Clinical significance of the time-average systemic immunoinflammatory index in primary immunoglobulin A nephropathy: a bicentric retrospective cohort study

Ya Hu; Ziyuan Huang; Wanwan Zhao; Hong Zhang; Ganxiu Li; Sishi Lin; Fangfang Feng; Chaosheng Chen; Zewen Liu

doi:10.23876/j.krcp.24.260

Kidney Res Clin Pract > Epub ahead of print

Hu, Huang, Zhao, Zhang, Li, Lin, Feng, Chen, and Liu: Clinical significance of the time-average systemic immunoinflammatory index in primary immunoglobulin A nephropathy: a bicentric retrospective cohort study

Original Article

Kidney Research and Clinical Practice 2025;j.krcp.24.260.

Published online: April 25, 2025

DOI: https://doi.org/10.23876/j.krcp.24.260

Clinical significance of the time-average systemic immunoinflammatory index in primary immunoglobulin A nephropathy: a bicentric retrospective cohort study

Ya Hu^1,^*

, Ziyuan Huang^2,^*

, Wanwan Zhao¹

, Hong Zhang¹

, Ganxiu Li¹

, Sishi Lin²

, Fangfang Feng²

, Chaosheng Chen²

, Zewen Liu¹

¹Department of Nephrology, Fuyang People’s Hospital affiliated with Anhui Medical University, Fuyang, China

²Department of Nephrology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China

Correspondence: Ya Hu Department of Nephrology, Fuyang People’s Hospital affiliated with Anhui Medical University, Fuyang 236000, Anhui, China. E-mail: 497356558@qq.com

^*Ya Hu and Ziyuan Huang contributed eqully to this article.

Ziyuan Huang's current affiliation: Department of Nephrology, The First Affiliated Hospital of Ningbo University, Ningbo, China

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial and No Derivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted non-commercial use, distribution of the material without any modifications, and reproduction in any medium, provided the original works properly cited.

Abstract

Background

The systemic immune-inflammation index (SII) is a potential marker reflecting the systemic inflammatory response. However, the clinical significance of SII in immunoglobulin A nephropathy (IgAN) remains to be determined.

Methods

This retrospective study included 1,399 IgAN patients diagnosed by renal biopsy between January 2011 and December 2023. The low time-averaged SII (L-TASII) group comprised patients with TASII values below the upper tertile, whereas the high TASII (H-TASII) group included the remaining patients. All patients were matched 1:1 for age, sex, follow-up duration, and baseline estimated glomerular filtration rate (eGFR). Clinical, pathological, and prognostic features were compared between the two groups.

Results

After matching, both the L-TASII and H-TASII groups consisted of 556 patients. Patients in the H-TASII group had lower albumin levels and higher proteinuria. When a ≥30% decline in eGFR from baseline or the onset of end-stage renal disease was defined as the composite endpoint, Kaplan-Meier analysis revealed that the renal survival rate was significantly lower in the H-TASII group compared to the L-TASII group after an average follow-up of 59.91 ± 32.42 months (log-rank p < 0.01). Multivariate analysis demonstrated that TASII was an independent predictor of endpoint events (hazard ratio, 1.02; 95% confidence interval, 1.01–1.04; p = 0.001).

Conclusion

The TASII score is a significant risk factor for adverse renal outcomes in IgAN patients and serves as a reliable predictor of renal survival.

Keywords: IgA glomerulonephritis, Pathology, Prognosis, Systemic immune inflammation index

Introduction

Immunoglobulin A nephropathy (IgAN) is a common form of primary glomerulonephritis, with an incidence of around 2.5 of 100,000, and predominantly affects young adults [1]. IgAN patients with high levels of heterogeneity are characterized by spontaneous and slow progress, or kidney function deteriorates rapidly to the point that renal replacement therapy is required. Previous research shows that approximately 30% to 40% of patients with IgAN reach end-stage renal disease (ESRD) 20 to 30 years after the first clinical presentation [2,3], thus creating a heavy economic and psychological burden for the patients. Therefore, there is an urgent need to develop an index that can determine the risk of developing IgAN.

The specific pathogenesis of IgAN remains unclear, although previous research has suggested that kidney autoimmunity, inflammation, and fibrosis are associated with slow disease progression [4]. One study investigated a cohort of 4,926 patients for up to 15 years and found that interleukin 6 (IL-6), white blood cell count, tumor necrosis factor-alpha (TNF-α) receptor 2, and high-sensitivity C-reactive protein (CRP) were positively related to the risk of suffering from chronic kidney disease [5]. Inflammation indices can provide good levels of prediction for chronic kidney disease (CKD) and the risk of adverse outcomes, including the ratio of monocytes and lymphocytes [6], and the platelet and lymphocyte ratio [7]. Over recent years, studies have shown that a high ratio of neutrophils and lymphocytes are independent risk factors in predicting the progression of IgAN [8,9]. However, these studies only included individual immune inflammatory cells and may not be able to fully reflect the inflammatory status.

Systemic immune inflammation index (SII) is a new form of inflammatory biomarker and is defined as the peripheral neutrophil count multiplied by the platelet count and divided by the lymphocyte count, and was initially employed for predicting the prognosis of liver cancer patients [10]. Previous research revealed that SII increased as diabetic nephropathy (DN) developed in type 2 diabetes mellitus patients; thus, SII may represent a low-cost, high-benefit indicator for diabetic kidney disease [11]. In addition, SII could serve as a valuable predictor for determining the need for long-term dialysis in children with CKD [12], adverse cardiovascular events in CKD patients [13,14], and the risk of death in individuals undergoing peritoneal dialysis [15]. However, there are very few studies on the use of SII in managing IgAN patients. In the present study, we retrospectively analyzed baseline SII in IgAN patients and investigated the associations of SII with renal function, pathology, and follow-up outcomes. In addition, we discuss the application prospects of SII as a prognostic indicator for IgAN patients.

Methods

Patients

We selected 2,567 IgAN patients (>18 years of age, confirmed through renal biopsy) at Fuyang People’s Hospital affiliated with Anhui Medical University and the First Affiliated Hospital of Wenzhou Medical University from January 2011 to December 2023. We excluded patients for the following reasons: 1) the presence of primary/secondary glomerular diseases; 2) other diseases secondary to IgAN, including systemic lupus erythematosus, allergic purpura, and hepatitis B viral hepatitis; 3) a short follow-up time (<6 months); and 4) an incomplete set of baseline information. Finally, 1,399 cases with primary IgAN were used in our analysis (Fig. 1).

According to the time-average SII (TASII) level, patients with a TASII less than the top third of the whole cohort were categorized into the low TASII group (L-TASII), and propensity score matching was employed to match patients within this group. Based on age at baseline, sex, estimated glomerular filtration rate (eGFR), and follow-up duration, 1:1 matching was used to allocate the remaining patients into a high TASII group (H-TASII).

Ethics statement

The study protocol adhered to the principles outlined in the Declaration of Helsinki. Ethics approval was obtained from the Institutional Review Boards (IRBs) of Fuyang People’s Hospital affiliated with Anhui Medical University and the First Affiliated Hospital of Wenzhou Medical University. Given the retrospective nature of this research and the use of anonymized data, the IRBs explicitly exempted the requirement for informed consent from the patients.

Clinical data

We collected a range of basic information for the included patients, including age, sex, diastolic and systolic blood pressure; and laboratory indices, including baseline data obtained during renal biopsy, such as neutrophils, platelets, lymphocytes, hemoglobin, serum creatinine, uric acid, blood albumin, triglycerides, total cholesterol (TC), high-density and low-density lipoprotein cholesterol, fibrinogen, and 24-hour urinary protein. We also collated information related to follow-up, neutrophil data during follow-up, platelets, lymphocytes, hemoglobin, and serum creatinine. We also recorded hospitalization and follow-up time for patients undergoing primary treatment, along with the use of glucocorticoid and immunosuppressants, renin-angiotensin-aldosterone system inhibitors, and other medications (Table 1).

Pathological data

Renal needle biopsies were performed by specialized nephrologists according to standard techniques. Pathological findings of renal biopsy specimens were assessed by expert renal pathologists based on the Oxford classification for IgAN [16], including mesangial hypercellularity (M0/1), endocapillary hypercellularity (E0/1), tubular atrophy and interstitial fibrosis (T0/1/2), crescent formation (C0/1), and segmental glomerulosclerosis (S0/1). In addition, we determined the percentage of glomerular sclerosis in pathological specimens obtained from renal biopsy (Table 1).

Definitions of key metrics

The creatinine revised CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) formula (2021) was applied to determine the eGFR [17]. The formula to calculate SII was as follows: SII = (neutrophils × platelets) / lymphocytes [10]. TASII was defined as follows: for each patient with IgAN during follow-up, we considered 6 months as a time interval and collected all of the time intervals for SII to represent the time interval and calculated the average SII. The composite endpoints of this study were defined as an eGFR reduced by 30% or higher compared with the baseline or progression into ESRD.

Statistical analysis

Continuous data that conform to the normal distribution are described as mean ± standard deviation and analyzed using the t test. The continuity of non-normally distributed data is described by medians and quartiles and comparisons between groups were performed by Wilcoxon rank test. We classified variables by the number of cases (%) and according to the specific performed comparisons between groups by the chi-square test or Fisher exact test. Kaplan-Meier curves (log-rank test) were utilized to reveal the difference in prognosis between patients in different SII groups. A generalized linear mixed model (GLMM) was employed to analyze the difference in variables between groups and different follow-up periods, and a line chart was generated to depict the marginal mean and the variance of each variable. Following adjustment for individual risk factors, a Cox regression model was developed to analyze the predictors for composite endpoint events. Values were shown as hazard ratios (HRs) and 95% confidence intervals (CIs). Indicators with significant differences (p < 0.10) in the univariate Cox HR model were incorporated into the multivariate HR model. This model was optimized to obtain the Akaike Information Criterion using bidirectional stepwise regression. Two-sided tests were conducted, with p < 0.05 deemed statistically significant. Statistical tests were carried out using R version 4.0.4 (R Foundation for Statistical Computing).

Results

Analysis of baseline data

A total of 556 patients with L-TASII and 556 patients with H-TASII matched with relevant baseline characteristics were recruited. The average follow-up time was 59.93 months. The shortest and longest follow-up times were 6.12 and 109.23 months, respectively. In total, 187 patients (16.8%) yielded composite endpoint events. Comparison between the two groups of baseline clinical data revealed that patient age, sex ratio, or follow-up time were not obviously different between the two groups. Compared with the L-TASII group, SII (p < 0.001), 24-hour proteinuria (p = 0.003), fibrinogen (p = 0.006), peripheral neutrophil count (p < 0.001), and platelet count (p < 0.001) in the H-TASII group were higher, and the serum albumin level was lower (p = 0.01). Additionally, the proportion of patients receiving glucocorticoid therapy was markedly higher in the H-TASII group (p = 0.01).

A comparison of prognosis and risk factors

No obvious difference in follow-up time was found between the two groups; however, the proportion of composite endpoint events was markedly increased in the H-TASII group (p = 0.04). Moreover, the overall long-term prognosis was more unfavorable in the H-TASII group than in the L-TASII group (p < 0.01), based on Kaplan-Meier curve analysis (Fig. 2).

The GLMM showed that during the follow-up period, the value of SII was remarkably higher in the H-TASII group than in the L-TASII group (p < 0.001); this was not related to follow-up duration. There was an interaction between the between-group differences in platelet count, neutrophil count, lymphocyte count, and follow-up duration. Furthermore, no obvious differences were found in platelet count, neutrophil count, and lymphocyte count between the two groups (Fig. 3; Supplementary Tables 1–3, available online).

Multivariate Cox regression analysis of all patients with primary IgAN revealed that albumin (HR, 0.97; 95% CI, 0.93–0.99, p = 0.02), eGFR (HR, 0.99; 95% CI, 0.98–1.00, p = 0.007), TC (HR, 1.14; 95% CI, 1.02–1.27, p = 0.02), the proportion of glomerulosclerosis (HR, 4.53; 95% CI, 2.23–9.72, p = 0.001), and TASII (HR, 1.23; 95% CI, 1.21–1.25, p = 0.001) were all independent predictors for composite endpoint events in IgAN patients (Table 2). Moreover, a Cox regression model was constructed by replacing SII with platelet counts, neutrophil counts, and lymphocyte counts. The findings revealed that platelet counts, neutrophil counts, and lymphocyte counts were not independent predictors of composite endpoint events in IgAN patients (Supplementary Tables 4–7, available online).

Discussion

Although the underlying mechanisms of IgAN remain incompletely understood, the prevailing view among researchers is that its development represents a chronic and progressive condition characterized by repeated immune-mediated assaults on the kidneys [18]. SII is a computed indicator derived from neutrophils platelets, and lymphocytes; researchers consider that SII can reflect the inflammation and immune status of a patient, at least to some extent [19]. Therefore, we hypothesized that SII may also indicate the condition and outcomes of IgAN patients. Previous research has not investigated the correlation between SII and the progression and prognosis of IgAN. Herein, patients with primary IgAN were grouped after screening according to specific inclusion criteria and propensity score matching was performed prior to data comparison. We detected many differences between patients with different TASII levels in terms of their clinical status and prognosis. Overall, IgAN patients with a high TASII had more serious clinical manifestations and a worse prognosis; therefore, high TASII scores represent a significant risk factor for unfavorable outcomes in IgAN patients.

Previous research discovered that a high neutrophil-to-lymphocyte ratio (NLR) represented a significant risk factor for renal outcome in IgAN patients and that pathological variations differed significantly between patients with increased NLR and those with decreased NLR. IgAN patients with elevated NLR levels were more prone to developing segmental glomerulosclerosis, tubular atrophy/interstitial fibrosis, and cellular/fibrocellular crescents [8]. However, because the severity of renal pathology in IgAN can directly reflect renal function [16], it is difficult to exclude bias in these previous findings caused by the pathological differences themselves. Herein, no obvious differences in pathological features were noted between the two groups. At baseline, the extent of kidney disease was similar between the two groups, largely because of the influence of different pathologies on prognosis.

Numerous reports have shown that inflammation can exacerbate renal dysfunction and that the reduction of inflammation indices can slow the progression of renal decline, thus affecting the severity of glomerular disease and progression. Therefore, we believe that the effect of time-dependent confounding factors on the entire study cannot be excluded, and only analyzing the isolated baseline data relating to SII cannot accurately evaluate disease status and prognosis. In order to reduce errors, previous studies have calculated time-averaged hematuria [20] and time-averaged proteinuria [21] to determine the impact of related indicators on patient prognosis. Referring to previous research methods, we defined the concept of TASII, undertook chronic disease management for IgAN patients, and the median SII during this interval was recorded for an interval of 6 months after renal biopsy; this was expressed as TASII.

Compared to the L-TASII group, the clinical manifestations of patients in the H-TASII group at baseline were more serious and disease activity was stronger, including higher serum creatinine, lower serum albumin, and more severe proteinuria. These factors have all been confirmed to directly influence the prognostic outcomes of IgAN patients in prior studies [22]. Moreover, patients in the H-TASII group showed significantly higher inflammation-related indicators, including blood neutrophil count, platelet count, and fibrinogen. In particular, the baseline SII was remarkably elevated compared to the L-TASII group, indicating severe inflammatory reactions in the H-TASII group. Considering that steroids exert strong anti-inflammatory effects in clinical treatment, patients in the H-TASII group exhibiting severe clinical symptoms generally benefit from glucocorticoids to manage their condition. Nonetheless, glucocorticoids did not significantly alter the prognosis of IgAN in most H-TASII patients, further emphasizing the critical role of SII in determining the prognostic outcomes of IgAN.

With the same baseline demographic characteristics, the proportion of patients with a poor outcome was markedly higher in the H-TASII group than in the L-TASII group. The long-term prognostic outcomes for patients in the H-TASII group were worse compared to the L-TASII group. Additionally, the H-TASII group exhibited an increased proportion of composite endpoint events (p = 0.04). Analysis of influencing factors for an unfavorable prognosis in IgAN patients revealed that in addition to currently confirmed risk factors (such as TC, serum albumin, serum creatinine, and eGFR, and proteinuria), especially proteinuria, as an important prognostic risk factor of IgAN. Cox regression shows that SII is also a significant risk factor for unfavorable prognostic outcomes of IgAN, and it does not interfere with other indexes such as proteinuria. The higher the TASII, the worse the prognosis. In addition, multivariate Cox regression analysis, using blood neutrophil count, platelet count, and lymphocyte count instead of SII, revealed that none of these three parameters were significant risk factors for the prognostic outcomes of IgAN. In addition, the GLMM model showed that these parameters differed significantly between the groups, along with follow-up interactions. Despite the fact that the baseline values of these several indicators in the H-TASII group were significantly higher than those of L-TASII, the former three showed no significant differences in the process of follow-up, while TASII still has significantly high levels of performance, which prompts that TASII has relatively high stability, its initial state can well predict the inflammatory state in follow-up. Therefore, compared with the former three individual monitoring measurements, we consider that TASII monitoring has clear superiority for predicting renal outcomes in patients with IgAN. Furthermore, the TASII is a clinical indicator that is readily derived, is economically accessible, and is therefore worth popularizing in wider clinical practice.

The onset of IgAN is due to immunoglobulin-A1 immune complexes in the kidneys that can lead to the local release of chemokines, cytokines, etc., resulting in glomerular lesions [22]. There have also been studies suggesting that tubulointerstitial inflammation can affect the prognosis of immunoglobulin A, but the specific mechanism is not yet clear [23]. Inflammation can cause changes in the blood components of neutrophils, monocytes, and platelets, leading to increased cytokine secretion and subsequent worsening or exacerbation of renal lesions. However, more recent studies have found that SII, compared to traditional inflammation markers, seems to be able to reflect the strength of inflammation in many diseases, including rheumatic, tumor, and cardiovascular diseases, and shows better prognostic value [24–26]. Our study found that IgAN patients with higher TASII had worse prognosis, considering that IgAN patients with higher TASII may represent a stronger inflammatory state, which may contribute to their poor prognosis. Furthermore, the study shows that patients with vascular complications in IgAN, particularly those with arteriolar/arteriosclerosis, tend to have more severe pathological and clinical manifestations, which are independently associated with progression to renal failure [27,28]. A recent study reveals that SII is strongly associated with atherosclerosis, which is linked to endothelial damage, oxidative stress, and thrombosis. This could be attributed to the strong correlation between inflammation and immune status, and we consider that patients with higher SII may have more vascular lesions, which may affect the progression of IgAN. Some studies have found a pleiotropic relationship between lipid levels and CRP [29,30]. A cross-sectional study including 6,117 adults reported that SII was significantly positively correlated with hyperlipidemia, which has been shown to induce inflammatory responses by activating the stimulator of interferon genes pathway [31]. Another study has found that IgAN patients with abnormal lipid metabolism have more severe pathological and clinical manifestations, characterized by more frequent monocyte and lymphocyte infiltration, tubular atrophy/interstitial fibrosis (T1/2), and segmental glomerulosclerosis (S1) [32]. Our study shows that TC is a significant factor for prognosis in IgAN, consistent with other studies, and considering that H-TASII patients show higher TC levels, which affects the prognostic outcomes of IgAN patients. However, the underlying mechanisms still require further investigation.

This research has a few limitations. Firstly, this is a retrospective study and lacked data relating to certain inflammatory indexes, such as IL-6 and TNF-α, at baseline. It is possible that these indicators may also be linked to the adverse renal outcomes of IgAN and therefore require further investigation. Second, although propensity matching was performed as much as possible in the groups, the interference of some confounding factors, such as IgAN onset time, type of drug use, duration, and drug dose, on the study results cannot be completely excluded.

In conclusion, high TASII scores indicate more serious and active disease in IgAN patients. Thus, an elevated TASII score constitutes a risk factor for an unfavorable prognosis in IgAN patients. Improving the state of inflammation in these patients may generate long-term benefits for the prognostic outcomes of IgAN patients. As an economical, accessible, and reliable inflammatory marker, the TASII score may facilitate clinical practice to identify high-risk IgAN patients and provide important reference value for the evaluation of therapeutic efficacy posttreatment.

Supplementary Materials

Supplementary data are available at Kidney Research and Clinical Practice online (https://doi.org/10.23876/j.krcp.24.260).

j-krcp-24-260-Supplementary-Table-1.pdf

j-krcp-24-260-Supplementary-Table-2.pdf

j-krcp-24-260-Supplementary-Table-3.pdf

j-krcp-24-260-Supplementary-Table-4.pdf

j-krcp-24-260-Supplementary-Table-5.pdf

j-krcp-24-260-Supplementary-Table-6.pdf

j-krcp-24-260-Supplementary-Table-7.pdf

Notes

Conflicts of interest

All authors have no conflicts of interest to declare.

Funding

This study was supported by grants from a project of the Natural Science Foundation of Zhejiang Province (No. LY22H050004).

Acknowledgments

The authors express their gratitude to their colleagues in Department of Blood Purification at Fuyang People’s Hospital affiliated with Anhui Medical University and Department of Nephrology at the First Affiliated Hospital of Wenzhou Medical University for their valuable assistance and support throughout the duration of the study.

Data sharing statement

Due to ethical considerations, the data used in this study are not publicly available. The data presented in this study are available from the corresponding author upon reasonable request.

Authors’ contributions

Conceptualization, Methodology, Project administration, Supervision, Validation: YH

Data curation: ZH, WZ, HZ, GL, SL, FF, ZL

Formal analysis: ZH, WZ

Funding acquisition, Resources: CC

Investigation: ZH, WZ, HZ, GL, SL, FF, ZL

Writing–original draft: ZH, WZ

Writing–review & editing: YH, CC

All authors read and approved the final manuscript.

Figure 1.

The process used to select eligible patients.

IgAN, immunoglobulin A nephropathy; SII, systemic immune inflammation index.

Figure 2.

Prognostic comparison between the H-TASII and L-TASII groups by Kaplan-Meier curve analysis.

Endpoint: ≥30% reduction in estimated glomerular filtration rate from baseline or progression to end-stage renal disease.

H-TASII, high time-average systemic immune inflammation index; L-TASII, low time-average systemic immune inflammation index.

Figure 3.

Generalized linear mixed model was used to estimate platelet count, neutrophil count, lymphocyte count, and the marginal mean and corresponding standard error of SII between the H-TASII and L-TASII groups at 60 months of follow-up.

H-TASII, high time-average systemic immune inflammation index; L-TASII, low time-average systemic immune inflammation index; PLT, blood platelet; SII, systemic immune inflammation index.

Table 1.

Comparison of clinical parameters between the H-TASII and L-TASII groups

Parameter	H-TASII group	L-TASII group	p-value^a)
No. of subjects	556	556
Age (yr)	39.73 ± 12.64	39.87 ± 12.23	0.70
Male sex	270 (48.6)	226 （40.6	0.80
MAP (mmHg)	144.37 ± 16.82	142.35 ± 16.93	0.10
Follow-up (mo)	58.48 ± 31.61	61.35 ± 33.24	0.40
sCr (umol/L)	1.21 ± 1.16	1.04 ± 0.53	0.08
eGFR (mL/min/1.73 m²)	87.93 ± 30.31	87.74 ± 27.46	0.92
Albumin (g/L)	36.88 ± 6.24	37.93 ± 5.45	0.01
Total cholesterol (mmol/L)	1.92 ± 1.26	1.86 ± 1.42	0.80
Triglyceride (mmol/L)	5.16 ± 1.48	5.07 ± 1.28	0.50
HDL (mmol/L)	1.12 ± 0.32	1.17 ± 0.34	0.60
LDL (mmol/L)	2.94 ± 1.03	2.92 ± 0.91	0.80
Urine acid (umol/L)	376.56 ± 98.92	367.85 ± 92.87	0.20
Hemoglobin (g/L)	129.00 ± 19.93	128.95 ± 18.92	>0.99
Fibrinogen (g/L)	3.62 ± 1.07	3.44 ± 0.92	0.006*
Proteinuria (g/day)	2.19 ± 2.25	1.75 ± 2.07	0.003*
sNC (×10⁹/L)	4.70 ± 2.14	3.67 ± 1.44	<0.001*
Baseline_SII	6.74 ± 5.23	4.00 ± 2.36	<0.001*
sLC (×10⁹/L)	2.16 ± 0.66	2.14 ± 0.59	0.07
Blood platelet (×10⁹/L)	255.06 ± 61.43	219.73 ± 57.16	<0.001*
Glomerulosclerosis	0.27 ± 0.20	0.25 ± 0.18	0.10
TASII	7.35 ± 6.52	3.32 ± 0.71	<0.001*
uWBC (×10⁹/L)	296.66 ± 1,078.95	216.42 ± 563.93	0.20
uRBC (×10¹²/L)	509.46 ± 1,032.85	446.02 ± 719.99	0.30
Oxford classification
M1	202 (36.3)	190 (34.2)	0.40
E1	248 (44.6)	217 (39.0)	0.20
S1	408 (73.4)	405 (72.8)	0.50
T category			0.30
T1	145 (26.1)	149 (26.8)
T2	56 (10.1)	39 (7.0)
C1	325 (58.5)	291 (52.3)	0.10
Endpoint	110 (19.8)	77 (13.8)	0.04
Glucocorticoid	343 (61.7)	295 (53.1)	0.01
Immunosuppression	200 (36.0)	172 (30.9)	0.10
ACEI/ARB	519 (93.3)	527 (94.8)	0.60

Data are expressed as number only, mean ± standard deviation, or number (%).

ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blocker; eGFR, estimated glomerular filtration rate; H-TASII, high time-average systemic immune inflammation index; HDL, high-density lipoprotein cholesterol; L-TASII, low time-average systemic immune inflammation index; LDL, low-density lipoprotein cholesterol; MAP, mean arterial pressure; TASII, time-average systemic immunoinflammatory index; sCr, serum creatinine; sLC, serum lymphocyte counts; sNC, serum neutrophil counts; uRBC, urine red blood cell counts; uWBC, urine white blood cell counts.

Oxford classification: M1, mesangial hypercellularity; E1, endocapillary hypercellularity; S1, segmental glomerulosclerosis; T1, mild to moderate tubular atrophy and interstitial fibrosis; T2, severe tubular atrophy and interstitial fibrosis; C1, crescents.

All p-values were calculated using two-sided tests. *p < 0.01.

Table 2.

Analysis of factors affecting adverse renal outcome in patients with IgAN

Factor	HR (95% CI)	p-value	HR (95% CI)	p-value
Age	1.01 (0.99–1.04)	0.40
Male sex	1.47 (1.06–2.01)	0.02
MAP	1.16 (1.07–1.26)	<0.001
sCr	1.40 (1.32–1.59)	<0.001
eGFR	0.98 (0.98–0.99)	<0.001	0.99 (0.98–1.00)	0.007
Albumin	0.96 (0.92–0.98)	<0.001	0.97 (0.93–0.99)	0.02
Total cholesterol	1.20 (1.09–1.32)	<0.001	1.14 (1.02–1.27)	0.02
Triglyceride	1.12 (1.00–1.26)	0.05
HDL	0.78 (0.43–1.41)	0.40
LDL	1.03 (0.88–1.22)	0.70
Urine acid	1.31 (1.22–1.45)	<0.001
Hemoglobin	1.00 (0.93–1.08)	0.90
Fibrinogen	1.28 (1.10–1.47)	0.001
Proteinuria	1.13 (1.08–1.19)	<0.001
Glomerulosclerosis	10.38 (5.26–20.52)	<0.001	4.53 (2.23–9.72)	0.001
TASII	1.24 (1.22–1.25)	<0.001	1.23 (1.21–1.25)	0.001
Oxford classification
M1	2.58 (1.75–3.82)	<0.001
E1	0.91 (0.61–1.37)	0.70
S1	1.30 (0.79–2.12)	0.30
T1	3.62 (2.59–5.04)	<0.001
T2	0.88 (0.64–1.21)	0.40
C1	0.84 (0.59–1.17)	0.50
Glucocorticoid	0.90 (0.64–1.22)	0.60
Immunosuppression	1.16 (0.84–1.59)	0.40
ACEI/ARB	0.73 (0.37–1.43)	0.40

ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blocker; CI, confidence interval; eGFR, estimated glomerular filtration rate; HDL, high-density lipoprotein cholesterol; HR, hazard ratio; IgAN, immunoglobulin A nephropathy; LDL, low-density lipoprotein cholesterol; MAP, mean arterial pressure; sCr, serum creatinine; TASII, time-average systemic immune inflammation index.

Significant variables in univariate analysis (p < 0.10) were included in the multivariate Cox proportional hazards regression model. According to the model selection principle, the model was optimized using backward stepwise regression. The model with the smallest Akaike Information Criterion value was selected. Endpoint: eGFR reduced by 30% or higher compared with the baseline or progression to end-stage kidney disease.

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