressure (SBP) (mean 133.6 and 129.0 vs 122.8 mmHg, p = 0.044). There were no differences in the rates of CVD or smoking habit. The High group had a higher percentage of diabetes than Medium and Low (21.4% vs 4.2% and 6.7%, p = 0.039). The Low group had lower values of eGFR at baseline (47.0 vs 75.67 and 68.64 ml/min/1.73m², p = 0.015), while the Medium group had lower values of uProt (0.75 vs 1.81 and 1.46 g/24h, p = 0.004). There were no differences in serum total protein, albumin, IgG, IgA, IgM, or CD4/CD8 lymphocytes ratio. There was a significant difference in serum C4 values, which seemed to decrease, switching from High to Medium to Low (40.71 vs 33.56 vs 27.40 mg/dL, p = 0.001). 3.2 Associations between serum C3 and C4 and baseline variables Baseline serum C3 and C4 levels were positively correlated (r = 0.41, p < 0.001) (Fig. 1 ). There were no associations between C3 and levels of IgA, IgG (Fig. 1 – Supplementary). Instead, C3 exhibited negative correlation with IgM (r = -0.20, p = 0.047). C4 levels were not associated with IgA, IgG, but were negatively correlated with IgM (r= -0.27, p = 0.007). In multivariate linear regression model, both C3 (β = 0.30, p = 0.05) and C4 (β= -0.76, p = 0.045) were associated with baseline eGFR (Table 1 – Supplementary). 3.3 Follow-up and prognosis according to baseline complement values. Median follow-up time was 54.28 months (IQR 39.88, 59.17). The Low group had higher incidence of primary outcome, 16.3 events (95%CI 7.3–36.3) x 100 pts/year as compared with Medium (2.9 events 95%CI 1.4–6.1 x 100 pts/year) and High group (1.7 events 95%CI 0.2–11.8 x 100 pts/year) with a significant difference between rates (p = 0.0026) (Fig. 2 ). Moreover, there were not differences in the rate of immunosuppression regimens. 3.4 Optimizing Prognostic Models for IgAN integrating Serum C3 and C4 As reference models, we used Model-1 (M1), including the main clinical variables of CKD progression (age, gender, SBP, eGFR, uProt) and Model-3 (M3), including variables from the IntIgANpt (age, systolic and diastolic BP, eGFR, uProt, MEST-score). MEST-C parameter was not tested since not included in the tool. Model-2 (M2) and Model-4 (M4) were built by adding baseline C3 and C4 to M1 and M3, respectively. This enables us to explore the supplementary contribution of C3 and C4 to standardized models. In M1 (Table 2 ), lower eGFR (OR 0.95, CI 0.93 to 0.98, p = 0.005) and higher uProt (OR = 1.52, CI 1.11 to 2.10, p = 0.008) were associated with higher risk of outcome, while there was not correlation with sex, age and SBP. In M2, higher uProt and lower eGFR still predicted a worse outcome, and both lower C3 (OR = 0.93, CI 0.88 to 0.98, p = 0.015) and higher C4 (OR 1.11, CI 1.01 to 1.23, p = 0.033) were associated to outcome. When the two models were compared, M2 showed lower values of both AIC (55.1 vs 68.2) and BIC (76.1 vs 83.7), higher discrimination (c-index 0.71 vs 0.59) and higher NR 2 (65% vs 46%). LRT between M1 and M2 was significant (p-value = 0.0040). Table 2 Multivariate logistic regression models: correlation between basal characteristics and outcome with and without serum C3 and C4. Model 1 Model 2 Characteristic OR 95% CI p-value OR 95% CI p-value Age at biopsy (years) 0.99 0.94, 1.05 0.9 1.03 0.96, 1.09 0.3 Sex (M/F) 1.4 0.29, 6.71 0.8 0.92 0.13, 6.22 0.9 SBP (mmHg) 0.98 0.93, 1.02 0.3 0.93 0.86, 1.01 0.06 eGFR (ml/min/1.73m 2 ) 0.95 0.93, 0.98 0.005 0.96 0.93, 0.99 0.05 uProt (g/24h) 1.52 1.11, 2.10 0.008 2.12 1.28, 3.50 0.003 C3 (mg/dL) 0.93 0.88, 0.98 0.015 C4 (mg/dL) 1.11 1.01, 1.23 0.033 AIC 68.2 55.1 BIC 83.7 76.2 C-index 0.59 0.71 < 0.001 NR 2 0.46 0.65 LRT 0.004 Abbreviations: SBP, systolic blood pressure; eGFR, estimated glomerular filtration rate; uProt, urinary protein; OR, Odds Ratio; CI, Confidential Interval; AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion; NR 2 , Nagelkerke R 2 ; LRT, Likelihood Ratio Test In M3 (Table 3 ), variables from the IntIgANpt where used. uProt (OR 2.25, CI 1.23 to 4.12, p = 0.008), SBP (OR 0.90, CI 0.82 to 0.99, p = 0.03) and MEST-M (OR 45.2, CI 1.58 to 129.29, p = 0.02) showed association with outcome. In M4, uProt (OR 2.12, CI 1.28 to 3.50, p = 0.003), age (OR 1.21, CI 1.01 to 1.45, p = 0.04), SBP (OR 0.80, CI 0.65 to 0.99, p = 0.04) and C3 (OR 0.88, CI 0.78 to 0.99, p = 0.05) showed association with outcome. C4 had a positive relation with outcome at borderline of statistical significance (OR 1.21, CI 0.99 to 1.48, p = 0.06). M4 had a lower AIC than M3 (49.1 vs 53.8) and a slightly lower BIC (79.4 vs 79.9), a higher c-index (0.74 vs 0.65) and a higher NR 2 (0.76 vs 0.65). LRT between M3 and M4 was significant (p = 0.0080) testifying an improvement in prediction of M4. Table 3 Multivariate logistic regression models: comparison of IntIgANpt variables with and without C3 and C4 Model 3 Model 4 Characteristic OR 95% CI p-value OR 95% CI p-value Age at biopsy (years) 1.12 0.99, 1.25 0.054 1.21 1.01, 1.45 0.04 SBP (mmHg) 0.90 0.82, 0.99 0.03 0.80 0.65, 0.99 0.04 DBP (mmHg) 1.05 0.91, 1.20 0.4 1.13 0.94, 1.37 0.1 eGFR (ml/min/1.73m 2 ) 0.95 0.90, 1.01 0.07 0.96 0.91, 1.02 0.2 uProt (g/24h) 2.25 1.23, 4.12 0.008 2.12 1.28, 3.50 0.003 MEST-M (0/1) 45.2 1.58, 129.29 0.02 52.0 0.89, 302.9 0.056 MEST-E (0/1) 0.50 0.08, 3.13 0.4 2.28 0.11, 4.53 0.5 MEST-S (0/1) 5.88 0.84, 41.0 0.07 6.04 0.56, 64.9 0.1 MEST-T 1 2 4.71 3.51 0.50, 43.9 0.29, 41.8 0.1 0.31 20.7 2.05 0.20, 206.8 0.10, 39.4 0.1 0.6 C3 (mg/dL) 0.88 0.78, 0.99 0.05 C4 (mg/dL) 1.21 0.99, 1.48 0.06 AIC 53.8 49.1 BIC 79.9 79.4 C-index 0.65 0.74 < 0.001 NR 2 0.65 0.76 LRT 0.008 Abbreviations: SBP, systolic blood pressure; eGFR, estimated glomerular filtration rate; uProt, urinary protein; OR, Odds Ratio; CI, Confidential Interval; AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion; NR 2 , Nagelkerke R 2 ; LRT, Likelihood Ratio Test 4. Discussion IgAN is a lifelong chronic affection which lead to a cumulative risk of poor renal outcome despite currently available treatments, with one-third of patient developing KF within 30 years after diagnosis [ 2 ] and a 19% cumulative incidence recurrence on graft after transplantation at 10 years [ 16 ], eliciting the un-met medical need for specific pathophysiology-oriented therapeutic strategies. Despite being an autoimmune disease, the management of IgAN is mainly focused on non-immunosuppressive treatment, such as lifestyle intervention, BP control, RAASi, SGLT2i and dual endothelin-angiotensin receptor antagonist, reserving a course of steroid therapy in those at high-risk of disease progression (i.e. uProt > 0.75g/die) despite supportive treatment. Enrollment in clinical trials must be always considered in this subgroup [ 4 , 17 – 19 ]. The CS is a part of innate immunity, critical for host defense against infections and tissue clearance from immunocomplexes or injured cells. Alterations leading to aberrant stimulations are responsible for a broad range of immune-mediated kidney diseases. Three main patways of activation have been described: classic pathway (CP), LP and AP [ 20 ]. Complement-driven renal damage and its potential therapeutic targets are becoming increasingly studied. Growing evidence indicate the pathogenetic role of complement activation, as evinced by the finding of its components in mesangial immune complexes; more than 90% of kidney biopsies in IgAN show positive C3 staining in immunofluorescence, whereas C1q is rarely reported, suggesting possible activation of the AP and/or LP, as opposed to the CP. Deposit of complement regulatory proteins such as properdin, factor-B and factor-H was described [ 21 ]. Notwithstanding technical challenges and subjective measurement, the intensity of glomerular C3 staining might predict high risk of disease progression [ 22 – 24 ]. Moreover, a link between tissue deposits and disease activity was also recorded: presence of factor-H related proteins like CFHR5 (indicating AP) was associated with progressive disease, as well as C4d and mannose-binding lectin mesangial staining (indicating LP in the absence of C1q) was correlated with worse outcome. [ 25 – 27 ] Further evidence for a complement-mediated pathogenesis came from genome-wide association studies which highlighted the role of deletion in CFHR1-3 genes (positive regulator of AP) in IgAN susceptibility. [ 28 , 29 ] Endorsing this hypothesis an overactivation of these factors, suggested by elevated serum levels of CFHR1-5, has been linked to disease activity [ 30 , 31 ]. Regardless of the site of production (systemic or local) and the involved pathway (LP or AP) aberrant complement activation by mesangial immunocomplexes precipitate glomerular injury and tissue inflammation through production of anaphylatoxins (C3a, C5a) and membrane attack complex (C5b9). Activation of coagulation cascade further triggers damage [ 20 ]. As stated before, to date there are no validated markers of complement activation for IgAN. Plasmatic C3 and C4 fractions are surrogate markers of complement activation and are widely measured in current nephrological practice, but their link with IgAN prognosis is unclear and is yet to be elucidated. C3 values frequently fluctuate within normal limits, and when decreased (suggesting AP activation) have been correlated with poor kidney outcomes [ 32 – 35 ]. Some authors suggested that subjects with elevated serum IgA/C3 ratio seem more likely to receive a diagnosis of IgAN and to exhibit a worst disease course [ 36 – 42 ]. Similarly, the galactose-deficient-IgA1/C3 ratio at biopsy has been proposed as independent predictor of CKD progression in a wide Chinese cohort [ 43 ]. Less data is available for the interrelation between serum C4 and IgAN prognosis. In 2014 Zhu et al. described the correlation between augmented C4 and worse kidney injury at the time of biopsy, whereas lower C4 were linked to worse outcome at follow-up [ 44 ]. A recent study conducted in 1356 Chinese IgAN patients confirmed that serum C4 correlated positively with uProt and negatively with eGFR at baseline. Furthermore, survival analysis found higher C4 being an independent risk-factor for disease progression. Noteworthy, C4 levels collected at the time of biopsy correlated with scoring of tubulointerstitial injury, glomerulosclerosis and crescents according to MEST-C score [ 13 ]. However, the above-mentioned studies are limited in many ways as either C3 or C4 alone were assessed, biasing the global contribute of each component in determining renal prognosis. The present study aimed to investigate the prognostic value of baseline serum C3 and C4 in a cohort of 101 IgAN patients with a median follow-up of 54 months. Stratification of subjects according to baseline serum C3 levels identified 3 subgroups, named respectively “High” (> 140 mg/dl), “Medium” (90–140 mg/dl) and “Low” (< 90 mg/dl). In contrast to the others, the hypocomplementemic subpopulation showed significant lower eGFR, lower C4 levels and higher proteinuria at baseline, suggesting the link between complement activation and more severe kidney injury at diagnosis. The latter confirmed by a significantly higher incidence rate of primary outcome compared to the Medium and High group, irrespective of immunosuppressant treatment. In order to assess possible relationship between serum complement and other baseline variables linear regression was performed, highlighting a positive correlation between C3 and C4 as previously described by Bi et colleagues, supporting the hypothesis of systemic complement consumption in complement-driven IgAN [ 13 ]. Furthermore, both C3 and C4 levels were found negatively correlated with serum IgM, pointing to a role for IgM that deserves to be further elucidated in future, as recently studied in a cohort of 116 pediatric patients by Xiong et al were mesangial IgM deposition has been found an independent risk factor for poor renal prognosis [ 45 ]. Both C3 and C4 were significantly associated with baseline eGFR, confirming and expanding previous findigs by Bi et al. and Pan et al [ 14 ]. Logistic regression models showed that the addition of C3 and C4 confers a better prediction accuracy as compared to reference models without these variables. Intriguingly, this was particularly true for goodness of fit analysis (LRT) and discrimination (c-index). These results suggest that incorporating complement levels into the baseline assessment of patients could help to discriminate those who are at higher risk of poor renal outcome. Additionally, the NR2 test indicates that serum complement enhances the percentage of events explained by the models, highlighting its inherent informativeness. Regardless of prognostic measures, another main finding of our study is that C3 and C4 levels were significantly associated with outcome despite the presence of robust variables in the models such as proteinuria and eGFR, that normally account for most of the prediction. Our data confirm previous observations from Pan et al that decreased C3 and increased C4 levels are associated with a poor renal prognosis in IgAN, expanding the evidence, for the first time to our knowledge, in a Caucasian-prevalent study population [ 14 ]. For this perspective, our model can be considered a further validation of the previous documented evidence. Compared to the latter our study has a wider follow-up period (54 vs 35 months) and higher risk of events (15% vs 9.8%) despite smaller sample (101 vs 403 patients) and harder endpoint (loss of 40% vs 30% of eGFR). Although complement activation in IgAN is widely described in current literature, the relative contribution of AP and LP is still not clear and needs to be discovered. Determining the dominant pathway driving IgAN progression may be useful to guide tailored therapies. At this moment we can safely conclude that complement activation in IgAN leads to worse outcome, eliciting the need for (new) biomarkers to assess risk of disease progression and potential response to new targeted treatments. Serum complement C3 and C4 factors could be effective ed easy to obtain markers to screen the more “Complement-Pathic” subset of patients. Abbreviations AIC Akaike Information Criterion AP Alternative pathway BIC Bayesian Information Criterion BP Blood pressure CKD Chronic Kidney Disease CS Complement System CP Classical Pathway AP Alternative Pathway LP Lectin Pathway DBP Diastolic blood pressure eGFR estimated Glomerular Filtration Rate Ig Immunoglobulin IgAN IgA Nephropathy IntIgANPT International IgA Nephropathy Prediction Tool IQR Interquartile range KF Kidney Failure LP Lectinic pathway LRT Likelihood Ratio Test NR 2 Nagelkerke R square RAASi Renin–Angiotensin–Aldosterone System inhibitors SBP Systolic blood pressure SD Standard deviations SGLT2i Sodium/Glucose Cotransporter 2 inhibitors uProt 24h–hour urine protein Declarations Disclosures: The authors declare no conflict of interest. Funding: This research received no external funding. Data availability statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy concern. Acknowledgements: None Authors‘ contributions: Study design: ET, DV; Supervision: OB, MP, GLM; Data collection: FT, BF, GP; Statistical analysis: DV, MP; Data interpretation: ET, OB, FM; Draft writing ET, DV. All authors read and approved the final manuscript References O'Shaughnessy MM, Hogan SL, Thompson BD, Coppo R, Fogo AB, Jennette JC. Glomerular disease frequencies by race, sex and region: results from the International Kidney Biopsy Survey. Nephrol Dial Transplant. 2018;33(4):661–669. Lai KN, Tang SC, Schena FP, Novak J, Tomino Y, Fogo AB, Glassock RJ. IgA nephropathy. Nat Rev Dis Primers. 2016;2:16001. Rodrigues JC, Haas M, Reich HN. IgA Nephropathy. Clin J Am Soc Nephrol. 2017;12(4):677–686. Rovin BH, Adler SG, Barratt J, Bridoux F, Burdge KA, Chan TM, Cook HT, Fervenza FC, Gibson KL, Glassock RJ, Jayne DRW, Jha V, Liew A, Liu ZH, Mejía-Vilet JM, Nester CM, Radhakrishnan J, Rave EM, Reich HN, Ronco P, Sanders JF, Sethi S, Suzuki Y, Tang SCW, Tesar V, Vivarelli M, Wetzels JFM, Lytvyn L, Craig JC, Tunnicliffe DJ, Howell M, Tonelli MA, Cheung M, Earley A, Floege J. Executive summary of the KDIGO 2021 Guideline for the Management of Glomerular Diseases. Kidney Int. 2021;100(4):753–779 Bartosik LP, Lajoie G, Sugar L, Cattran DC. Predicting progression in IgA nephropathy. Am J Kidney Dis. 2001;38(4):728–35. Tanaka S, Ninomiya T, Katafuchi R, Masutani K, Tsuchimoto A, Noguchi H, Hirakata H, Tsuruya K, Kitazono T. Development and validation of a prediction rule using the Oxford classification in IgA nephropathy. Clin J Am Soc Nephrol. 2013;8(12):2082–90. Chen T, Li X, Li Y, Xia E, Qin Y, Liang S, Xu F, Liang D, Zeng C, Liu Z. Prediction and Risk Stratification of Kidney Outcomes in IgA Nephropathy. Am J Kidney Dis. 2019;74(3):300–309. Barbour SJ, Coppo R, Zhang H, Liu ZH, Suzuki Y, Matsuzaki K, Katafuchi R, Er L, Espino-Hernandez G, Kim SJ, Reich HN, Feehally J, Cattran DC; International IgA Nephropathy Network. Evaluating a New International Risk-Prediction Tool in IgA Nephropathy. JAMA Intern Med. 2019;179(7):942–952 Provenzano M, Rotundo S, Chiodini P, Gagliardi I, Michael A, Angotti E, Borrelli S, Serra R, Foti D, De Sarro G, Andreucci M. Contribution of Predictive and Prognostic Biomarkers to Clinical Research on Chronic Kidney Disease. Int J Mol Sci. 2020;21(16):5846 Maillard N, Wyatt RJ, Julian BA, Kiryluk K, Gharavi A, Fremeaux-Bacchi V, Novak J. Current Understanding of the Role of Complement in IgA Nephropathy. J Am Soc Nephrol. 2015;26(7):1503–12. Roos A, Rastaldi MP, Calvaresi N, Oortwijn BD, Schlagwein N, van Gijlswijk-Janssen DJ, Stahl GL, Matsushita M, Fujita T, van Kooten C, Daha MR. Glomerular activation of the lectin pathway of complement in IgA nephropathy is associated with more severe renal disease. J Am Soc Nephrol. 2006;17(6):1724–34. Seelen MA, Roos A, Daha MR. Role of complement in innate and autoimmunity. J Nephrol. 2005 Nov-Dec;18(6):642–53. Bi TD, Zheng JN, Zhang JX, Yang LS, Liu N, Yao L, Liu LL. Serum complement C4 is an important prognostic factor for IgA nephropathy: a retrospective study. BMC Nephrol. 2019;20(1):244. Pan M, Zhang J, Li Z, Jin L, Zheng Y, Zhou Z, Zhen S, Lu G. Increased C4 and decreased C3 levels are associated with a poor prognosis in patients with immunoglobulin A nephropathy: a retrospective study. BMC Nephrol. 2017;18(1):231. Tangri N, Grams ME, Levey AS et al.; CKD Prognosis Consortium. Multinational assessment of accuracy of equations for predicting risk of kidney failure: a meta-analysis. JAMA 2016; 315: 164–174 Uffing A, Pérez-Saéz MJ, Jouve T, Bugnazet M, Malvezzi P, Muhsin SA, Lafargue MC, Reindl-Schwaighofer R, Morlock A, Oberbauer R, Buxeda A, Burballa C, Pascual J, von Moos S, Seeger H, La Manna G, Comai G, Bini C, Russo LS, Farouk S, Nissaisorakarn P, Patel H, Agrawal N, Mastroianni-Kirsztajn G, Mansur J, Tedesco-Silva H, Ventura CG, Agena F, David-Neto E, Akalin E, Alani O, Mazzali M, Manfro RC, Bauer AC, Wang AX, Cheng XS, Schold JD, Berger SP, Cravedi P, Riella LV. Recurrence of IgA Nephropathy after Kidney Transplantation in Adults. Clin J Am Soc Nephrol. 2021;16(8):1247–1255. Anders HJ, Peired AJ, Romagnani P. SGLT2 inhibition requires reconsideration of fundamental paradigms in chronic kidney disease, 'diabetic nephropathy', IgA nephropathy and podocytopathies with FSGS lesions. Nephrol Dial Transplant. 2022;37(9):1609–1615. Wheeler DC, Toto RD, Stefánsson BV, Jongs N, Chertow GM, Greene T, Hou FF, McMurray JJV, Pecoits-Filho R, Correa-Rotter R, Rossing P, Sjöström CD, Umanath K, Langkilde AM, Heerspink HJL; DAPA-CKD Trial Committees and Investigators. A pre-specified analysis of the DAPA-CKD trial demonstrates the effects of dapagliflozin on major adverse kidney events in patients with IgA nephropathy. Kidney Int. 2021;100(1):215–224. Heerspink HJL, Radhakrishnan J, Alpers CE, Barratt J, Bieler S, Diva U, Inrig J, Komers R, Mercer A, Noronha IL, Rheault MN, Rote W, Rovin B, Trachtman H, Trimarchi H, Wong MG, Perkovic V; PROTECT Investigators. Sparsentan in patients with IgA nephropathy: a prespecified interim analysis from a randomised, double-blind, active-controlled clinical trial. Lancet. 2023;401(10388):1584–1594. Noris M, Remuzzi G. Overview of complement activation and regulation. Semin Nephrol. 2013;33(6):479–92. Roberts IS. Pathology of IgA nephropathy. Nat Rev Nephrol. 2014;10(8):445–54. Wu D, Li X, Yao X, Zhang N, Lei L, Zhang H, Tang M, Ni J, Ling C, Chen Z, Chen X, Liu X. Mesangial C3 deposition and serum C3 levels predict renal outcome in IgA nephropathy. Clin Exp Nephrol. 2021;25(6):641–651. Xie M, Zhu Y, Wang X, Ren J, Guo H, Huang B, Wang S, Wang P, Liu Y, Liu Y, Zhang J. Predictive prognostic value of glomerular C3 deposition in IgA nephropathy. J Nephrol. 2023;36(2):495–505. Wu J, Hu Z, Wang Y, Hu D, Yang Q, Li Y, Dai W, Zhu F, Yang J, Wang M, Zhu H, Liu L, He X, Han M, Yao Y, Pei G, Zeng R, Xu G. Severe glomerular C3 deposition indicates severe renal lesions and a poor prognosis in patients with immunoglobulin A nephropathy. Histopathology. 2021;78(6):882–895. Medjeral-Thomas NR, Troldborg A, Constantinou N, Lomax-Browne HJ, Hansen AG, Willicombe M, Pusey CD, Cook HT, Thiel S, Pickering MC. Progressive IgA Nephropathy Is Associated With Low Circulating Mannan-Binding Lectin-Associated Serine Protease-3 (MASP-3) and Increased Glomerular Factor H-Related Protein-5 (FHR5) Deposition. Kidney Int Rep. 2017;3(2):426–438. Espinosa M, Ortega R, Sánchez M, Segarra A, Salcedo MT, González F, Camacho R, Valdivia MA, Cabrera R, López K, Pinedo F, Gutierrez E, Valera A, Leon M, Cobo MA, Rodriguez R, Ballarín J, Arce Y, García B, Muñoz MD, Praga M; Spanish Group for Study of Glomerular Diseases (GLOSEN). Association of C4d deposition with clinical outcomes in IgA nephropathy. Clin J Am Soc Nephrol. 2014;9(5):897–904. Segarra A, Romero K, Agraz I, Ramos N, Madrid A, Carnicer C, Jatem E, Vilalta R, Lara LE, Ostos E, Valtierra N, Jaramillo J, Arredondo KV, Ariceta G, Martinez C. Mesangial C4d Deposits in Early IgA Nephropathy. Clin J Am Soc Nephrol. 2018;13(2):258–264. Gharavi AG, Kiryluk K, Choi M, Li Y, Hou P, Xie J, Sanna-Cherchi S, Men CJ, Julian BA, Wyatt RJ, Novak J, He JC, Wang H, Lv J, Zhu L, Wang W, Wang Z, Yasuno K, Gunel M, Mane S, Umlauf S, Tikhonova I, Beerman I, Savoldi S, Magistroni R, Ghiggeri GM, Bodria M, Lugani F, Ravani P, Ponticelli C, Allegri L, Boscutti G, Frasca G, Amore A, Peruzzi L, Coppo R, Izzi C, Viola BF, Prati E, Salvadori M, Mignani R, Gesualdo L, Bertinetto F, Mesiano P, Amoroso A, Scolari F, Chen N, Zhang H, Lifton RP. Genome-wide association study identifies susceptibility loci for IgA nephropathy. Nat Genet. 2011;43(4):321–7. Kiryluk K, Li Y, Sanna-Cherchi S, Rohanizadegan M, Suzuki H, Eitner F, Snyder HJ, Choi M, Hou P, Scolari F, Izzi C, Gigante M, Gesualdo L, Savoldi S, Amoroso A, Cusi D, Zamboli P, Julian BA, Novak J, Wyatt RJ, Mucha K, Perola M, Kristiansson K, Viktorin A, Magnusson PK, Thorleifsson G, Thorsteinsdottir U, Stefansson K, Boland A, Metzger M, Thibaudin L, Wanner C, Jager KJ, Goto S, Maixnerova D, Karnib HH, Nagy J, Panzer U, Xie J, Chen N, Tesar V, Narita I, Berthoux F, Floege J, Stengel B, Zhang H, Lifton RP, Gharavi AG. Geographic differences in genetic susceptibility to IgA nephropathy: GWAS replication study and geospatial risk analysis. PLoS Genet. 2012;8(6):e1002765. Medjeral-Thomas NR, Lomax-Browne HJ, Beckwith H, Willicombe M, McLean AG, Brookes P, Pusey CD, Falchi M, Cook HT, Pickering MC. Circulating complement factor H-related proteins 1 and 5 correlate with disease activity in IgA nephropathy. Kidney Int. 2017;92(4):942–952. Tortajada A, Gutiérrez E, Goicoechea de Jorge E, Anter J, Segarra A, Espinosa M, Blasco M, Roman E, Marco H, Quintana LF, Gutiérrez J, Pinto S, Lopez-Trascasa M, Praga M, Rodriguez de Córdoba S. Elevated factor H-related protein 1 and factor H pathogenic variants decrease complement regulation in IgA nephropathy. Kidney Int. 2017;92(4):953–963. Le Stang MB, Gleeson PJ, Daha MR, Monteiro RC, van Kooten C. Is complement the main accomplice in IgA nephropathy? From initial observations to potential complement-targeted therapies. Mol Immunol. 2021;140:1–11. Kim SJ, Koo HM, Lim BJ, Oh HJ, Yoo DE, Shin DH, Lee MJ, Doh FM, Park JT, Yoo TH, Kang SW, Choi KH, Jeong HJ, Han SH. Decreased circulating C3 levels and mesangial C3 deposition predict renal outcome in patients with IgA nephropathy. PLoS One. 2012;7(7):e40495. Wyatt RJ, Kanayama Y, Julian BA, Negoro N, Sugimoto S, Hudson EC, Curd JG. Complement activation in IgA nephropathy. Kidney Int. 1987;31(4):1019–23. Zwirner J, Burg M, Schulze M, Brunkhorst R, Götze O, Koch KM, Floege J. Activated complement C3: a potentially novel predictor of progressive IgA nephropathy. Kidney Int. 1997;51(4):1257–64. Tomino Y, Suzuki S, Imai H, Saito T, Kawamura T, Yorioka N, Harada T, Yasumoto Y, Kida H, Kobayashi Y, Endoh M, Sato H, Saito K. Measurement of serum IgA and C3 may predict the diagnosis of patients with IgA nephropathy prior to renal biopsy. J Clin Lab Anal. 2000;14(5):220–3. Maeda A, Gohda T, Funabiki K, Horikoshi S, Shirato I, Tomino Y. Significance of serum IgA levels and serum IgA/C3 ratio in diagnostic analysis of patients with IgA nephropathy. J Clin Lab Anal. 2003;17(3):73–6. Yanagawa H, Suzuki H, Suzuki Y, Kiryluk K, Gharavi AG, Matsuoka K, Makita Y, Julian BA, Novak J, Tomino Y. A panel of serum biomarkers differentiates IgA nephropathy from other renal diseases. PLoS One. 2014;9(5):e98081. Gong WY, Liu M, Luo D, Liu FN, Yin LH, Li YQ, Zhang J, Peng H. High serum IgA/C3 ratio better predicts a diagnosis of IgA nephropathy among primary glomerular nephropathy patients with proteinuria ≤ 1 g/d: an observational cross-sectional study. BMC Nephrol. 2019;20(1):150. Zhang J, Wang C, Tang Y, Peng H, Ye ZC, Li CC, Lou TQ. Serum immunoglobulin A/C3 ratio predicts progression of immunoglobulin A nephropathy. Nephrology (Carlton). 2013;18(2):125–31. Komatsu H, Fujimoto S, Hara S, Sato Y, Yamada K, Eto T. Relationship between serum IgA/C3 ratio and progression of IgA nephropathy. Intern Med. 2004;43(11):1023–8. Stefan G, Stancu S, Boitan B, Zugravu A, Petre N, Mircescu G. Is There a Role for IgA/C3 Ratio in IgA Nephropathy Prognosis? An Outcome Analysis on An European Population. Iran J Kidney Dis. 2020;14(6):470–477. Chen P, Yu G, Zhang X, Xie X, Wang J, Shi S, Liu L, Lv J, Zhang H. Plasma Galactose-Deficient IgA1 and C3 and CKD Progression in IgA Nephropathy. Clin J Am Soc Nephrol. 2019;14(10):1458–1465. Zhu B, Zhu CF, Lin Y, Perkovic V, Li XF, Yang R, Tang XL, Zhu XL, Cheng XX, Li Q, Chen HY, Sun Y, Chen QW, Wang YJ. Clinical characteristics of IgA nephropathy associated with low complement 4 levels. Ren Fail. 2015;37(3):424–32. Xiong L, Liu L, Tao Y, Guo H. Clinical significance of IgM and C3 deposition in children with primary immunoglobulin A nephropathy. J Nephrol. 2023 Aug 5. doi: 10.1007/s40620-023-01724-7 . Additional Declarations No competing interests reported. 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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-4344779","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":299792838,"identity":"e5440e4f-38be-4efa-97d2-c4e653f83616","order_by":0,"name":"Edoardo Tringali","email":"","orcid":"","institution":"IRCCS Azienda Ospedaliero-Universitaria di Bologna","correspondingAuthor":false,"prefix":"","firstName":"Edoardo","middleName":"","lastName":"Tringali","suffix":""},{"id":299792840,"identity":"2f53a8fd-e977-4045-986f-2043b17f213c","order_by":1,"name":"Daniele Vetrano","email":"","orcid":"","institution":"IRCCS Azienda Ospedaliero-Universitaria di Bologna","correspondingAuthor":false,"prefix":"","firstName":"Daniele","middleName":"","lastName":"Vetrano","suffix":""},{"id":299792842,"identity":"737afce0-a2ad-4505-b315-024bd005520b","order_by":2,"name":"Francesco Tondolo","email":"","orcid":"","institution":"IRCCS Azienda Ospedaliero-Universitaria di Bologna","correspondingAuthor":false,"prefix":"","firstName":"Francesco","middleName":"","lastName":"Tondolo","suffix":""},{"id":299792844,"identity":"3562c3f5-f85e-45df-9b58-cc33c1fa4738","order_by":3,"name":"Federica Maritati","email":"","orcid":"","institution":"IRCCS Azienda Ospedaliero-Universitaria di Bologna","correspondingAuthor":false,"prefix":"","firstName":"Federica","middleName":"","lastName":"Maritati","suffix":""},{"id":299792846,"identity":"40e0edcd-ba88-4ac1-823b-8cbda2e2fd18","order_by":4,"name":"Benedetta Fabbrizio","email":"","orcid":"","institution":"IRCCS Azienda Ospedaliero-Universitaria di Bologna","correspondingAuthor":false,"prefix":"","firstName":"Benedetta","middleName":"","lastName":"Fabbrizio","suffix":""},{"id":299792848,"identity":"527e17e3-0331-4a38-90c5-89f4601a8e8f","order_by":5,"name":"Gianandrea Pasquinelli","email":"","orcid":"","institution":"University of Bologna","correspondingAuthor":false,"prefix":"","firstName":"Gianandrea","middleName":"","lastName":"Pasquinelli","suffix":""},{"id":299792850,"identity":"e340c9d1-7d7b-47a7-8ac1-1fef2e890f74","order_by":6,"name":"Michele Provenzano","email":"","orcid":"","institution":"IRCCS Azienda Ospedaliero-Universitaria di Bologna","correspondingAuthor":false,"prefix":"","firstName":"Michele","middleName":"","lastName":"Provenzano","suffix":""},{"id":299792852,"identity":"c1665008-dc48-4169-aaaf-765028adee29","order_by":7,"name":"Gaetano La Manna","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYDADPigtx8DA2AakJfApZmwAkWxQnjFMCz49qFoSG6BsnFrk3Q8ff/Bzhw0DG/vpxM+FO+zSN9xubnvwgcGiDpcWwzNpiY29Z9IY2HhyN0vPPJOcu+HOwXbDGXgcZtiQY9jA23YY6JjcDdK8bcy5G24ktknz4NPS//5j41+QFv63m3/zttWnGxDSIi+Rw9gMtkUidxvQlsMJBLUYSDwznC3blsbDJvF2mzVv23HDmTcSgX4xkJBswGVLf/KDj2/bbOT4+XM33+Ztq5bnu5H+7MGHijp+nLYcgNA86OK4NABtwWX9KBgFo2AUjAI4AACFVVIeIUnCwgAAAABJRU5ErkJggg==","orcid":"","institution":"IRCCS Azienda Ospedaliero-Universitaria di Bologna","correspondingAuthor":true,"prefix":"","firstName":"Gaetano","middleName":"La","lastName":"Manna","suffix":""},{"id":299792854,"identity":"d6966133-77bc-46bb-b02a-343d86af061c","order_by":8,"name":"Olga Baraldi","email":"","orcid":"","institution":"IRCCS Azienda Ospedaliero-Universitaria di Bologna","correspondingAuthor":false,"prefix":"","firstName":"Olga","middleName":"","lastName":"Baraldi","suffix":""}],"badges":[],"createdAt":"2024-04-29 18:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4344779/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4344779/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56198629,"identity":"7befaaee-d105-4a39-afa1-9578b7e1c7bd","added_by":"auto","created_at":"2024-05-09 18:37:46","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":304772,"visible":true,"origin":"","legend":"\u003cp\u003eResults from the correlation analysis showing the relationship between baseline variables. In particular, panel A shows the positive relation between C3 a C4. Panel B shows the negative relation between C3 and IgM and panel C shows the negative relation between C4 and IgM\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4344779/v1/592ec8409f0b39ab20174e1f.jpg"},{"id":56198269,"identity":"fd3aa8ad-7af9-40ee-b35f-9462791ce67c","added_by":"auto","created_at":"2024-05-09 18:29:46","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":192423,"visible":true,"origin":"","legend":"\u003cp\u003eIncidence of outcome expressed as incidence rate ratio (100 patients[pts]/year) stratified by baseline C3. Low group had a higher incidence, 16.3 events (95%CI 7.3-36.3) compared with Medium (2.9 events 95%CI 1.4-6.1) and High group (1.7 events 95%CI 0.2-11.8)\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4344779/v1/ae2b6d31a60a5b0b2dc23838.jpg"},{"id":56197156,"identity":"0205c6b8-25fd-43c3-b013-85fd34f8ab0f","added_by":"auto","created_at":"2024-05-09 18:21:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":64051,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4344779/v1/11cef65fd39c1ff5cd3fa2e7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Looking for a new role of known players: the additional value of plasmatic C3 and C4 in predicting IgA Nephropathy prognosis, an observational study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIgA Nephropathy (IgAN) is the most common glomerular disease worldwide, accounting almost for 22 and 11% of diagnoses in Europe and North America, respectively [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eClinical manifestations, disease course and response to immunosuppressive treatment are extremely heterogeneous, with a 10-year risk of kidney failure (KF) varying between 5% and 60% [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to the current guidelines [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], after the biopsy-confirmed diagnosis of IgAN, disease prognosis should be evaluated by the MEST-C system, the histologic score including mesangial hypercellularity (M), endocapillary hypercellularity (E), segmental sclerosis (S), interstitial fibrosis-tubular atrophy (T) and crescents (C) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The histology findings have been recently incorporated in a prognostic score (International IgAN Prediction Tool [IntIgAPT]) together with clinical and biochemical variables collected at the time of biopsy. This system, validated in more than 4000 patients, may predict the risk of disease progression at 5 years (50% of estimated Glomerular Filtration Rate [eGFR] decline or KF), helping subsequent treatment strategy [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, neither this calculator nor the MEST-C score can be used to determine the likely impact of any treatment regime on disease course. Thus, treatment strategy is based on the severity of proteinuria and estimated eGFR.\u003c/p\u003e \u003cp\u003eTo improve risk stratification of patients is an urgent need in nephrology research and can go alongside with the motivated development of novel reliable serum and urine prognostic biomarkers [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eComplement system (CS) activation is crucial in the pathogenesis of IgAN. Both the alternative (AP) and lectin pathways (LP) can be activated, resulting in the production of anaphylatoxins, as well as the formation of the membrane attack complex. This leads to the stimulation of mesangial cells to produce inflammatory mediators and matrix proteins [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The CS can be activated both locally, as demonstrated by the presence of C3 deposits alongside IgA in over 90% of patients, and systemically, with elevated levels of subproducts of C3 found in affected patients [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. So far, the role of CS has been mainly investigated histologically, genetically, and biochemically concerning the byproducts of cascade activation and detectable metabolites in tissues, blood, and urine. To date, only few studies have correlated serum C3 and C4 fractions with disease activity and prognosis [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConsidering the pivotal role of CS in disease initiation and progression, we present results from a longitudinal Caucasian-prevalent cohort. This study assesses the supplementary value of incorporating serum C3 and C4 levels at the time of IgAN diagnosis into established prognostic models for evaluating prognosis.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Patients and treatments\u003c/h2\u003e \u003cp\u003eWe retrospectively reviewed clinical records of all patients with a histology-proven diagnosis of IgAN referred at our unit (Sant\u0026rsquo;Orsola University Hospital, Bologna) from Jan-2009 to Dec-2022.\u003c/p\u003e \u003cp\u003eInclusion criteria were: I) renal biopsy scored according to Oxford MEST-C scoring system; II) availability of anamnestic information, blood pressure (BP) measures, data related to renal function (serum creatinine and eGFR), urinary exams (chemical-physical urine examination, 24-hour proteinuria [uProt]), serum protein profile (serum albumin and total protein), serum complement levels (C3 and C4), and immunoglobulin (Ig) levels of IgA, IgG, and IgM at the time of renal biopsy; III) follow up duration of at least 12 months; IV) at least 18 years at diagnosis. Exclusion criteria were: I) KF at diagnosis, II) over-imposed nephropathy.\u003c/p\u003e \u003cp\u003e Treatment was administrated according to current clinical practice and guidelines recommendations. Optimized first-line included management of BP and other cardiovascular risk factors, lifestyle modification and maximally tolerated dose of Renin-Angiotensin-Aldosterone-System inhibitors (RAASi) and Sodium/Glucose Cotransporter-2 inhibitors (SGLT-2i). High-risk patients (i.e. severe proteinuria and/or impaired eGFR) were offered a personalized course of immunosuppression therapy, for example steroids, both systemic (Manno or Pozzi regimens) and local (Budesonide).\u003c/p\u003e \u003cp\u003eSubjects underwent regular follow-up in outpatient nephrology clinic (in our center) comprehensive of blood and urine tests and outcome measurement.\u003c/p\u003e \u003cp\u003e The study was performed in accordance with the declaration of Helsinki and the protocol was approved by the ethical committee of the Sant\u0026rsquo;Orsola University Hospital of Bologna (Protocol number 420/2018/Oss/AOUBo). Patients provided informed consent for study participation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data collection\u003c/h2\u003e \u003cp\u003eAt the time of admission for kidney biopsy, a thorough medical and pharmacological history of the patients was conducted. Additionally, BP measurements were taken using a standardized office method. Furthermore, patients underwent both blood and urine laboratory tests. All data were collected in electronic spreadsheet. Diagnosis-subsequent pharmacological treatment regimen, with particular focus on supportive \u0026ndash; (i.e., RAASi) and immunosuppressive treatment (e.g., steroids) - were also obtained. After obtaining the diagnosis all patients underwent regular follow-up in outpatient nephrology clinic (in our center) comprehensive of blood and urine tests and outcome measurement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Kidney biopsies\u003c/h2\u003e \u003cp\u003eKidney biopsies were performed in the suspicion of kidney disease (e.g., urinary abnormalities and/or functional impairment) after written consent of the patient and in accordance with current clinical practice (percutaneous ultrasound-guided approach). Histological samples were processed and evaluated by local nephropathologist under light microscopy, immunofluorescence, and electron microscopy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Study aim\u003c/h2\u003e \u003cp\u003eThe main aim of this retrospective observational single-center study is to investigate the additional prognostic value of serum C3 and C4, when added to the already known prognostic variables, on IgAN prognosis. The primary outcome was a kidney outcome defined as 40% decline in eGFR and/or the onset of KF. Specifically, the aim of this study is to assess whether the addition of serum complement to currently used predictive models of chronic kidney disease (CKD) progression can improve the prediction of events.\u003c/p\u003e \u003cp\u003eSecondly, we investigated the interaction of serum complement with other baseline clinical features on the primary endpoint.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were reported as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (SD) or median and interquartile range (IQR) for continuous variables, according to distribution. Categorical variables were reported as percentages (%). To the aim of the present work, patients were stratified according to baseline serum C3 levels (Low\u0026thinsp;\u0026lt;\u0026thinsp;90 mg/dl, Medium 90\u0026ndash;140 mg/dl, High\u0026thinsp;\u0026gt;\u0026thinsp;140 mg/dl). Comparison between groups were tested by means of ANOVA or Kruskal-Wallis test for continuous variables according to distribution. Categorical variables were compared using the Chi-squared test. Linear associations between C3, C4 and other continuous baseline variables involved in IgAN pathophysiology were plotted graphically and tested with Pearson coefficients. Multivariate linear regressions were used to assess correlation between C3 and C4 with eGFR. The slope of the regression line (beta coefficient, [β]) and its corresponding p-value were used to assess the strength and significance of the linear association. Median follow-up was computed by means of inverse Kaplan-Meier approach. Patients were followed until the onset of the primary outcome or until the last follow-up visit in nephrology clinic. Patients lost to follow up were right censored at the time of the last outpatient visit. For the survival analysis we observed sufficient events to compute the incidence rate of outcome. To assess the additional prognostic value of serum complement in comparison with the \u0026lsquo;already used models\u0026rsquo; we adopted a logistic regression approach.\u003c/p\u003e \u003cp\u003eAs \u0026lsquo;already used models\u0026rsquo;, we selected: a first one used in clinical practice based on classical variables of kidney disease progression namely age, BP, eGFR, proteinuria [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]; a second one including the variables of the IntIgAPT. For each comparison, we first built a model without C3 and C4 and then added these two variables to build a new model. We computed the measures of goodness of fit: Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), Nagelkerke R\u003csup\u003e2\u003c/sup\u003e test (NR\u003csup\u003e2\u003c/sup\u003e) that depicts the % explained variation of the outcome based on the set of variables included, and Likelihood Ratio Test (LRT); discrimination with the c-index.\u003c/p\u003e \u003cp\u003eA p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003eStatistical analysis was performed using R version 4.0.3 (R Core Team, 2020).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Patients characteristics stratified by C3 groups\u003c/h2\u003e \u003cp\u003e101 patients were included in the cohort. 90% were Caucasian and 65.3% were male with a mean age of 42.6 years. As reported in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, mean eGFR and median proteinuria levels at baseline were 70.4 ml/min/1.73m\u0026sup2; and 1.04 g/24h respectively. Mean serum C3 was 114.9 mg/dL.\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\u003eBasal characteristics stratified by serum C3.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\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\u003eHigh\u003c/p\u003e \u003cp\u003e\u0026ge;\u0026thinsp;140\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;14\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003cp\u003e90\u0026ndash;140\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;72\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003cp\u003e\u0026le;\u0026thinsp;90\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;15\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;101\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale gender\u003c/b\u003e \u003cem\u003en\u003c/em\u003e (\u003cem\u003e%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12 (85.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46 (63.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8 (53.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e66 (65.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge at biopsy\u003c/b\u003e \u003cem\u003eyears (\u0026plusmn;\u0026thinsp;SD)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46.29 (9.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.15 (14.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46.13 (13.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42.60 (13.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e \u003cem\u003en\u003c/em\u003e (\u003cem\u003e%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12 (85.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37 (51.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11 (73.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60 (59.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSBP\u003c/b\u003e \u003cem\u003emmHg (\u0026plusmn;\u0026thinsp;SD)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e133.57 (14.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e122.83 (16.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e129.00 (14.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e125.26 (16.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.044\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDBP\u003c/b\u003e \u003cem\u003emmHg (\u0026plusmn;\u0026thinsp;SD)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80.71 (9.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76.79 (11.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e79.33 (8.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e77.72 (10.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.365\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e \u003cem\u003en\u003c/em\u003e (\u003cem\u003e%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (21.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7 (6.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCVD\u003c/b\u003e \u003cem\u003en\u003c/em\u003e (\u003cem\u003e%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (21.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7 (9.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3 (20.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13 (12.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoke\u003c/b\u003e \u003cem\u003en\u003c/em\u003e (\u003cem\u003e%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6 (5.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7 (9.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11 (10.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eeGFR\u003c/b\u003e \u003cem\u003emL/min/1.73m\u003c/em\u003e \u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e(\u0026plusmn;\u0026thinsp;SD)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68.64 (23.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.67 (36.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.00 (31.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70.44 (35.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003euProt\u003c/b\u003e \u003cem\u003eg/24h [IQR]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.81 [1.15, 4.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.75 [0.30, 1.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.46 [0.88, 3.18]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.04 [0.38, 1.90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTot.protein\u003c/b\u003e \u003cem\u003eg/L (\u0026plusmn;\u0026thinsp;SD)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.91 (1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.72 (0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.41 (0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.70 (0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlbumin\u003c/b\u003e \u003cem\u003eg/L (\u0026plusmn;\u0026thinsp;SD)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.98 (0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.95 (0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.59 (0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.91 (0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.410\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC4\u003c/b\u003e \u003cem\u003emg/dL (\u0026plusmn;\u0026thinsp;SD)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40.71 (8.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.56 (9.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.40 (10.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33.63 (10.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIgG\u003c/b\u003e \u003cem\u003emg/dL (\u0026plusmn;\u0026thinsp;SD)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1086.36 (325.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e985.40 (267.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e956.20 (363.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e995.45 (291.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.429\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIgA\u003c/b\u003e \u003cem\u003emg/dL [IQR]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e263.50\u003c/p\u003e \u003cp\u003e[220.50, 460.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e302.00\u003c/p\u003e \u003cp\u003e[236.00, 371.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e273.00\u003c/p\u003e \u003cp\u003e[217.50, 411.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e294.00\u003c/p\u003e \u003cp\u003e[229.75, 377.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.975\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIgM\u003c/b\u003e \u003cem\u003emg/dL (\u0026plusmn;\u0026thinsp;SD)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86.43 (38.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100.49 (43.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e119.33 (65.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e101.39 (47.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCD4/CD8\u003c/b\u003e \u003cem\u003eratio (\u0026plusmn;\u0026thinsp;SD)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.74 (0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.72 (0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.78 (0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.73 (0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.907\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eImmunosuppression\u003c/b\u003e \u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12 (85.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49 (68.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11 (73.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e72 (71.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOutcome\u003c/b\u003e \u003cem\u003en\u003c/em\u003e (\u003cem\u003e%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7 (10.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6 (40.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14 (15.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTime to outcome\u003c/b\u003e \u003cem\u003emonths (\u0026plusmn;\u0026thinsp;SD)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51.53 (18.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.57 (34.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.39 (20.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43.86 (30.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.049\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: SD, standard deviation; IQR, interquartile range; SBP, systolic blood pressure; DBP, diastolic blood pressure; CVD, cardiovascular disease; eGFR, estimated glomerular filtration rate; uProt, 24h urinary protein.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eStratifying by serum C3 levels (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), we didn\u0026rsquo;t find significant differences in gender or age. High and Low groups had a significantly higher prevalence of hypertension (85.7% and 73.3% vs 51.4%, p\u0026thinsp;=\u0026thinsp;0.028) with a significant higher systolic blood pressure (SBP) (mean 133.6 and 129.0 vs 122.8 mmHg, p\u0026thinsp;=\u0026thinsp;0.044). There were no differences in the rates of CVD or smoking habit. The High group had a higher percentage of diabetes than Medium and Low (21.4% vs 4.2% and 6.7%, p\u0026thinsp;=\u0026thinsp;0.039). The Low group had lower values of eGFR at baseline (47.0 vs 75.67 and 68.64 ml/min/1.73m\u0026sup2;, p\u0026thinsp;=\u0026thinsp;0.015), while the Medium group had lower values of uProt (0.75 vs 1.81 and 1.46 g/24h, p\u0026thinsp;=\u0026thinsp;0.004). There were no differences in serum total protein, albumin, IgG, IgA, IgM, or CD4/CD8 lymphocytes ratio. There was a significant difference in serum C4 values, which seemed to decrease, switching from High to Medium to Low (40.71 vs 33.56 vs 27.40 mg/dL, p\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Associations between serum C3 and C4 and baseline variables\u003c/h2\u003e \u003cp\u003eBaseline serum C3 and C4 levels were positively correlated (r\u0026thinsp;=\u0026thinsp;0.41, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). There were no associations between C3 and levels of IgA, IgG (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u0026ndash; Supplementary). Instead, C3 exhibited negative correlation with IgM (r = -0.20, p\u0026thinsp;=\u0026thinsp;0.047). C4 levels were not associated with IgA, IgG, but were negatively correlated with IgM (r= -0.27, p\u0026thinsp;=\u0026thinsp;0.007). In multivariate linear regression model, both C3 (β\u0026thinsp;=\u0026thinsp;0.30, p\u0026thinsp;=\u0026thinsp;0.05) and C4 (β= -0.76, p\u0026thinsp;=\u0026thinsp;0.045) were associated with baseline eGFR (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u0026ndash; Supplementary).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Follow-up and prognosis according to baseline complement values.\u003c/h2\u003e \u003cp\u003eMedian follow-up time was 54.28 months (IQR 39.88, 59.17). The Low group had higher incidence of primary outcome, 16.3 events (95%CI 7.3\u0026ndash;36.3) x 100 pts/year as compared with Medium (2.9 events 95%CI 1.4\u0026ndash;6.1 x 100 pts/year) and High group (1.7 events 95%CI 0.2\u0026ndash;11.8 x 100 pts/year) with a significant difference between rates (p\u0026thinsp;=\u0026thinsp;0.0026) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Moreover, there were not differences in the rate of immunosuppression regimens.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Optimizing Prognostic Models for IgAN integrating Serum C3 and C4\u003c/h2\u003e \u003cp\u003eAs reference models, we used Model-1 (M1), including the main clinical variables of CKD progression (age, gender, SBP, eGFR, uProt) and Model-3 (M3), including variables from the IntIgANpt (age, systolic and diastolic BP, eGFR, uProt, MEST-score). MEST-C parameter was not tested since not included in the tool. Model-2 (M2) and Model-4 (M4) were built by adding baseline C3 and C4 to M1 and M3, respectively. This enables us to explore the supplementary contribution of C3 and C4 to standardized models.\u003c/p\u003e \u003cp\u003eIn M1 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), lower eGFR (OR 0.95, CI 0.93 to 0.98, p\u0026thinsp;=\u0026thinsp;0.005) and higher uProt (OR\u0026thinsp;=\u0026thinsp;1.52, CI 1.11 to 2.10, p\u0026thinsp;=\u0026thinsp;0.008) were associated with higher risk of outcome, while there was not correlation with sex, age and SBP. In M2, higher uProt and lower eGFR still predicted a worse outcome, and both lower C3 (OR\u0026thinsp;=\u0026thinsp;0.93, CI 0.88 to 0.98, p\u0026thinsp;=\u0026thinsp;0.015) and higher C4 (OR 1.11, CI 1.01 to 1.23, p\u0026thinsp;=\u0026thinsp;0.033) were associated to outcome. When the two models were compared, M2 showed lower values of both AIC (55.1 vs 68.2) and BIC (76.1 vs 83.7), higher discrimination (c-index 0.71 vs 0.59) and higher NR\u003csup\u003e2\u003c/sup\u003e (65% vs 46%). LRT between M1 and M2 was significant (p-value\u0026thinsp;=\u0026thinsp;0.0040).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate logistic regression models: correlation between basal characteristics and outcome with and without serum C3 and C4.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge at biopsy\u003c/b\u003e \u003cem\u003e(years)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94, 1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.96, 1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e \u003cem\u003e(M/F)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.29, 6.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.13, 6.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSBP\u003c/b\u003e \u003cem\u003e(mmHg)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93, 1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.86, 1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eeGFR\u003c/b\u003e \u003cem\u003e(ml/min/1.73m\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93, 0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.93, 0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003euProt\u003c/b\u003e \u003cem\u003e(g/24h)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11, 2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.28, 3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC3\u003c/b\u003e \u003cem\u003e(mg/dL)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.88, 0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC4\u003c/b\u003e \u003cem\u003e(mg/dL)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.01, 1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.033\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAIC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBIC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC-index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNR\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLRT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eAbbreviations: SBP, systolic blood pressure; eGFR, estimated glomerular filtration rate; uProt, urinary protein; OR, Odds Ratio; CI, Confidential Interval; AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion; NR\u003csup\u003e2\u003c/sup\u003e, Nagelkerke R\u003csup\u003e2\u003c/sup\u003e; LRT, Likelihood Ratio Test\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn M3 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), variables from the IntIgANpt where used. uProt (OR 2.25, CI 1.23 to 4.12, p\u0026thinsp;=\u0026thinsp;0.008), SBP (OR 0.90, CI 0.82 to 0.99, p\u0026thinsp;=\u0026thinsp;0.03) and MEST-M (OR 45.2, CI 1.58 to 129.29, p\u0026thinsp;=\u0026thinsp;0.02) showed association with outcome. In M4, uProt (OR 2.12, CI 1.28 to 3.50, p\u0026thinsp;=\u0026thinsp;0.003), age (OR 1.21, CI 1.01 to 1.45, p\u0026thinsp;=\u0026thinsp;0.04), SBP (OR 0.80, CI 0.65 to 0.99, p\u0026thinsp;=\u0026thinsp;0.04) and C3 (OR 0.88, CI 0.78 to 0.99, p\u0026thinsp;=\u0026thinsp;0.05) showed association with outcome. C4 had a positive relation with outcome at borderline of statistical significance (OR 1.21, CI 0.99 to 1.48, p\u0026thinsp;=\u0026thinsp;0.06). M4 had a lower AIC than M3 (49.1 vs 53.8) and a slightly lower BIC (79.4 vs 79.9), a higher c-index (0.74 vs 0.65) and a higher NR\u003csup\u003e2\u003c/sup\u003e (0.76 vs 0.65). LRT between M3 and M4 was significant (p\u0026thinsp;=\u0026thinsp;0.0080) testifying an improvement in prediction of M4.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate logistic regression models: comparison of IntIgANpt variables with and without C3 and C4\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge at biopsy\u003c/b\u003e \u003cem\u003e(years)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99, 1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.01, 1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSBP\u003c/b\u003e \u003cem\u003e(mmHg)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.82, 0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.65, 0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDBP\u003c/b\u003e \u003cem\u003e(mmHg)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91, 1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.94, 1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eeGFR\u003c/b\u003e \u003cem\u003e(ml/min/1.73m\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.90, 1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.91, 1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003euProt\u003c/b\u003e \u003cem\u003e(g/24h)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23, 4.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.28, 3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMEST-M\u003c/b\u003e \u003cem\u003e(0/1)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.58, 129.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.89, 302.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMEST-E\u003c/b\u003e \u003cem\u003e(0/1)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08, 3.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.11, 4.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMEST-S\u003c/b\u003e \u003cem\u003e(0/1)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.84, 41.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.56, 64.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMEST-T\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.71\u003c/p\u003e \u003cp\u003e3.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.50, 43.9\u003c/p\u003e \u003cp\u003e0.29, 41.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.7\u003c/p\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.20, 206.8\u003c/p\u003e \u003cp\u003e0.10, 39.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC3\u003c/b\u003e \u003cem\u003e(mg/dL)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.78, 0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC4\u003c/b\u003e \u003cem\u003e(mg/dL)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.99, 1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAIC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBIC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC-index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNR\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLRT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eAbbreviations: SBP, systolic blood pressure; eGFR, estimated glomerular filtration rate; uProt, urinary protein; OR, Odds Ratio; CI, Confidential Interval; AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion; NR\u003csup\u003e2\u003c/sup\u003e, Nagelkerke R\u003csup\u003e2\u003c/sup\u003e; LRT, Likelihood Ratio Test\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIgAN is a lifelong chronic affection which lead to a cumulative risk of poor renal outcome despite currently available treatments, with one-third of patient developing KF within 30 years after diagnosis [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] and a 19% cumulative incidence recurrence on graft after transplantation at 10 years [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], eliciting the un-met medical need for specific pathophysiology-oriented therapeutic strategies.\u003c/p\u003e \u003cp\u003eDespite being an autoimmune disease, the management of IgAN is mainly focused on non-immunosuppressive treatment, such as lifestyle intervention, BP control, RAASi, SGLT2i and dual endothelin-angiotensin receptor antagonist, reserving a course of steroid therapy in those at high-risk of disease progression (i.e. uProt\u0026thinsp;\u0026gt;\u0026thinsp;0.75g/die) despite supportive treatment. Enrollment in clinical trials must be always considered in this subgroup [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe CS is a part of innate immunity, critical for host defense against infections and tissue clearance from immunocomplexes or injured cells. Alterations leading to aberrant stimulations are responsible for a broad range of immune-mediated kidney diseases. Three main patways of activation have been described: classic pathway (CP), LP and AP [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eComplement-driven renal damage and its potential therapeutic targets are becoming increasingly studied. Growing evidence indicate the pathogenetic role of complement activation, as evinced by the finding of its components in mesangial immune complexes; more than 90% of kidney biopsies in IgAN show positive C3 staining in immunofluorescence, whereas C1q is rarely reported, suggesting possible activation of the AP and/or LP, as opposed to the CP. Deposit of complement regulatory proteins such as properdin, factor-B and factor-H was described [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNotwithstanding technical challenges and subjective measurement, the intensity of glomerular C3 staining might predict high risk of disease progression [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Moreover, a link between tissue deposits and disease activity was also recorded: presence of factor-H related proteins like CFHR5 (indicating AP) was associated with progressive disease, as well as C4d and mannose-binding lectin mesangial staining (indicating LP in the absence of C1q) was correlated with worse outcome. [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eFurther evidence for a complement-mediated pathogenesis came from genome-wide association studies which highlighted the role of deletion in CFHR1-3 genes (positive regulator of AP) in IgAN susceptibility. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] Endorsing this hypothesis an overactivation of these factors, suggested by elevated serum levels of CFHR1-5, has been linked to disease activity [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRegardless of the site of production (systemic or local) and the involved pathway (LP or AP) aberrant complement activation by mesangial immunocomplexes precipitate glomerular injury and tissue inflammation through production of anaphylatoxins (C3a, C5a) and membrane attack complex (C5b9). Activation of coagulation cascade further triggers damage [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs stated before, to date there are no validated markers of complement activation for IgAN. Plasmatic C3 and C4 fractions are surrogate markers of complement activation and are widely measured in current nephrological practice, but their link with IgAN prognosis is unclear and is yet to be elucidated. C3 values frequently fluctuate within normal limits, and when decreased (suggesting AP activation) have been correlated with poor kidney outcomes [\u003cspan additionalcitationids=\"CR33 CR34\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Some authors suggested that subjects with elevated serum IgA/C3 ratio seem more likely to receive a diagnosis of IgAN and to exhibit a worst disease course [\u003cspan additionalcitationids=\"CR37 CR38 CR39 CR40 CR41\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Similarly, the galactose-deficient-IgA1/C3 ratio at biopsy has been proposed as independent predictor of CKD progression in a wide Chinese cohort [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLess data is available for the interrelation between serum C4 and IgAN prognosis. In 2014 Zhu et al. described the correlation between augmented C4 and worse kidney injury at the time of biopsy, whereas lower C4 were linked to worse outcome at follow-up [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. A recent study conducted in 1356 Chinese IgAN patients confirmed that serum C4 correlated positively with uProt and negatively with eGFR at baseline. Furthermore, survival analysis found higher C4 being an independent risk-factor for disease progression. Noteworthy, C4 levels collected at the time of biopsy correlated with scoring of tubulointerstitial injury, glomerulosclerosis and crescents according to MEST-C score [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, the above-mentioned studies are limited in many ways as either C3 or C4 alone were assessed, biasing the global contribute of each component in determining renal prognosis.\u003c/p\u003e \u003cp\u003eThe present study aimed to investigate the prognostic value of baseline serum C3 and C4 in a cohort of 101 IgAN patients with a median follow-up of 54 months. Stratification of subjects according to baseline serum C3 levels identified 3 subgroups, named respectively \u0026ldquo;High\u0026rdquo; (\u0026gt;\u0026thinsp;140 mg/dl), \u0026ldquo;Medium\u0026rdquo; (90\u0026ndash;140 mg/dl) and \u0026ldquo;Low\u0026rdquo; (\u0026lt;\u0026thinsp;90 mg/dl). In contrast to the others, the hypocomplementemic subpopulation showed significant lower eGFR, lower C4 levels and higher proteinuria at baseline, suggesting the link between complement activation and more severe kidney injury at diagnosis. The latter confirmed by a significantly higher incidence rate of primary outcome compared to the Medium and High group, irrespective of immunosuppressant treatment.\u003c/p\u003e \u003cp\u003eIn order to assess possible relationship between serum complement and other baseline variables linear regression was performed, highlighting a positive correlation between C3 and C4 as previously described by Bi et colleagues, supporting the hypothesis of systemic complement consumption in complement-driven IgAN [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Furthermore, both C3 and C4 levels were found negatively correlated with serum IgM, pointing to a role for IgM that deserves to be further elucidated in future, as recently studied in a cohort of 116 pediatric patients by Xiong et al were mesangial IgM deposition has been found an independent risk factor for poor renal prognosis [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBoth C3 and C4 were significantly associated with baseline eGFR, confirming and expanding previous findigs by Bi et al. and Pan et al [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLogistic regression models showed that the addition of C3 and C4 confers a better prediction accuracy as compared to reference models without these variables. Intriguingly, this was particularly true for goodness of fit analysis (LRT) and discrimination (c-index). These results suggest that incorporating complement levels into the baseline assessment of patients could help to discriminate those who are at higher risk of poor renal outcome. Additionally, the NR2 test indicates that serum complement enhances the percentage of events explained by the models, highlighting its inherent informativeness. Regardless of prognostic measures, another main finding of our study is that C3 and C4 levels were significantly associated with outcome despite the presence of robust variables in the models such as proteinuria and eGFR, that normally account for most of the prediction.\u003c/p\u003e \u003cp\u003eOur data confirm previous observations from Pan et al that decreased C3 and increased C4 levels are associated with a poor renal prognosis in IgAN, expanding the evidence, for the first time to our knowledge, in a Caucasian-prevalent study population [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. For this perspective, our model can be considered a further validation of the previous documented evidence. Compared to the latter our study has a wider follow-up period (54 vs 35 months) and higher risk of events (15% vs 9.8%) despite smaller sample (101 vs 403 patients) and harder endpoint (loss of 40% vs 30% of eGFR).\u003c/p\u003e \u003cp\u003eAlthough complement activation in IgAN is widely described in current literature, the relative contribution of AP and LP is still not clear and needs to be discovered. Determining the dominant pathway driving IgAN progression may be useful to guide tailored therapies.\u003c/p\u003e \u003cp\u003eAt this moment we can safely conclude that complement activation in IgAN leads to worse outcome, eliciting the need for (new) biomarkers to assess risk of disease progression and potential response to new targeted treatments.\u003c/p\u003e \u003cp\u003eSerum complement C3 and C4 factors could be effective ed easy to obtain markers to screen the more \u0026ldquo;Complement-Pathic\u0026rdquo; subset of patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAkaike Information Criterion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlternative pathway\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBayesian Information Criterion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBlood pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCKD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChronic Kidney Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eComplement System\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eClassical Pathway\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlternative Pathway\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLectin Pathway\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiastolic blood pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eeGFR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eestimated Glomerular Filtration Rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIg\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eImmunoglobulin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIgAN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIgA Nephropathy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIntIgANPT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational IgA Nephropathy Prediction Tool\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterquartile range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKidney Failure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLectinic pathway\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLRT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLikelihood Ratio Test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNR\u003csup\u003e2\u003c/sup\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNagelkerke R square\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRAASi\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRenin\u0026ndash;Angiotensin\u0026ndash;Aldosterone System inhibitors\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSystolic blood pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard deviations\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSGLT2i\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSodium/Glucose Cotransporter 2 inhibitors\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003euProt\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e24h\u0026ndash;hour urine protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDisclosures:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy concern.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026lsquo; contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy design: ET, DV; Supervision: OB, MP, GLM; Data collection: FT, BF, GP; Statistical analysis: DV, MP; Data interpretation: ET, OB, FM; Draft writing ET, DV.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eO'Shaughnessy MM, Hogan SL, Thompson BD, Coppo R, Fogo AB, Jennette JC. Glomerular disease frequencies by race, sex and region: results from the International Kidney Biopsy Survey. Nephrol Dial Transplant. 2018;33(4):661\u0026ndash;669.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLai KN, Tang SC, Schena FP, Novak J, Tomino Y, Fogo AB, Glassock RJ. IgA nephropathy. Nat Rev Dis Primers. 2016;2:16001.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodrigues JC, Haas M, Reich HN. IgA Nephropathy. Clin J Am Soc Nephrol. 2017;12(4):677\u0026ndash;686.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRovin BH, Adler SG, Barratt J, Bridoux F, Burdge KA, Chan TM, Cook HT, Fervenza FC, Gibson KL, Glassock RJ, Jayne DRW, Jha V, Liew A, Liu ZH, Mej\u0026iacute;a-Vilet JM, Nester CM, Radhakrishnan J, Rave EM, Reich HN, Ronco P, Sanders JF, Sethi S, Suzuki Y, Tang SCW, Tesar V, Vivarelli M, Wetzels JFM, Lytvyn L, Craig JC, Tunnicliffe DJ, Howell M, Tonelli MA, Cheung M, Earley A, Floege J. Executive summary of the KDIGO 2021 Guideline for the Management of Glomerular Diseases. Kidney Int. 2021;100(4):753\u0026ndash;779\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBartosik LP, Lajoie G, Sugar L, Cattran DC. Predicting progression in IgA nephropathy. Am J Kidney Dis. 2001;38(4):728\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanaka S, Ninomiya T, Katafuchi R, Masutani K, Tsuchimoto A, Noguchi H, Hirakata H, Tsuruya K, Kitazono T. Development and validation of a prediction rule using the Oxford classification in IgA nephropathy. Clin J Am Soc Nephrol. 2013;8(12):2082\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen T, Li X, Li Y, Xia E, Qin Y, Liang S, Xu F, Liang D, Zeng C, Liu Z. Prediction and Risk Stratification of Kidney Outcomes in IgA Nephropathy. Am J Kidney Dis. 2019;74(3):300\u0026ndash;309.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarbour SJ, Coppo R, Zhang H, Liu ZH, Suzuki Y, Matsuzaki K, Katafuchi R, Er L, Espino-Hernandez G, Kim SJ, Reich HN, Feehally J, Cattran DC; International IgA Nephropathy Network. Evaluating a New International Risk-Prediction Tool in IgA Nephropathy. JAMA Intern Med. 2019;179(7):942\u0026ndash;952\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eProvenzano M, Rotundo S, Chiodini P, Gagliardi I, Michael A, Angotti E, Borrelli S, Serra R, Foti D, De Sarro G, Andreucci M. Contribution of Predictive and Prognostic Biomarkers to Clinical Research on Chronic Kidney Disease. Int J Mol Sci. 2020;21(16):5846\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaillard N, Wyatt RJ, Julian BA, Kiryluk K, Gharavi A, Fremeaux-Bacchi V, Novak J. Current Understanding of the Role of Complement in IgA Nephropathy. J Am Soc Nephrol. 2015;26(7):1503\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoos A, Rastaldi MP, Calvaresi N, Oortwijn BD, Schlagwein N, van Gijlswijk-Janssen DJ, Stahl GL, Matsushita M, Fujita T, van Kooten C, Daha MR. Glomerular activation of the lectin pathway of complement in IgA nephropathy is associated with more severe renal disease. J Am Soc Nephrol. 2006;17(6):1724\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeelen MA, Roos A, Daha MR. Role of complement in innate and autoimmunity. J Nephrol. 2005 Nov-Dec;18(6):642\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBi TD, Zheng JN, Zhang JX, Yang LS, Liu N, Yao L, Liu LL. Serum complement C4 is an important prognostic factor for IgA nephropathy: a retrospective study. BMC Nephrol. 2019;20(1):244.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePan M, Zhang J, Li Z, Jin L, Zheng Y, Zhou Z, Zhen S, Lu G. 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Recurrence of IgA Nephropathy after Kidney Transplantation in Adults. Clin J Am Soc Nephrol. 2021;16(8):1247\u0026ndash;1255.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnders HJ, Peired AJ, Romagnani P. SGLT2 inhibition requires reconsideration of fundamental paradigms in chronic kidney disease, 'diabetic nephropathy', IgA nephropathy and podocytopathies with FSGS lesions. Nephrol Dial Transplant. 2022;37(9):1609\u0026ndash;1615.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWheeler DC, Toto RD, Stef\u0026aacute;nsson BV, Jongs N, Chertow GM, Greene T, Hou FF, McMurray JJV, Pecoits-Filho R, Correa-Rotter R, Rossing P, Sj\u0026ouml;str\u0026ouml;m CD, Umanath K, Langkilde AM, Heerspink HJL; DAPA-CKD Trial Committees and Investigators. A pre-specified analysis of the DAPA-CKD trial demonstrates the effects of dapagliflozin on major adverse kidney events in patients with IgA nephropathy. 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Mesangial C3 deposition and serum C3 levels predict renal outcome in IgA nephropathy. Clin Exp Nephrol. 2021;25(6):641\u0026ndash;651.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie M, Zhu Y, Wang X, Ren J, Guo H, Huang B, Wang S, Wang P, Liu Y, Liu Y, Zhang J. Predictive prognostic value of glomerular C3 deposition in IgA nephropathy. J Nephrol. 2023;36(2):495\u0026ndash;505.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu J, Hu Z, Wang Y, Hu D, Yang Q, Li Y, Dai W, Zhu F, Yang J, Wang M, Zhu H, Liu L, He X, Han M, Yao Y, Pei G, Zeng R, Xu G. Severe glomerular C3 deposition indicates severe renal lesions and a poor prognosis in patients with immunoglobulin A nephropathy. Histopathology. 2021;78(6):882\u0026ndash;895.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMedjeral-Thomas NR, Troldborg A, Constantinou N, Lomax-Browne HJ, Hansen AG, Willicombe M, Pusey CD, Cook HT, Thiel S, Pickering MC. Progressive IgA Nephropathy Is Associated With Low Circulating Mannan-Binding Lectin-Associated Serine Protease-3 (MASP-3) and Increased Glomerular Factor H-Related Protein-5 (FHR5) Deposition. Kidney Int Rep. 2017;3(2):426\u0026ndash;438.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEspinosa M, Ortega R, S\u0026aacute;nchez M, Segarra A, Salcedo MT, Gonz\u0026aacute;lez F, Camacho R, Valdivia MA, Cabrera R, L\u0026oacute;pez K, Pinedo F, Gutierrez E, Valera A, Leon M, Cobo MA, Rodriguez R, Ballar\u0026iacute;n J, Arce Y, Garc\u0026iacute;a B, Mu\u0026ntilde;oz MD, Praga M; Spanish Group for Study of Glomerular Diseases (GLOSEN). Association of C4d deposition with clinical outcomes in IgA nephropathy. Clin J Am Soc Nephrol. 2014;9(5):897\u0026ndash;904.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSegarra A, Romero K, Agraz I, Ramos N, Madrid A, Carnicer C, Jatem E, Vilalta R, Lara LE, Ostos E, Valtierra N, Jaramillo J, Arredondo KV, Ariceta G, Martinez C. Mesangial C4d Deposits in Early IgA Nephropathy. Clin J Am Soc Nephrol. 2018;13(2):258\u0026ndash;264.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGharavi AG, Kiryluk K, Choi M, Li Y, Hou P, Xie J, Sanna-Cherchi S, Men CJ, Julian BA, Wyatt RJ, Novak J, He JC, Wang H, Lv J, Zhu L, Wang W, Wang Z, Yasuno K, Gunel M, Mane S, Umlauf S, Tikhonova I, Beerman I, Savoldi S, Magistroni R, Ghiggeri GM, Bodria M, Lugani F, Ravani P, Ponticelli C, Allegri L, Boscutti G, Frasca G, Amore A, Peruzzi L, Coppo R, Izzi C, Viola BF, Prati E, Salvadori M, Mignani R, Gesualdo L, Bertinetto F, Mesiano P, Amoroso A, Scolari F, Chen N, Zhang H, Lifton RP. Genome-wide association study identifies susceptibility loci for IgA nephropathy. Nat Genet. 2011;43(4):321\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKiryluk K, Li Y, Sanna-Cherchi S, Rohanizadegan M, Suzuki H, Eitner F, Snyder HJ, Choi M, Hou P, Scolari F, Izzi C, Gigante M, Gesualdo L, Savoldi S, Amoroso A, Cusi D, Zamboli P, Julian BA, Novak J, Wyatt RJ, Mucha K, Perola M, Kristiansson K, Viktorin A, Magnusson PK, Thorleifsson G, Thorsteinsdottir U, Stefansson K, Boland A, Metzger M, Thibaudin L, Wanner C, Jager KJ, Goto S, Maixnerova D, Karnib HH, Nagy J, Panzer U, Xie J, Chen N, Tesar V, Narita I, Berthoux F, Floege J, Stengel B, Zhang H, Lifton RP, Gharavi AG. Geographic differences in genetic susceptibility to IgA nephropathy: GWAS replication study and geospatial risk analysis. PLoS Genet. 2012;8(6):e1002765.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMedjeral-Thomas NR, Lomax-Browne HJ, Beckwith H, Willicombe M, McLean AG, Brookes P, Pusey CD, Falchi M, Cook HT, Pickering MC. Circulating complement factor H-related proteins 1 and 5 correlate with disease activity in IgA nephropathy. Kidney Int. 2017;92(4):942\u0026ndash;952.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTortajada A, Guti\u0026eacute;rrez E, Goicoechea de Jorge E, Anter J, Segarra A, Espinosa M, Blasco M, Roman E, Marco H, Quintana LF, Guti\u0026eacute;rrez J, Pinto S, Lopez-Trascasa M, Praga M, Rodriguez de C\u0026oacute;rdoba S. Elevated factor H-related protein 1 and factor H pathogenic variants decrease complement regulation in IgA nephropathy. Kidney Int. 2017;92(4):953\u0026ndash;963.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLe Stang MB, Gleeson PJ, Daha MR, Monteiro RC, van Kooten C. 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Kidney Int. 1997;51(4):1257\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTomino Y, Suzuki S, Imai H, Saito T, Kawamura T, Yorioka N, Harada T, Yasumoto Y, Kida H, Kobayashi Y, Endoh M, Sato H, Saito K. Measurement of serum IgA and C3 may predict the diagnosis of patients with IgA nephropathy prior to renal biopsy. J Clin Lab Anal. 2000;14(5):220\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaeda A, Gohda T, Funabiki K, Horikoshi S, Shirato I, Tomino Y. Significance of serum IgA levels and serum IgA/C3 ratio in diagnostic analysis of patients with IgA nephropathy. J Clin Lab Anal. 2003;17(3):73\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYanagawa H, Suzuki H, Suzuki Y, Kiryluk K, Gharavi AG, Matsuoka K, Makita Y, Julian BA, Novak J, Tomino Y. A panel of serum biomarkers differentiates IgA nephropathy from other renal diseases. PLoS One. 2014;9(5):e98081.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGong WY, Liu M, Luo D, Liu FN, Yin LH, Li YQ, Zhang J, Peng H. High serum IgA/C3 ratio better predicts a diagnosis of IgA nephropathy among primary glomerular nephropathy patients with proteinuria\u0026thinsp;\u0026le;\u0026thinsp;1 g/d: an observational cross-sectional study. BMC Nephrol. 2019;20(1):150.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang J, Wang C, Tang Y, Peng H, Ye ZC, Li CC, Lou TQ. Serum immunoglobulin A/C3 ratio predicts progression of immunoglobulin A nephropathy. Nephrology (Carlton). 2013;18(2):125\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKomatsu H, Fujimoto S, Hara S, Sato Y, Yamada K, Eto T. Relationship between serum IgA/C3 ratio and progression of IgA nephropathy. Intern Med. 2004;43(11):1023\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStefan G, Stancu S, Boitan B, Zugravu A, Petre N, Mircescu G. Is There a Role for IgA/C3 Ratio in IgA Nephropathy Prognosis? An Outcome Analysis on An European Population. Iran J Kidney Dis. 2020;14(6):470\u0026ndash;477.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen P, Yu G, Zhang X, Xie X, Wang J, Shi S, Liu L, Lv J, Zhang H. Plasma Galactose-Deficient IgA1 and C3 and CKD Progression in IgA Nephropathy. Clin J Am Soc Nephrol. 2019;14(10):1458\u0026ndash;1465.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu B, Zhu CF, Lin Y, Perkovic V, Li XF, Yang R, Tang XL, Zhu XL, Cheng XX, Li Q, Chen HY, Sun Y, Chen QW, Wang YJ. Clinical characteristics of IgA nephropathy associated with low complement 4 levels. Ren Fail. 2015;37(3):424\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiong L, Liu L, Tao Y, Guo H. Clinical significance of IgM and C3 deposition in children with primary immunoglobulin A nephropathy. J Nephrol. 2023 Aug 5. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s40620-023-01724-7\u003c/span\u003e\u003cspan address=\"10.1007/s40620-023-01724-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Complement, C3, C4, IgA Nephropathy, MEST, Glomerulonephritis","lastPublishedDoi":"10.21203/rs.3.rs-4344779/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4344779/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIgA Nephropathy (IgAN) is the prevalent glomerular disease worldwide. Complement system activation is crucial in its pathogenesis. Few studies correlated serum C3 and C4 with disease activity and prognosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective monocentric study investigated the prognostic value of serum complement in patients with IgAN. Primary outcome was defined as 40% decline in eGFR or onset of kidney failure. The aim was to evaluate whether the addition of serum C3 and C4 to established predictive models, including one based on variables related to chronic kidney disease (CKD) progression and another incorporating variables from the International IgA Prediction Tool (IntIgAPT), enhances the accuracy of outcome prediction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e101 patients were stratified according to baseline C3 levels in three groups (Low, Medium and High). During a median 54.28 months follow-up, the Low group exhibited higher primary outcome incidence (16.3 events vs 2.9 and 1.7 events x 100 pts/year, p = 0.0026). Model-1 (M1), consisting of CKD progression variables, and Model-3 (M3), comprising IntIgANPT variables, were implemented with baseline C3 and C4 to form Model-2 (M2) and Model-4 (M4), respectively. M2 demonstrated improved predictive performance over M1 showing higher discrimination (lower AIC and BIC, higher C-index and NR2). Similarly, M4 outperformed M3 showing enhanced outcome prediction when adding C3 and C4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInclusion of serum C3 and C4 can enhance prediction accuracy of already existing prognostic models. Specifically, lower C3 and higher C4 levels were associated with poorer prognosis in IgAN, characterizing a more 'Complement-Pathic' subset of patients.\u003c/p\u003e","manuscriptTitle":"Looking for a new role of known players: the additional value of plasmatic C3 and C4 in predicting IgA Nephropathy prognosis, an observational study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-09 18:21:34","doi":"10.21203/rs.3.rs-4344779/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-05T11:25:38+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-04T18:46:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-30T21:12:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"200243915804790647100581721374429262607","date":"2024-05-24T15:44:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"37565493196107148486237166435135810896","date":"2024-05-24T13:01:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-07T06:53:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-07T06:36:41+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-05-03T13:38:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-02T09:26:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-04-29T18:51:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"adf11e12-828b-47a9-8556-d41e7d2b8e56","owner":[],"postedDate":"May 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":31630275,"name":"Health sciences/Nephrology"},{"id":31630276,"name":"Health sciences/Nephrology/Kidney diseases/Glomerular diseases/Iga nephropathy"}],"tags":[],"updatedAt":"2024-06-25T04:21:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-09 18:21:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4344779","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4344779","identity":"rs-4344779","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}