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Journal of Peking University (Health Sciences) ›› 2025, Vol. 57 ›› Issue (2): 284-290. doi: 10.19723/j.issn.1671-167X.2025.02.010

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Correlation between streptococcal infection and renal damage in children with Henoch-Schönlein purpura nephritis

Ziwei WANG1, Min LI1, Hui GAO1,2, Fang DENG1,2,3,*()   

  1. 1. Department of Pediatrics, the First Affiliated Hospital of Anhui Medical University, Hefei 230032, China
    2. Key Laboratory of Ministry of Education for Birth Population Health, Hefei 230032, China
    3. Department of Nephrology, Anhui Provincial Children's Hospital, Hefei 230051, China
  • Received:2021-08-04 Online:2025-04-18 Published:2025-04-12
  • Contact: Fang DENG E-mail:doczhhxyy@yeah.net
  • Supported by:
    the Key Research and Development Projects of Anhui Province(202004j07020028)

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Abstract:

Objective: To explore whether streptococcal infection may aggravate renal damage in children with Henoch-Schönlein purpura nephritis and its possible mechanism. Methods: In the study, 485 children diagnosed with Henoch-Schönlein purpura nephritis from July 2015 to December 2019 were selected to analyze their clinical data retrospectively. According to the diagnosis of discharge, whether it was combined with streptococcal infection, the children were divided into two groups. The experimental group contained 91 children with Henoch-Schönlein purpura nephritis combined with streptococcal infection, and there were 394 children who were not infected with Streptococcus in the control group. Suitable test items were preliminarily selected through artificial neural network, and then data analysis was performed through SPSS 23.0. Results: The children with Henoch-Schönlein purpura nephritis infected with streptococcus had statistically significant differences compared with the uninfected children in the test items of urine protein, liver and kidney function, immunoglobulin and complement. Anti-streptolysin O had mild correlation with IgG (Spearman r=-0.328), fibrin degradation products (Spearman r=-0.207), total protein (Spearman r=-0.202) and globulin (Spearman r=-0.223). Compared with the children who were not infected with streptococcus, the differences of the average levels of age (P=0.001), IgG (P < 0.001), fibrin degradation products (P=0.019), total protein (P < 0.001), globulin (P < 0.001), IgA (P < 0.001), IgM (P=0.003), complement 3 (P=0.016), complement 4 (P=0.002), albumin/globulin ratio (P=0.007), alkaline phosphatase (P=0.036), and estimated glomerular filtration rate (P=0.039) in the infected children were statistically significant. In order to explore the risk factors of kidney damage in the children with Henoch-Schönlein purpura nephritis, Logistic regression was performed using anti-streptolysin O, age, immunoglobulin and complement as independent variables, urine protein detection parameters, liver and kidney functions as dependent variables. Age ≤10 years old and hypocomplementemia might be risk factors for aggravating renal damage in the children with Henoch-Schönlein purpura nephritis. Conclusion: Streptococcal infections may aggravate renal damage in children with Henoch-Schönlein purpura nephritis, in which hypocomplementemia, inflammation, fibrinolysis and disorders of coagulation perhaps play an important role. Children with streptococcal infection should be treated with anti-infective treatment in time and necessarily, and followed up after discharge regularly.

Key words: Henoch-Schönlein purpura nephritis, Streptococcal infections, Renal damage, Child

CLC Number: 

  • R726.9

Figure 1

Artificial neural networks model structure"

Table 1

Clinical characteristics of children with HSPN"

Feature Total(n=485) HSPN-ASO+(n=91) HSPN-ASO-(n=394) χ2/t P
Gender, n (%) 2.719 0.099
  Male 288 (59.38) 61 (67.03) 227 (57.61)
  Female 197 (40.62) 30 (32.97) 167 (42.39)
Age/years, ${\bar x}$±s 8.67±3.19 9.64±3.19 8.42±3.18 3.297 0.001
Purpura, n (%) 485 (100.00) 91 (100.00) 394 (100.00)
Joint pain/Edema, n (%) 97 (20.00) 24 (26.37) 73 (18.53) 2.804 0.094
Abdominal pain/Gastrointestinal reaction, n (%) 94 (19.38) 17 (18.68) 77 (19.51) 0.039 0.843
Kidney damage, n (%) 485 (100.00) 91 (100.00) 394 (100.00)

Figure 2

Classification accuracy of different test items"

Table 2

Mann-Whitney U test, Spearman rank correlation and artificial neural networks classification accuracy of the correlation between ASO and immunoglobulin and complement"

Items HSPN-ASO+ HSPN-ASO- Spearman r Classification accuracy/%
Number of cases 86 368
IgG/(g/L), ${\bar x}$±s 11.42±3.64* 8.49±2.70 -0.328# 66.356
IgA/(g/L), ${\bar x}$±s 2.40±0.84* 2.07±0.90 -0.157# 60.523
IgM/(g/L), ${\bar x}$±s 1.32±0.41* 1.21±0.52 -0.138# 57.475
IgE/(IU/L), ${\bar x}$±s 118.87±171.61 111.24±165.43 0.023 48.466
C3/(g/L), ${\bar x}$±s 1.12±0.25* 1.18±0.20 0.116# 58.443
C4/(g/L), ${\bar x}$±s 0.22±0.11* 0.25±0.09 0.142# 57.964

Table 3

Mann-Whitney U test, Spearman rank correlation and artificial neural networks classification accuracy of the correlation between ASO and urine protein test"

Items HSPN-ASO+ HSPN-ASO- Spearman r Classification accuracy/%
Number of cases 25 105
Transferrin/(mg/L), ${\bar x}$±s 22.87±27.35 26.37±29.00 0.014 56.586
Retinol binding protein/(mg/L), ${\bar x}$±s 0.26±0.16 0.31±0.29 0.008 53.605
NAG/(U/L), ${\bar x}$±s 9.52±10.78 11.80±11.07 0.152 57.871
Albumin/(mg/L), ${\bar x}$±s 351.16±493.69 449.35±560.77 0.045 61.158
IgG/(mg/L), ${\bar x}$±s 40.77±86.83 43.17±84.61 0.091 59.135
FDP/(mg/L), ${\bar x}$±s 0.37±0.24* 0.25±0.18 -0.207# 61.960
Cystatin C/(mg/L), ${\bar x}$±s 0.15±0.09 0.20±0.13 0.155 64.374
Urine creatinine/(mmol/L), ${\bar x}$±s 8.17±4.15 9.32±4.47 0.105 53.544
β2-microglobulin/(mg/L), ${\bar x}$±s 0.40±0.24 0.52±0.48 0.007 60.343
α1-microglobulin/(mg/L), ${\bar x}$±s 8.02±9.09 9.00±10.22 0.096 58.065

Table 4

Mann-Whitney U test, Spearman rank correlation and artificial neural networks classification accuracy of the correlation between ASO and liver and kidney function"

Items HSPN-ASO+ HSPN-ASO- Spearman r Classification accuracy/%
Number of cases 59 288
Total protein/(g/L), ${\bar x}$±s 71.81±5.93* 68.03±6.78 -0.202# 60.029
ALB/(g/L), ${\bar x}$±s 43.11±3.60 42.29±4.51 -0.062 52.104
GLO/(g/L), ${\bar x}$±s 28.70±5.72* 25.71±3.76 -0.223# 61.887
ALB/GLO, ${\bar x}$±s 1.56±0.35* 1.67±0.25 0.145# 56.768
TBIL/(μmol/L), ${\bar x}$±s 10.03±4.09 9.39±3.74 -0.044 51.106
DBIL/(μmol/L), ${\bar x}$±s 3.39±1.48 3.14±1.42 -0.055 51.219
IBIL/(μmol/L), ${\bar x}$±s 6.65±2.83 6.26±2.58 -0.040 50.297
ALT/(U/L), ${\bar x}$±s 16.27±10.52 17.55±13.16 0.024 50.734
AST/(U/L), ${\bar x}$±s 20.42±8.10 19.99±6.67 -0.017 49.098
ALP/(U/L), ${\bar x}$±s 194.17±60.94* 178.39±70.48 -0.113# 54.103
GGT/(U/L), ${\bar x}$±s 12.59±7.44 13.35±7.96 0.043 50.228
LDH/(U/L), ${\bar x}$±s 229.07±45.96 244.68±71.08 0.064 52.202
PALB/(mg/L), ${\bar x}$±s 233.36±76.15 251.71±92.74 0.063 51.509
TBA/(μmol/L), ${\bar x}$±s 4.37±3.32 4.97±3.99 0.050 51.243
Urea/(mmol/L), ${\bar x}$±s 4.42±1.63 4.21±1.20 -0.031 49.655
CRE/(μmol/L), ${\bar x}$±s 45.15±15.30* 40.03±10.08 -0.135# 55.792
UA/(μmol/L), ${\bar x}$±s 270.75±90.38 248.99±73.39 -0.080 51.613
CYC/(mg/L), ${\bar x}$±s 1.00±0.31 0.96±0.19 -0.071 52.520
eGFR/[mL/(min·1.73 m2)], ${\bar x}$±s 171.59±23.49* 179.07±21.55 0.111# 55.245

Table 5

Statistically significant results of Logistic regression model"

Dependent variable Independent factors B SE Wald P OR 95%CI
α1-MG* IgE* 2.161 0.800 7.300 0.007 8.678 1.810-41.610
C3# 3.742 1.516 6.094 0.014 42.181 2.162-823.013
Total protein# C3# 2.436 0.743 10.747 0.001 11.429 2.664-49.044
ALP* Age≤10 years 0.743 0.349 4.527 0.033 2.102 1.060-4.167
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