The relationship between serum lipid profiles and related clinicopathologic features of IgA nephropathy (IgAN) and c-Maf-inducing protein (CMIP) gene polymorphisms is unclear. The present study was designed to examine the effect of CMIP single-nucleotide polymorphisms (SNPs) on dyslipidaemia and clinicopathologic features of IgAN. Clinical and pathological data from patients with IgAN diagnosed at the First Affiliated Hospital of Guangxi Medical University were collected. DNA was extracted from blood samples. CMIP rs2925979 and CMIP rs16955379 genotypes were determined by PCR and direct sequencing. Among 543 patients, 281 had dyslipidaemia (51.7%). Compared with the non-dyslipidaemia group, the dyslipidaemia group exhibited higher blood pressure, blood urea nitrogen, uric acid, and body mass index; higher prevalence of oedema, haematuria, tubular atrophy, and interstitial fibrosis; and lower albumin and estimated glomerular filtration rate. In the dyslipidaemia group, the frequency of C allele carriers was higher than that of non-C allele carriers for rs16955379. Multivariate linear regression analysis showed that total cholesterol, low-density lipoprotein and high-density lipoprotein were associated with rs16955379C allele carriers. Apolipoprotein B was associated with A allele carriers of rs2925979. Linkage disequilibrium was observed between rs16955379 and rs2925979, and rs2925979G-rs16955379T was the most common haplotype. The frequencies of the four CMIP SNP haplotypes differed between dyslipidaemia and non-dyslipidaemia groups in IgAN (P<0.05, for all above). Dyslipidaemia is a common complication in IgAN patients, and those with dyslipidaemia present poor clinicopathologic features. CMIP SNPs and their haplotypes are closely correlated with the occurrence of dyslipidaemia and clinicopathologic damage in IgAN patients.

IgA nephropathy (IgAN) is the most common primary glomerular disease worldwide [1]. Approximately 15–40% of patients with IgAN eventually develop end-stage renal disease (ESRD) [2], which requires renal replacement therapy, and these patients have a poor prognosis. IgAN is a complex disease that is affected by multiple factors [3,4]. Moreover, the clinical and pathological manifestations of IgAN are diverse. Many studies have suggested that genetic factors play an important role in the occurrence and development of IgAN [5,6]. Gene polymorphisms are important factors affecting the clinicopathologic features and prognosis of IgAN [7–9].

Cardiovascular disease (CVD) is the primary reason for death and the most common complication in patients with IgAN and chronic renal failure or ESRD. Dyslipidaemia is the primary risk factor for atherosclerosis, which is the most common cause of CVD. Patients with IgAN and dyslipidaemia are more prone to suffer from cardiovascular complications [10]. However, there is very limited research on the genetic background and clinicopathologic features of dyslipidaemia susceptibility in patients with IgAN. The c-Maf-inducing protein (CMIP) was recently found to be closely related to total cholesterol (TC), low-density lipoprotein (LDL-C), and high-density lipoprotein (HDL-C) levels [11,12]. The relationships between dyslipidaemia, clinicopathologic features, and CMIP single-nucleotide polymorphisms (SNPs) and their haplotypes have rarely been reported. The present study was designed to investigate the effects of CMIP SNPs (CMIP rs2925979 and CMIP rs16955379) in patients with IgAN and dyslipidaemia and to explore the clinicopathologic features associated with CMIP SNPs in patients with IgAN. These findings will help to identify potential predictive biomarkers of dyslipidaemia complications and to predict clinicopathologic prognosis in IgAN patients.

Subjects

All subjects were diagnosed with IgAN by renal biopsy at the First Affiliated Hospital of Guangxi Medical University from August 2010 to December 2017. The inclusion criteria were: (1) age ≥16 years old and (2) renal pathological diagnosis indicating IgAN. The exclusion criteria were: (1) secondary IgAN (e.g., hepatitis B, allergic purpura, lupus, cirrhosis, rheumatoid arthritis, tumours, multiple myelomas, and human immunodeficiency virus); (2) serious liver or heart failure; (3) use of steroid hormones, immunosuppressive medicine, or lipid-lowering therapy in the past 1 month; and (4) acute cardiovascular and cerebrovascular diseases. The present study was approved by the ethics committee of the First Affiliated Hospital of Guangxi Medical University (approval number: 2019KY-E-006). The purpose of the present study was explained to all patients, who provided written informed consent.

Clinical and pathological data

Questionnaires were used to collect general information, including name, sex, age, smoking status, drinking status, and medical history (e.g., disease complications, such as diabetes, hypertension, hyperlipidaemia, and medication history). A physical examination was performed in which blood pressure, pulse, height, and weight were measured. Body mass index (BMI) was also calculated. Pathological data were recorded using the Oxford classification of IgAN based on renal biopsy findings.

Specimen collection and lab tests

Fasting venous blood samples (5 ml) were obtained from all participants and were used to evaluate kidney function [blood urea nitrogen (BUN), serum creatinine (Scr), uric acid (UA), and estimated glomerular filtration rate (eGFR)], blood lipid levels, and other biochemical indicators. Blood lipid indexes included TC, triglyceride (TG), HDL-C, LDL-C, apolipoprotein A1 (ApoA1), and apolipoprotein B (ApoB), which were measured in the laboratory at the First Affiliated Hospital of Guangxi Medical University. The ApoA1-to-ApoB ratio was also calculated. Blood samples used to measure serum biochemical indexes were immediately cryopreserved at −80°C. DNA was subsequently extracted from the blood samples. Morning urine samples (5 ml) were used for routine urine testing. We also collected 24-h urine volume for urine protein quantification.

Diagnostic criteria

IgAN diagnosis was based on renal findings, especially immunofluorescence microscopy. IgAN is defined by the presence of IgA-dominant or co-dominant immune deposits within the glomerulus, particularly in the mesangial area [13,14]. Using reference levels from lipid profiles of 2007 Chinese adults, dyslipidaemia was defined as TC levels greater than 6.22 mmol/l, LDL-C levels greater than 4.14 mmol/l, HDL-C levels less than 1.04 mmol/l, and TG levels greater than 2.26 mmol/l [15–17]. Systolic blood pressure (SBP) greater than 140 mmHg (1 mmHg = 0.133 kpa) and/or diastolic blood pressure (DBP) greater than 90 mmHg was diagnosed as hypertension [18–20]. Renal dysfunction was defined as an eGFR<60 ml/min/1.73 m2. The Oxford pathological classification of IgAN was used to assess mesangial cell proliferation (M1 defined as having 50% of the glomerular mesangial area exceeding three mesangial cells and M0 as otherwise), capillary hyperplasia (E1 was defined as cell proliferation in glomerular capillaries causing narrowing of the cavity and E0 as otherwise), segmental glomerular sclerosis (S1 was defined as having loops affected to any degree, without involvement of the entire glomerulus or adhesion and S0 as otherwise), renal tubular atrophy/interstitial fibrosis (T0 was defined as 0–25%, T1 defined as 26–50%, and T2 was defined as >50%), and crescent (C0 was defined as 0%, C1 was defined as 0–25%, and was C2 defined as ≥25%) [21].

Selection of SNPs

Haploview 4.2 software (http://www.broadinstitute.org/haploview/haploview) was used, and the Tagger program was run to choose SNPs. SNP information was then obtained from the National Center for Biotechnology Information (NCBI) SNP database (http://www.ncbi.nlm.nih.gov/SNP/). The minimum allele frequency (MAF) of the selected SNPs was >1%. A search of the literature showed that selected SNPs may be related to lipid metabolism [12]. Based on these factors, we chose CMIP rs16955379 and rs2925979 SNPs as the label loci.

Genotyping

Genomic DNA was separated from peripheral blood leukocytes using phenol chloroform extraction. Using gene sequences in the NCBI database and Primer 5.0 software (Premier Company, North York, Canada), we designed specific primers. Primer pair sequences were synthesized by Shanghai Sangon Biology Engineering Technology and Service Co., Ltd. (Shanghai, China) for CMIP rs16955379F (5′GGGATTGCGTACATGGTGTC3′), CMIP rs16955379R (5′TGTGCTGTCTCGAAGGTGAT3′), CMIP rs2925979F (5′CAAGGAGCCCGATACAATGC3′), and CMIP rs2925979R (5′GGAGGAAGGGAAGGACAGAG3′). Extracted DNA was subjected to PCR amplification and electrophoresis imaging to confirm presence of PCR products. A gel imaging system was used to capture images for analysis (electrophoresis gel of PCR products shown in Figure 1). All PCR products were sent to the Shanghai Sangon Biology Engineering Technology and Service Co., Ltd. for direct sequencing to confirm the genotype. Partial nucleotide sequences for the genotypes are shown in Figure 2.

Agarose gel electrophoresis of the PCR products

Figure 1
Agarose gel electrophoresis of the PCR products

(A) The CMIP rs16955379 SNP. Lane M, 100 bp marker ladder; lanes 1–10, the PCR products from 10 different DNA samples (299 bp). (B) The CMIP rs2925979 SNP. Lane M, 100 bp marker ladder; lanes 1–10, the PCR products from 10 different DNA samples (619 bp).

Figure 1
Agarose gel electrophoresis of the PCR products

(A) The CMIP rs16955379 SNP. Lane M, 100 bp marker ladder; lanes 1–10, the PCR products from 10 different DNA samples (299 bp). (B) The CMIP rs2925979 SNP. Lane M, 100 bp marker ladder; lanes 1–10, the PCR products from 10 different DNA samples (619 bp).

The genotypes of the CMIP rs16955379 (CC, CT and TT) and rs2925979 (AA, AG and GG) by direct sequencing

Figure 2
The genotypes of the CMIP rs16955379 (CC, CT and TT) and rs2925979 (AA, AG and GG) by direct sequencing
Figure 2
The genotypes of the CMIP rs16955379 (CC, CT and TT) and rs2925979 (AA, AG and GG) by direct sequencing

Statistical analysis

All statistical analyses were performed using SPSS, version 19.0 (SPSS, Chicago, IL, U.S.A.). Data with a normal distribution are presented as the mean ± standard deviation (SD) or as percentages. Data with a non-normal distribution are presented as the median with quartiles. Qualitative data (e.g., sex, smoking, drinking, and pathological stages) are presented as percentages. Clinical indicators between dyslipidaemia and non-dyslipidaemia groups and blood lipid levels in different genotype carriers were compared using a t-test. Pathological scores between the dyslipidaemia and non-dyslipidaemia groups and the genotype distribution between the different genotype carriers were compared using chi-square test. The association between genotypes and the Oxford pathological classification was evaluated by analysis of covariance (ANCOVA). Hardy–Weinberg equilibrium for the genotype, linkage disequilibrium (LD) calculation, and haplotype analysis were performed using SHE-sis online software (http://analysis.bio-x.cn/myAnalysis.php). The influence of lipid level factors was analysed using a multiple linear regression analysis. The level of statistical significance was set at P<0.05.

Comparison of clinicopathologic indexes between the dyslipidaemia and non-dyslipidaemia groups of patients with IgAN

There were 543 patients with IgAN and complete data who were included in the study, with a male-to-female ratio of 0.905:1, mean age (mean ± SD) of 35.23±11.72 years, and age range of 15–82 years. Among study participants, 281 (51.7%) patients had dyslipidaemia.

The clinical features and pathological characteristics of patients in dyslipidaemia and non-dyslipidaemia groups were compared. The dyslipidaemia group exhibited higher weight, BMI, TC, TG, LDL-C, SBP, BUN, UA, ratio of drinking, ratio of oedema, ratio of microscopic haematuria, ratio of tubular atrophy, and interstitial fibrosis than did the non-dyslipidaemia group (P<0.05, for each). ApoA1, eGFR, haemoglobin (Hb), and albumin (Alb) were lower in the dyslipidaemia group than in the non-dyslipidaemia group (P<0.05, for each). There were no significant differences in age, gender ratio, HDL-C, ApoB, Glu, Scr, 24 h-proteinuria, smoking ratio, mesangial cell proliferation, segmental glomerulosclerosis, and crescents between the dyslipidaemia and non-dyslipidaemia groups (P>0.05, for each).

CMIP SNPs genotype and allele frequency comparison between dyslipidaemia and non-dyslipidaemia groups

Tables 1 and 2 show CMIP genotype and allele frequency comparisons between the dyslipidaemia and non-dyslipidaemia groups in patients with IgAN. A significant difference was observed between the dyslipidaemia and non-dyslipidaemia groups in CMIP rs16955379 CC, CT, and TT genotype frequencies (P<0.05). Furthermore, C allele frequencies were higher than T allele frequencies for CMIP rs16955379 (P<0.05). There was no significant difference between CMIP rs2925979 AA, AG, or GG genotype frequencies, or A and G allele frequencies in dyslipidaemia and non-dyslipidaemia groups (all P>0.05).

Table 1
Comparison of the CMIP genotype frequencies between the dyslipidaemia and non-dyslipidaemia groups in patients with IgAN
GroupNGenotype [n (%)]Allele [n (%)]
CCCTTTCT
rs16955379       
Non-dyslipidaemia 262 169 (64.6) 83 (31.7) 10 (3.7) 211 (80.5) 51 (19.5) 
Dyslipidaemia 281 128 (45.6) 124 (44.1) 29 (10.3) 190 (67.6) 91 (32.4) 
χ2  7.270   6.481  
P  0.026   0.011  
PHWE  0.931     
GroupNGenotype [n (%)]Allele [n (%)]
CCCTTTCT
rs16955379       
Non-dyslipidaemia 262 169 (64.6) 83 (31.7) 10 (3.7) 211 (80.5) 51 (19.5) 
Dyslipidaemia 281 128 (45.6) 124 (44.1) 29 (10.3) 190 (67.6) 91 (32.4) 
χ2  7.270   6.481  
P  0.026   0.011  
PHWE  0.931     
Table 2
Comparison of the CMIP allele frequencies between the dyslipidaemia and non-dyslipidaemia groups in patients with IgAN
GroupnGenotype [n (%)]Allele [n (%)]
AAAGGGAG
rs2925979       
Non-dyslipidaemia 262 62 (23.7) 104 (39.5) 96 (36.8) 114 (43.4) 148 (56.6) 
Dyslipidaemia 281 79 (28.1) 141 (50.0) 61 (21.9) 149 (53.1) 132 (46.9) 
χ2  1.865   1.311  
P  0.394   0.252  
PHWE  0.346     
GroupnGenotype [n (%)]Allele [n (%)]
AAAGGGAG
rs2925979       
Non-dyslipidaemia 262 62 (23.7) 104 (39.5) 96 (36.8) 114 (43.4) 148 (56.6) 
Dyslipidaemia 281 79 (28.1) 141 (50.0) 61 (21.9) 149 (53.1) 132 (46.9) 
χ2  1.865   1.311  
P  0.394   0.252  
PHWE  0.346     

Comparison of lipid levels and clinicopathologic features in patients with IgAN and different genotypes

Tables 3 and 4 show a comparison of blood lipid levels and clinicopathologic features in patients with IgAN and different CMIP genotypes. These results indicate: (1) CMIP rs16955379 patients carrying the C allele (CC and CT genotypes) have higher levels of HDL-C, SBP, DBP, pulse pressure, ratio of hypertension than those that do not carry the C allele (TTgenotype), while renal function decline, degrees of mesangial cell proliferation, capillary hyperplasia, and tubular atrophy/interstitial fibrosis than TT genotype (P<0.05, for each); (2) CMIP rs2925979 patients carrying the A allele (AA and AG genotypes) have higher levels of TC, DBP, BUN, Scr, UA, urine protein, ratio of hypertension, ratio of renal function decline, degree of mesangial cell proliferation, segmental glomerulosclerosis, and tubular atrophy/interstitial fibrosis than those that do not carry the A allele (GG genotype) (P<0.05, for each).

Table 3
Comparison of serum lipid levels and clinicopathologic features among different rs16955379 genotypes in patients with IgAN
ParameterCC + CTTTt (χ2)P
Male/female 318/186 16/23 3.046 0.081 
Age (year) 35.52 ± 11.88 31.40 ± 8.94 1.315 0.190 
BMI (kg/m222.88 ± 3.26 23.87 ± 3.75 -1.118 0.265 
TC (mmol/l) 5.12 ± 2.31 5.37 ± 1.64 -0.403 0.687 
TG (mmol/l) 1.85 ± 1.38 1.66 ± 1.26 2.345 0.042 
HDL-C (mmol/l) 1.29 ± 0.46 1.00 ± 0.28 2.374 0.019 
LDL-C (mmol/l) 3.01 ± 1.96 3.89 ± 1.49 -0.243 0.808 
ApoA1 (g/l) 1.30 ± 0.55 1.29 ± 0.07 0.029 0.977 
ApoB (g/l) 0.97 ± 0.51 0.76 ± 0.02 1.020 0.310 
ApoA1/ApoB 1.51 ± 0.67 1.70 ± 0.14 -0.707 0.481 
SBP (mmHg) 129.23 ± 19.19 116.20 ± 8.24 5.145 <0.0001 
DBP (mmHg) 78.95 ± 13.69 73.40 ± 6.33 2.913 0.007 
BUN (mmol/l) 6.03 ± 2.82 5.90 ± 1.81 0.179 0.858 
Scr (μmol/l) 108.86 ± 73.52 88.00 ± 44.59 1.082 0.281 
UA (μmol/l) 384.91 ± 123.43 417.40 ± 165.04 -0.957 0.339 
eGFR (ml/min/1.73 m282.83 ± 38.77 100.38 ± 34.37 -1.702 0.090 
Alb (g/l) 26.75 ± 3.90 28.04 ± 5.33 -0.981 0.328 
Hypertension [n (%)] 168 (33.3) 3 (7.7) 11.031 0.001 
Renal function decline [n (%)] 204 (40.5) 31 (80.0) 22.441 <0.0001 
Microscopic hematuria [n (%)] 457(90.6) 36 (92.3) 0.003 0.958 
24-h proteinuria (g/d) 1.59 ± 1.66 2.10 ± 2.89 -0.604 0.558 
Degree of urine protein   5.593 0.052 
  Mild: < 1 g/d 264 (52.3) 19 (46.6)   
  Moderate: 1–3.5 g/d 178 (35.4) 10 (26.7)   
  Heavy: ≥ 3.5 g/d 62 (12.3) 10 (26.7)   
Mesangial cell proliferation   10.437 0.001 
  M0 336 (66.7) 16 (40.0)   
  M1 168 (33.3) 23 (60.0)   
Hyperplasia of capillaries   18.762 <0.0001 
  E0 453 (89.8) 26 (66.7)   
  E1 51 (10.2) 13 (33.3)   
Segmental glomerulosclerosis   2.829 0.093 
  S0 227 (45.1) 23 (60.0)   
  S1 277 (54.9) 16 (40.0)   
IFTA   34.388 <0.0001 
  T0 367 (72.8) 11 (26.7)   
  T1 108 (21.5) 23 (60.0)   
  T2 29 (5.7) 5 (13.3)   
Crescents   2.459 0.292 
  C0 434 (86.1) 30 (76.9)   
  C1 47 (9.3) 6 (15.4)   
  C2 23 (4.6) 3 (7.7)   
ParameterCC + CTTTt (χ2)P
Male/female 318/186 16/23 3.046 0.081 
Age (year) 35.52 ± 11.88 31.40 ± 8.94 1.315 0.190 
BMI (kg/m222.88 ± 3.26 23.87 ± 3.75 -1.118 0.265 
TC (mmol/l) 5.12 ± 2.31 5.37 ± 1.64 -0.403 0.687 
TG (mmol/l) 1.85 ± 1.38 1.66 ± 1.26 2.345 0.042 
HDL-C (mmol/l) 1.29 ± 0.46 1.00 ± 0.28 2.374 0.019 
LDL-C (mmol/l) 3.01 ± 1.96 3.89 ± 1.49 -0.243 0.808 
ApoA1 (g/l) 1.30 ± 0.55 1.29 ± 0.07 0.029 0.977 
ApoB (g/l) 0.97 ± 0.51 0.76 ± 0.02 1.020 0.310 
ApoA1/ApoB 1.51 ± 0.67 1.70 ± 0.14 -0.707 0.481 
SBP (mmHg) 129.23 ± 19.19 116.20 ± 8.24 5.145 <0.0001 
DBP (mmHg) 78.95 ± 13.69 73.40 ± 6.33 2.913 0.007 
BUN (mmol/l) 6.03 ± 2.82 5.90 ± 1.81 0.179 0.858 
Scr (μmol/l) 108.86 ± 73.52 88.00 ± 44.59 1.082 0.281 
UA (μmol/l) 384.91 ± 123.43 417.40 ± 165.04 -0.957 0.339 
eGFR (ml/min/1.73 m282.83 ± 38.77 100.38 ± 34.37 -1.702 0.090 
Alb (g/l) 26.75 ± 3.90 28.04 ± 5.33 -0.981 0.328 
Hypertension [n (%)] 168 (33.3) 3 (7.7) 11.031 0.001 
Renal function decline [n (%)] 204 (40.5) 31 (80.0) 22.441 <0.0001 
Microscopic hematuria [n (%)] 457(90.6) 36 (92.3) 0.003 0.958 
24-h proteinuria (g/d) 1.59 ± 1.66 2.10 ± 2.89 -0.604 0.558 
Degree of urine protein   5.593 0.052 
  Mild: < 1 g/d 264 (52.3) 19 (46.6)   
  Moderate: 1–3.5 g/d 178 (35.4) 10 (26.7)   
  Heavy: ≥ 3.5 g/d 62 (12.3) 10 (26.7)   
Mesangial cell proliferation   10.437 0.001 
  M0 336 (66.7) 16 (40.0)   
  M1 168 (33.3) 23 (60.0)   
Hyperplasia of capillaries   18.762 <0.0001 
  E0 453 (89.8) 26 (66.7)   
  E1 51 (10.2) 13 (33.3)   
Segmental glomerulosclerosis   2.829 0.093 
  S0 227 (45.1) 23 (60.0)   
  S1 277 (54.9) 16 (40.0)   
IFTA   34.388 <0.0001 
  T0 367 (72.8) 11 (26.7)   
  T1 108 (21.5) 23 (60.0)   
  T2 29 (5.7) 5 (13.3)   
Crescents   2.459 0.292 
  C0 434 (86.1) 30 (76.9)   
  C1 47 (9.3) 6 (15.4)   
  C2 23 (4.6) 3 (7.7)   

Abbreviations: Alb, albumin; ApoA1, apolipoprotein A1; ApoB, apolipoprotein B; ApoA1/ApoB, ratio of ApoA1 to ApoB; BMI, body mass index; BUN, blood urea nitrogen; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; HDL-C, high-density lipoprotein; IFTA, tubular atrophy/interstitial fibrosis; LDL-C, low-density lipoprotein; Scr, serum creatinine; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride; UA, uric acid.

Table 4
Comparison of serum lipid levels and clinicopathologic features among different rs2925979 genotypes in patients with IgAN
ParameterAA+AGGGt (χ2)P
Male/female 252/134 82/75 3.109 0.078 
Age (year) 34.92 ± 12.14 35.95 ± 10.75 -0.585 0.559 
BMI (kg/m222.94 ± 3.16 22.99 ± 3.64 -0.101 0.919 
TC (mmol/l) 5.32 ± 2.58 4.70 ± 1.78 2.462 0.015 
TG (mmol/l) 1.05(0.8) 1.17(1.1) -1.347 0.178 
HDL-C (mmol/l) 1.26 ± 0.46 1.31 ± 0.45 -0.722 0.471 
LDL-C (mmol/l) 3.09 ± 2.20 2.78± 1.00 1.065 0.288 
ApoA1 (g/l) 1.23 ± 0.32 1.41 ± 0.76 -1.641 0.158 
ApoB (g/l) 1.01 ± 0.61 0.89 ± 0.22 1.625 0.107 
ApoA1/ApoB 1.44 ± 0.57 1.64 ± 0.78 -1.736 0.085 
SBP (mmHg) 129.22 ± 17.69 126.14 ± 21.48 1.083 0.280 
DBP (mmHg) 79.78 ± 13.55 75.71 ± 12.58 2.033 0.043 
BUN (mmol/l) 6.32 ± 2.76 5.33 ± 2.66 2.409 0.017 
Scr (μmol/l) 117.76 ± 81.22 81.85 ± 28.61 4.694 0.000 
UA (μmol/l) 398.51 ± 134.29 360.90 ± 102.87 1.986 0.048 
eGFR (ml/min/1.73 m283.11 ± 39.68 86.36 ± 36.36 -0.558 0.578 
Alb (g/l) 27.42 ± 3.5 28.02 ± 4.63 -0.887 0.236 
Hypertension [n (%)] 134 (34.7) 37 (23.8) 6.423 0.011 
Renal function decline [n (%)] 197 (51.0) 52 (33.3) 14.427 <0.0001 
Microscopic hematuria [n (%)] 360 (93.2) 142 (90.4) 1.270 0.260 
24-hour proteinuria (g/d) 1.79±1.86 1.24 ± 1.46 2.256 0.026 
Degree of urine protein   21.889 <0.0001 
  Mild: < 1 g/d 173 (44.9) 105 (66.7)   
  Moderate: 1–3.5 g/d 158 (40.8) 37 (23.8)   
  Heavy: ≥3.5 g/d 55 (14.3) 15 (9.5)   
Mesangial cell proliferation   7.281 0.007 
  M0 268 (69.4) 90 (57.1)   
  M1 118 (30.6) 67 (42.9)   
Hyperplasia of capillaries   6.147 0.013 
  E0 339 (87.8) 149 (95.2)   
  E1 47 (12.2) 8 (4.8)   
Segmental glomerulosclerosis   35.177 <0.0001 
  S0 150 (38.8) 105 (66.7)   
  S1 236 (61.2) 52 (33.3)   
IFTA   40.714 <0.0001 
  T0 252 (65.3) 141 (90.0)   
  T1 118 (30.6) 8 (5.0)   
  T2 16 (4.1) 8 (5.0)   
Crescents   3.542 0.170 
  C0 331 (85.8) 142 (90.4)   
  C1 39 (10.1) 8 (5.1)   
  C2 16 (4.1) 7 (4.5)   
ParameterAA+AGGGt (χ2)P
Male/female 252/134 82/75 3.109 0.078 
Age (year) 34.92 ± 12.14 35.95 ± 10.75 -0.585 0.559 
BMI (kg/m222.94 ± 3.16 22.99 ± 3.64 -0.101 0.919 
TC (mmol/l) 5.32 ± 2.58 4.70 ± 1.78 2.462 0.015 
TG (mmol/l) 1.05(0.8) 1.17(1.1) -1.347 0.178 
HDL-C (mmol/l) 1.26 ± 0.46 1.31 ± 0.45 -0.722 0.471 
LDL-C (mmol/l) 3.09 ± 2.20 2.78± 1.00 1.065 0.288 
ApoA1 (g/l) 1.23 ± 0.32 1.41 ± 0.76 -1.641 0.158 
ApoB (g/l) 1.01 ± 0.61 0.89 ± 0.22 1.625 0.107 
ApoA1/ApoB 1.44 ± 0.57 1.64 ± 0.78 -1.736 0.085 
SBP (mmHg) 129.22 ± 17.69 126.14 ± 21.48 1.083 0.280 
DBP (mmHg) 79.78 ± 13.55 75.71 ± 12.58 2.033 0.043 
BUN (mmol/l) 6.32 ± 2.76 5.33 ± 2.66 2.409 0.017 
Scr (μmol/l) 117.76 ± 81.22 81.85 ± 28.61 4.694 0.000 
UA (μmol/l) 398.51 ± 134.29 360.90 ± 102.87 1.986 0.048 
eGFR (ml/min/1.73 m283.11 ± 39.68 86.36 ± 36.36 -0.558 0.578 
Alb (g/l) 27.42 ± 3.5 28.02 ± 4.63 -0.887 0.236 
Hypertension [n (%)] 134 (34.7) 37 (23.8) 6.423 0.011 
Renal function decline [n (%)] 197 (51.0) 52 (33.3) 14.427 <0.0001 
Microscopic hematuria [n (%)] 360 (93.2) 142 (90.4) 1.270 0.260 
24-hour proteinuria (g/d) 1.79±1.86 1.24 ± 1.46 2.256 0.026 
Degree of urine protein   21.889 <0.0001 
  Mild: < 1 g/d 173 (44.9) 105 (66.7)   
  Moderate: 1–3.5 g/d 158 (40.8) 37 (23.8)   
  Heavy: ≥3.5 g/d 55 (14.3) 15 (9.5)   
Mesangial cell proliferation   7.281 0.007 
  M0 268 (69.4) 90 (57.1)   
  M1 118 (30.6) 67 (42.9)   
Hyperplasia of capillaries   6.147 0.013 
  E0 339 (87.8) 149 (95.2)   
  E1 47 (12.2) 8 (4.8)   
Segmental glomerulosclerosis   35.177 <0.0001 
  S0 150 (38.8) 105 (66.7)   
  S1 236 (61.2) 52 (33.3)   
IFTA   40.714 <0.0001 
  T0 252 (65.3) 141 (90.0)   
  T1 118 (30.6) 8 (5.0)   
  T2 16 (4.1) 8 (5.0)   
Crescents   3.542 0.170 
  C0 331 (85.8) 142 (90.4)   
  C1 39 (10.1) 8 (5.1)   
  C2 16 (4.1) 7 (4.5)   

Abbreviations: ApoA1, apolipoprotein A1; ApoB, apolipoprotein B; ApoA1/ApoB, ratio of ApoA1 to ApoB; BUN, blood urea nitrogen; BMI, body mass index; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; HDL-C, high-density lipoprotein; IFTA, interstitial fibrosis/tubular atrophy; LDL-C, low-density lipoprotein; SBP, systolic blood pressure; Scr, serum creatinine; TC, total cholesterol; TG, triglyceride; UA, uric acid.

Relationship between serum lipid levels and clinicopathologic parameters in patients with IgAN

As shown in Table 5, multiple linear regression analysis showed that serum TC was significantly associated with alanine aminotransferase (ALT), UA, CMIP rs16955379 with the C allele, hypertension, and drinking status. Serum TG were associated with ALT, UA, renal function decline, CMIP rs16955379 with the C allele, drinking status, smoking status, BMI, and renal tubular interstitial disease. Serum HDL-C was associated with ALT, UA, CMIP rs16955379 with the C allele, renal function decline, mesenchymal cell proliferation, and capillary proliferation. Serum LDL-C was associated with ALT, UA, CMIP rs16955379 with the C allele, and hypertension. ApoA1 was associated with BMI and renal function decline. Serum ApoB was associated with renal function decline, increased weight, and CMIP rs16955379 with the C allele. ApoA1/ApoB was associated with renal function decline and hypertension (P<0.05 for all).

Table 5
Multiple linear regression analysis examining the influence of serum lipid levels in patients with IgAN
Lipid parameterRisk factorBSEBetatP
TC (mmol/l) ALT (U/l) 0.011 0.005 0.046 2.424 0.020 
 rs16955379      
 CC+CT 0.481 0.191 0.038 2.524 0.016 
 Hypertension -0.227 0.089 -0.041 -2.549 0.015 
 Drinking 0.374 0.155 0.048 2.415 0.021 
 UA (μmol/l) 0.986 0.024 1.146 41.871 <0.0001 
TG (mmol/l) ALT (U/l) 0.075 0.014 0.495 5.265 <0.0001 
 Renal function decline 0.657 0.314 0.203 2.090 0.043 
 rs16955379      
 CC+CT -1.966 0.720 -0.250 -2.729 0.009 
 Drinking 2.001 0.532 0.421 3.764 0.001 
 Smoking -1.941 0.485 -0.460 -4.001 <0.0001 
 BMI (kg/m20.109 0.048 0.240 2.283 0.028 
 UA (μmol/l) 0.037 0.011 0.118 3.384 0.002 
 IFTA 1.273 0.238 1.169 5.351 <0.0001 
HDL-C (mmol/l) ALT (U/l) -0.008 0.003 -0.187 -2.204 0.033 
 rs16955379      
 CC+CT -0.299 0.143 -0.141 -2.097 0.042 
 UA (μmol/l) -0.567 0.063 -2.936 -8.993 <0.0001 
 Progression of renal function 0.333 0.110 0.346 3.042 0.003 
 Mesangial cell proliferation -0.343 0.037 -1.267 -9.194 <0.0001 
 Hyperplasia of capillaries 0.585 0.055 3.522 10.571 <0.0001 
 rs16955379      
LDL-C (mmol/l) CC+CT -0.483 0.195 -0.044 -2.477 0.018 
 Hypertension 0.223 0.091 0.047 2.460 0.019 
 ALT (U/l) -0.011 0.005 -0.050 -2.219 0.033 
 UA (μmol/l) 1.273 0.238 1.169 5.351 <0.0001 
ApoA1 (g/l) BMI (kg/m20.825 0.066 1.009 12.529 <0.0001 
 Renal function decline -0.277 0.088 -0.252 -3.145 0.003 
ApoB (g/l) Renal function decline 0.131 0.035 0.130 3.700 0.001 
 Weight (kg) -0.004 0.001 -0.102 -2.987 0.005 
 rs2925979      
 AA+AG 0.037 0.011 0.118 3.384 0.002 
ApoA1/ApoB Renal function decline 0.277 0.095 0.206 2.914 0.006 
 Hypertension -0.004 0.002 -0.139 -2.113 0.041 
Lipid parameterRisk factorBSEBetatP
TC (mmol/l) ALT (U/l) 0.011 0.005 0.046 2.424 0.020 
 rs16955379      
 CC+CT 0.481 0.191 0.038 2.524 0.016 
 Hypertension -0.227 0.089 -0.041 -2.549 0.015 
 Drinking 0.374 0.155 0.048 2.415 0.021 
 UA (μmol/l) 0.986 0.024 1.146 41.871 <0.0001 
TG (mmol/l) ALT (U/l) 0.075 0.014 0.495 5.265 <0.0001 
 Renal function decline 0.657 0.314 0.203 2.090 0.043 
 rs16955379      
 CC+CT -1.966 0.720 -0.250 -2.729 0.009 
 Drinking 2.001 0.532 0.421 3.764 0.001 
 Smoking -1.941 0.485 -0.460 -4.001 <0.0001 
 BMI (kg/m20.109 0.048 0.240 2.283 0.028 
 UA (μmol/l) 0.037 0.011 0.118 3.384 0.002 
 IFTA 1.273 0.238 1.169 5.351 <0.0001 
HDL-C (mmol/l) ALT (U/l) -0.008 0.003 -0.187 -2.204 0.033 
 rs16955379      
 CC+CT -0.299 0.143 -0.141 -2.097 0.042 
 UA (μmol/l) -0.567 0.063 -2.936 -8.993 <0.0001 
 Progression of renal function 0.333 0.110 0.346 3.042 0.003 
 Mesangial cell proliferation -0.343 0.037 -1.267 -9.194 <0.0001 
 Hyperplasia of capillaries 0.585 0.055 3.522 10.571 <0.0001 
 rs16955379      
LDL-C (mmol/l) CC+CT -0.483 0.195 -0.044 -2.477 0.018 
 Hypertension 0.223 0.091 0.047 2.460 0.019 
 ALT (U/l) -0.011 0.005 -0.050 -2.219 0.033 
 UA (μmol/l) 1.273 0.238 1.169 5.351 <0.0001 
ApoA1 (g/l) BMI (kg/m20.825 0.066 1.009 12.529 <0.0001 
 Renal function decline -0.277 0.088 -0.252 -3.145 0.003 
ApoB (g/l) Renal function decline 0.131 0.035 0.130 3.700 0.001 
 Weight (kg) -0.004 0.001 -0.102 -2.987 0.005 
 rs2925979      
 AA+AG 0.037 0.011 0.118 3.384 0.002 
ApoA1/ApoB Renal function decline 0.277 0.095 0.206 2.914 0.006 
 Hypertension -0.004 0.002 -0.139 -2.113 0.041 

Abbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; ApoA1, apolipoprotein A1; ApoB, apolipoprotein B; ApoA1/ApoB, ratio of ApoA1 to ApoB; BMI, body mass index; BUN, blood urea nitrogen; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; GLU, glucose; HDL-C, high-density lipoprotein; IFTA, tubular atrophy/interstitial fibrosis; LDL-C, low-density lipoprotein; SBP, systolic blood pressure; Scr, serum creatinine; TC, total cholesterol; TG, triglyceride; UA, uric acid.

Haplotype analysis of CMIP SNPs

The combined effects of CMIP SNPs (CMIP rs2925979 and CMIP rs16955379) between dyslipidaemia and non-dyslipidaemia groups in patients with IgAN were examined using haplotype analysis (Table 6). The haplotype constructed by rs2925979G-rs16955379C was the most common. The haplotype frequencies of rs2925979G-rs16955379T, rs2925979A-rs16955379C, and rs2925979A-rs16955379T in the dyslipidaemia group significantly differed from those in the non-dyslipidaemia group (P<0.05).

Table 6
Haplotype frequencies of the CMIP SNPs
HaplotypeTotal [n (%)]Dyslipidaemia group [n (%)]Non-dyslipidaemia group [n (%)]POR (95% CI)
rs2925979G- rs16955379C 261 (48.0) 144 (55.0) 45 (40.0) – 1.00 
rs2925979A- rs16955379C 141 (26.0) 71 (27.0) 70 (25.0) 0.043 1.86 (1.71 - 3.25) 
rs2925979A- rs16955379T 119 (22.0) 71 (18.0) 79 (28.0) 0.012 2.25 (1.20 - 4.21) 
rs2925979G- rs16955379T 21 (3.8) 4 (1.7) 18 (6.5) 0.042 6.05 (1.09 -3.70) 
HaplotypeTotal [n (%)]Dyslipidaemia group [n (%)]Non-dyslipidaemia group [n (%)]POR (95% CI)
rs2925979G- rs16955379C 261 (48.0) 144 (55.0) 45 (40.0) – 1.00 
rs2925979A- rs16955379C 141 (26.0) 71 (27.0) 70 (25.0) 0.043 1.86 (1.71 - 3.25) 
rs2925979A- rs16955379T 119 (22.0) 71 (18.0) 79 (28.0) 0.012 2.25 (1.20 - 4.21) 
rs2925979G- rs16955379T 21 (3.8) 4 (1.7) 18 (6.5) 0.042 6.05 (1.09 -3.70) 

Linkage disequilibrium analysis of CMIP SNPs in IgAN patients

LD analysis between CMIP rs2925979 and CMIP rs16955379 in patients with IgAN is shown in Figure 3. These data suggest that CMIP rs2925979 and CMIP rs16955379 (r2= 0.305, D=0.720) are linked.

Linkage disequilibrium analysis between the CMIP rs2925979 and rs16955379 SNPs in patients with IgAN

Figure 3
Linkage disequilibrium analysis between the CMIP rs2925979 and rs16955379 SNPs in patients with IgAN
Figure 3
Linkage disequilibrium analysis between the CMIP rs2925979 and rs16955379 SNPs in patients with IgAN

Dyslipidaemia is a common comorbidity in patients with chronic kidney disease (CKD). The prevalence of dyslipidaemia in patients with CKD is approximately 40% [22,23] and in patients with ESRD this prevalence is 60%. Moreover, dyslipidaemia plays an important role in renal function progression, cardiovascular complications, and prognosis in patients with CKD [24]. Our study showed that the prevalence of dyslipidaemia is higher in patients with IgAN (51.7%), and half of the patients examined had dyslipidaemia, similar to the results of other studies [25,26]. Our study also demonstrated that patients with IgAN and dyslipidaemia exhibit higher blood pressure, BMI, and UA levels, greater renal impairment, and more severe renal tubular interstitial damage than do patients without dyslipidaemia. Many studies have suggested that dyslipidaemia is closely related to the pathological manifestations of IgAN [27,28]. A Chinese study examining patients with primary IgAN found that tubulointerstitial atrophy was more serious in the hyperlipidaemia group than in the non-dyslipidaemia group. In another study [29], those with hyperlipidaemia were more likely to have a severe stage of pathologic classification, while non-dyslipidaemia patients were more likely to have a stage I–II classification. Patients with IgAN and dyslipidaemia also have worse clinical symptoms and complications. Dyslipidaemia is an independent risk factor for atherosclerosis and cardiovascular complications in patients with IgAN [30,31]. These research findings all suggest that dyslipidaemia plays an important role in the clinical manifestations, pathological lesions, and worse prognosis of patients with IgAN, which are consistent with the findings of our study. Lipid metabolism disorder can cause renal arteriolar sclerosis, oxidative stress, and inflammation, which may lead to glomerular damage, proteinuria, and renal dysfunction in patients with IgAN. Therefore, effective management of blood lipid levels is beneficial to reduce cardiovascular complications and to improve renal function and prognosis in patients with IgAN. Therefore, an emphasis needs to be placed on controlling lipid levels in patients with IgAN.

Many studies suggest that genetic factors are important in the pathogenesis of IgAN [6], which was shown to be related to genetic susceptibility. There are racial and regional differences in IgAN, which also indicate familial aggregation. Genome-wide association studies have reported many genetic loci that confer susceptibility to IgAN [5,8,9]. As an important factor of renal prognosis and cardiovascular complications in IgAN, SNPs related to lipid metabolism are also associated with the clinicopathologic features and prognosis of IgAN. In recent years, genome-wide association studies have identified over 100 gene loci that are associated with dyslipidaemia. CMIP (c-Maf-inducing protein gene, Gene ID: ID80790, Location: 16q23.2–q23.3) encodes the c-maf-inducing protein, and is located on chromosome 16, 16q23. Recent studies have shown that CIMP is closely associated with TC, LDL-C, and HDL-C levels and with Type 2 diabetes and acute myocardial infarction. It is currently thought that this gene primarily acts on the T-cell signalling pathway [32]. The T-cell receptor (TCR) alpha constant gene encodes the constant region of the T-cell receptor [33]. TCR constant alpha chain (TCRCα) gene polymorphism is associated with IgAN [34]. TCRCα SNPs are associated with susceptibility to IgAN in Chinese people [35]. Moreover, pedigree studies and large-sample repeated studies have confirmed that TRAC variants are associated with susceptibility in patients with IgAN, indicating that T-cell signalling pathways and T-cell receptors play a specific role in lipid metabolism. CMIP is also an important factor in the T-cell signalling pathway, affecting lipid index. One study from Japan showed that the CMIP rs16955379 (C>T,16q23.2) locus may increase the risk of T2DM by affecting blood lipid and blood glucose levels [12]. Another Chinese study indicated that CMIP rs2925979 is significantly correlated with LDL-C and HDL-C levels, and the correlation between CMIP rs2925979 and blood lipid levels showed ethnic and sex specificity [11]. Our results demonstrate that CMIP SNPs are closely associated with serum lipid levels, clinical complications, and pathological manifestations in patients with IgAN. Moreover, our results also suggest that there is LD between rs16955379 and rs2925979, and that the rs2925979G-rs16955379T haplotype significantly increases the risk of dyslipidaemia. These results suggest that genetic factors are important to the pathogenesis, occurrence, and development of IgAN, and CMIP is a susceptibility gene for dyslipidaemia in patients with IgAN.

Our study also showed that CMIP SNPs are associated with serum lipid levels that are closely correlated with clinical manifestations and Oxford pathological classification in patients with IgAN. Our results show that CMIP rs16955379 is associated with the degree of damage in mesangial cell proliferation, capillary hyperplasia, and renal tubular atrophy/interstitial fibrosis; CMIP rs2925979 is associated with the degree of damage in mesangial cell proliferation, segmental glomerular sclerosis, and renal tubular atrophy/interstitial fibrosis. CMIP was not only a susceptibility gene for dyslipidaemia in patients with IgAN but also affected renal progression and prognosis in these patients. IgAN is one of the common causes of CKD. Therefore, CMIP SNPs may be associated with development of CKD. CMIP primarily acts on the T-cell pathway, which is associated with adiponectin levels, insulin resistance, diabetes, and lipid levels. It can also cause metabolic disorder by affecting Wnt signalling and fat cell size. In addition, one study showed that CMIP is associated with kidney disease, especially podocyte-related kidney disease. CMIP up-regulates expression of the pro-apoptotic factor BAX in the NF-κB signalling pathway [36] and promotes apoptosis in podocytes, causing proteinuria and renal progression [37]. A previous study revealed that the CMIP protein is over expressed in podocytes, leading to severe destruction of podocyte structure [38]. An additional study [39] found that the Wilms tumour suppressor gene may protect podocytes by binding to the CMIP promoter in vivo and repressing its transcription. In patients with membranous nephropathy, minimal change nephropathy, or focal segmental glomerular sclerosis, podocytes show a significant increase in CMIP expression, decreased podocyte marker expression, abnormal cell morphology, cell contraction, and decreased adhesion to the collagen matrix, leading to renal proteinuria [40,41]. Some scholars have reported that CMIP may affect cholesterol excretion, causing abnormal lipid metabolism in patients with kidney disease [42]. In addition, abnormal lipid metabolism can lead to a dose-dependent increase in lipid droplets in podocytes and reactive oxygen species generation, causing lysosomal dysfunction, mitochondrial dysfunction, inflammation, and renal fibrosis, resulting in renal damage and dysfunction [43,44]. Therefore, CMIP may cause progression of CKD, including IgAN, via abnormal lipid metabolism-mediated podocyte damage. It is speculated that CMIP SNPs that affect blood lipid levels may also be susceptibility genes for morbidity and progression of IgAN, but this needs to be confirmed by future studies with larger sample sizes and additional SNP sites.

There are some limitations in the present study. First, our sample size was limited, and the study was cross-sectional, which cannot demonstrate a direct cause of all related risk factors including the lipid situation, disease condition, and prognosis of IgAN. Second, the two selected SNP loci were not representative of all CMIP loci. Third, study questionnaires did not include dietary factors, so we could not clarify the effects of diet on dyslipidaemia. In the future, these limitations need to be addressed in a cohort study that includes additional CMIP SNP loci and analysis using different genetic models.

About half of the patients with IgAN presented with dyslipidaemia. The patients with dyslipidaemia had higher blood pressure, poorer renal function, more obvious haematuria, lower serum albumin, and more serious renal tubular interstitial damage. CMIP is an important susceptibility gene for predicting the condition and prognosis of IgAN. CMIP SNPs and their haplotypes are closely associated with dyslipidaemia and are also related to clinical features and pathological damage in patients with IgAN.

The authors declare that there are no competing interests associated with the manuscript.

This work was supported by the National Natural Science Foundation of China [grant numbers 81460169 and 8196030236] and the “Medical Excellence Award” Funded by the Creative Research Development Grant from the First Affiliated Hospital of Guangxi Medical University. This work was made possible through my IACN-ISN-HKSN funded scholarship.

L.P: study design, data collection and analysis, sample collection, experiment conduction, and drafting of the manuscript; R.X.Y: study design, data analysis, and revision of the manuscript; Y.H.L: study design, data and sample collection, revision of the manuscript; M.Q.M: sample collection, experiment conduction, and data collection; and Q.H.Z: data analysis. All authors read and approved the final manuscript.

This study was approved by the ethics committee of the First Affiliated Hospital of Guangxi Medical University and was conducted in accordance with the declaration of Helsinki.

All subjects signed an informed consent before enrolment in the study.

We thank the reviewers for their critical comments.

     
  • ALT

    alanine aminotransferase

  •  
  • ApoA1

    apolipoprotein A1

  •  
  • ApoB

    apolipoprotein B

  •  
  • BMI

    body mass index

  •  
  • BUN

    blood urea nitrogen

  •  
  • CKD

    chronic kidney disease

  •  
  • CMIP

    c-Maf-inducing protein

  •  
  • CVD

    cardiovascular disease

  •  
  • DBP

    diastolic blood pressure

  •  
  • eGFR

    estimated glomerular filtration rate

  •  
  • ESRD

    end-stage renal disease

  •  
  • HDL-C

    high-density lipoprotein

  •  
  • IFTA

    tubular atrophy/interstitial fibrosis

  •  
  • IgAN

    IgA nephropathy

  •  
  • LDL-C

    low-density lipoprotein

  •  
  • SBP

    systolic blood pressure

  •  
  • Scr

    serum creatinine

  •  
  • SNP

    single-nucleotide polymorphism

  •  
  • TC

    total cholesterol

  •  
  • TG

    triglyceride

1.
Fabiano
R.C.
,
Pinheiro
S.V.
and
Simoes
E.S.A.C.
(
2016
)
Immunoglobulin A nephropathy: a pathophysiology view
.
Inflamm. Res.
65
,
757
770
[PubMed]
2.
Zhou
F.D.
,
Zhao
M.H.
,
Zou
W.Z.
,
Liu
G.
and
Wang
H.
(
2009
)
The changing spectrum of primary glomerular diseases within 15 years: a survey of 3331 patients in a single Chinese centre
.
Nephrol. Dial. Transplant.
24
,
870
876
[PubMed]
3.
Wyatt
R.J.
and
Julian
B.A.
(
2013
)
IgA nephropathy
.
N. Engl. J. Med.
368
,
2402
2414
[PubMed]
4.
Magistroni
R.
,
D'Agati
V.D.
,
Appel
G.B.
and
Kiryluk
K.
(
2015
)
New developments in the genetics, pathogenesis, and therapy of IgA nephropathy
.
Kidney Int.
88
,
974
989
[PubMed]
5.
Lin
D.
,
Li
S.
,
Xu
H.
,
Chen
H.
and
Dong
Z.
(
2015
)
Association of uteroglobin G38A gene polymorphism with IgA nephropathy risk: an updated meta-analysis
.
J. Recept. Signal Transduct. Res.
35
,
115
121
[PubMed]
6.
Kiryluk
K.
,
Julian
B.A.
,
Wyatt
R.J.
,
Scolari
F.
,
Zhang
H.
,
Novak
J.
et al.
(
2010
)
Genetic studies of IgA nephropathy: past, present, and future
.
Pediatr. Nephrol.
25
,
2257
2268
[PubMed]
7.
Yeo
S.C.
,
Liu
X.
and
Liew
A.
(
2018
)
Complement factor H gene polymorphism rs6677604 and the risk, severity and progression of IgA nephropathy: A systematic review and meta-analysis
.
Nephrology (Carlton).
23
,
1096
1106
[PubMed]
8.
Wei
L.
,
Fu
R.
,
Liu
X.
,
Wang
L.
,
Wang
M.
,
Yu
Q.
et al.
(
2018
)
Rs1520220 and Rs2195239 Polymorphisms of IGF-1 Gene Associated with Histopathological Grades in IgA Nephropathy in Northwestern Chinese Han Population
.
Kidney Blood Press. Res.
43
,
80
87
[PubMed]
9.
Qi
Y.Y.
,
Zhou
X.J.
,
Cheng
F.J.
,
Hou
P.
,
Zhu
L.
,
Shi
S.F.
et al.
(
2015
)
DEFA gene variants associated with IgA nephropathy in a Chinese population
.
Genes Immun.
16
,
231
237
[PubMed]
10.
Syrjanen
J.
,
Mustonen
J.
and
Pasternack
A.
(
2000
)
Hypertriglyceridaemia and hyperuricaemia are risk factors for progression of IgA nephropathy
.
Nephrol. Dial. Transplant.
15
,
34
42
[PubMed]
11.
Shi
G.Y.
(
2016
)
Association of the CMIP rs2925979 SNP and serum lipid levels in the Guangxi Jing and Han population
,
Guangxi Medical University
12.
Sakai
K.
,
Imamura
M.
,
Tanaka
Y.
,
Iwata
M.
,
Hirose
H.
,
Kaku
K.
et al.
(
2013
)
Replication study for the association of 9 East Asian GWAS-derived loci with susceptibility to type 2 diabetes in a Japanese population
.
PLoS One
8
,
e76317
[PubMed]
13.
Soares
M.F.
(
2016
)
An update on pathology of IgA nephropathy
.
J. Bras. Nefrol.
38
,
435
440
[PubMed]
14.
Roberts
I.S.
(
2014
)
Pathology of IgA nephropathy
.
Nat. Rev. Nephrol.
10
,
445
454
[PubMed]
15.
Yuan
J.H.
,
Pan
F.
,
Chen
J.
,
Chen
C.E.
,
Xie
D.P.
,
Jiang
X.Z.
et al.
(
2017
)
Neuroprotection by plumbagin involves BDNF-TrkB-PI3K/Akt and ERK1/2/JNK pathways in isoflurane-induced neonatal rats
.
J. Pharm. Pharmacol.
69
,
896
906
[PubMed]
16.
Pan
L.
,
Yang
Z.H.
,
Wu
Y.
,
Yin
R.X.
,
Liao
Y.H.
,
Wang
J.W.
et al.
(
2016
)
The prevalence, awareness, treatment and control of dyslipidemia among adults in China
.
Atherosclerosis
248
,
2
9
[PubMed]
17.
Joint Committee for Developing Chinese Guidelines on Prevention and Treatment of Dyslipidemia in Adults
(
2007
)
Chinese guidelines on prevention and treatment of dyslipidemia in adults
.
Zhonghua Xin Xue Guan Bing Za Zhi
35
,
390
419
[PubMed]
18.
Wang
J.G.
(
2015
)
Chinese Hypertension Guidelines
.
Pulse (Basel)
3
,
14
20
[PubMed]
19.
Li
S.
,
Cai
J.
,
Feng
Z.B.
,
Jin
Z.R.
,
Liu
B.H.
,
Zhao
H.Y.
et al.
(
2017
)
BDNF Contributes to Spinal Long-Term Potentiation and Mechanical Hypersensitivity Via Fyn-Mediated Phosphorylation of NMDA Receptor GluN2B Subunit at Tyrosine 1472 in Rats Following Spinal Nerve Ligation
.
Neurochem. Res.
42
,
2712
2729
[PubMed]
20.
Castillo Saiz
A.
,
Timoner Aguilera
J.
and
Guzman Quilo
C.
(
2000
)
Novelties in the management of hypertension according to the new 1999 guide of the WHO
.
Aten. Primaria
25
,
128
129
[PubMed]
21.
Haas
M.
,
Verhave
J.C.
,
Liu
Z.H.
,
Alpers
C.E.
,
Barratt
J.
,
Becker
J.U.
et al.
(
2017
)
A Multicenter Study of the Predictive Value of Crescents in IgA Nephropathy
.
J. Am. Soc. Nephrol.
28
,
691
701
[PubMed]
22.
Liu
Z.S.
and
Liu
D.W.
(
2013
)
Dyslipidemia promotes the progression of chronic kidney disease
.
Chin. Med. J. (Engl.)
126
,
1203
1206
[PubMed]
23.
Khurana
M.
and
Silverstein
D.M.
(
2015
)
Etiology and management of dyslipidemia in children with chronic kidney disease and end-stage renal disease
.
Pediatr. Nephrol.
30
,
2073
2084
[PubMed]
24.
Campese
V.M.
(
2014
)
Dyslipidemia and progression of kidney disease: role of lipid-lowering drugs
.
Clin. Exp. Nephrol.
18
,
291
295
[PubMed]
25.
Liao
Y.
,
He
Y.C.
,
Cao
R.
and
Hu
H.F.
(
2016
)
Retrospective analysis of dyslipidemia patients with IgA nephropathy
.
Chinese J. Integrated Traditional Western Nephrol.
17
,
539
540
26.
Jia
X.Y.
(
2012
)
Primary IgA nephropathy: characteritics of abnormal glucose metabolism, dyslipidemia and treatment
,
Shanghai Jiaotong University
27.
Qin
J.
,
Li
L.
and
Liao
Y.H.
(
2015
)
Clinical and pathological effect of hyperlipidemia on primary IgA nephropathy
.
J. Minimally Invasive Med.
10
,
287
289
28.
Li
Y.Z.
,
Jia
R.H.
and
Liu
H.Y.
(
2011
)
Clinicopathological analysis of IgA nephropathy associated with hyperlipidaemia
.
J. Clin. Nephrol.
11
,
157
159
,
29.
Shan
S.
and
Hua-qi
C.
(
2013
)
The Relationship between Hyperlipidemia and Clinic Pathology of IgA Nephropathy
.
Chin. J. Hemorheol.
268
270
,
30.
Iwasa
Y.
,
Otsubo
S.
,
Ishizuka
T.
,
Uchida
K.
and
Nitta
K.
(
2008
)
Influence of serum high-molecular-weight and total adiponectin on arteriosclerosis in IgA nephropathy patients
.
Nephron Clin. Pract.
108
,
c226
232
[PubMed]
31.
Liang
X.H.
,
Yin
D.M.
and
Zhang
L.
(
2016
)
Prevalence of cardiovascular disease (CVD)in patients with IgA nephropathy (IgAN)and its related risk factors
.
J. Xinjiang Med. University
39
,
207-209+214
32.
Oniszczuk
J.
,
Sendeyo
K.
,
Chhuon
C.
,
Savas
B.
,
Cogne
E.
,
Vachin
P.
et al.
(
2020
)
CMIP is a negative regulator of T cell signaling
.
Cell Mol. Immunol.
17
,
1026
1041
[PubMed]
33.
Li
R.
,
Xue
C.
,
Li
C.
,
Lou
T.
,
Tao
Y.
,
Li
Y.
et al.
(
2009
)
TRAC variants associate with IgA nephropathy
.
J. Am. Soc. Nephrol.
20
,
1359
1367
[PubMed]
34.
Li
P.K.
,
Poon
P.
,
Phil
M.
,
Poon
A.S.
,
Szeto
C.C.
,
Yu
A.W.
et al.
(
1997
)
Association of IgA nephropathy with T-cell receptor constant alpha chain gene polymorphism
.
Am. J. Kidney Dis.
30
,
260
264
[PubMed]
35.
Di-Fei
H.E.
,
Zhi-Quan
Z.
and
Lian-Yi
H.E.
(
2017
)
Clinical analysis on polymorphism of TCRCα-560 C/T gene in patients with IgA nephropathy in Guangxi Zhuang Autonomous Region
.
J. Clin. Exp. Med.
16
,
58
60
36.
Strawbridge
R.J.
,
Laumen
H.
,
Hamsten
A.
,
Breier
M.
,
Grallert
H.
,
Hauner
H.
et al.
(
2016
)
Effects of Genetic Loci Associated with Central Obesity on Adipocyte Lipolysis
.
PLoS One
11
,
e0153990
[PubMed]
37.
Ory
V.
,
Fan
Q.
,
Hamdaoui
N.
,
Zhang
S.Y.
,
Desvaux
D.
,
Audard
V.
et al.
(
2012
)
c-mip down-regulates NF-kappaB activity and promotes apoptosis in podocytes
.
Am. J. Pathol.
180
,
2284
2292
[PubMed]
38.
Ollero
M.
and
Sahali
D.
(
2015
)
Inhibition of the VEGF signalling pathway and glomerular disorders
.
Nephrol. Dial. Transplant.
30
,
1449
1455
[PubMed]
39.
Moktefi
A.
,
Zhang
S.Y.
,
Vachin
P.
,
Ory
V.
,
Henique
C.
,
Audard
V.
et al.
(
2016
)
Repression of CMIP transcription by WT1 is relevant to podocyte health
.
Kidney Int.
90
,
1298
1311
[PubMed]
40.
Sendeyo
K.
,
Audard
V.
,
Zhang
S.Y.
,
Fan
Q.
,
Bouachi
K.
,
Ollero
M.
et al.
(
2013
)
Upregulation of c-mip is closely related to podocyte dysfunction in membranous nephropathy
.
Kidney Int.
83
,
414
425
[PubMed]
41.
Zhang
S.Y.
,
Kamal
M.
,
Dahan
K.
,
Pawlak
A.
,
Ory
V.
,
Desvaux
D.
et al.
(
2010
)
c-mip impairs podocyte proximal signaling and induces heavy proteinuria
.
Sci. Signal
3
,
ra39
[PubMed]
42.
Sayols-Baixeras
S.
,
Hernaez
A.
,
Subirana
I.
,
Lluis-Ganella
C.
,
Munoz
D.
,
Fito
M.
et al.
(
2017
)
DNA Methylation and High-Density Lipoprotein Functionality-Brief Report: The REGICOR Study (Registre Gironi del Cor)
.
Arterioscler. Thromb. Vasc. Biol.
37
,
567
569
[PubMed]
43.
Yamamoto
T.
,
Takabatake
Y.
,
Takahashi
A.
,
Kimura
T.
,
Namba
T.
,
Matsuda
J.
et al.
(
2017
)
High-Fat Diet-Induced Lysosomal Dysfunction and Impaired Autophagic Flux Contribute to Lipotoxicity in the Kidney
.
J. Am. Soc. Nephrol.
28
,
1534
1551
[PubMed]
44.
Jaishy
B.
and
Abel
E.D.
(
2016
)
Lipids, lysosomes, and autophagy
.
J. Lipid Res.
57
,
1619
1635
[PubMed]

Author notes

*

These authors contributed equally to this work.

This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY).