Abstract
Introduction
Immunoglobulin A nephropathy (IgAN) is one of the most common glomerulonephritic diseases in the world. Several lines of evidence have suggested that dyslipidemia is related to the disease progression and prognosis of IgAN. However, the study is scarce on the clinicopathological characteristics and outcomes of IgAN with dyslipidemia.
Methods
This study retrospectively analyzed 234 patients with biopsy-proven idiopathic IgAN at the Department of Nephrology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, between January 2015 and June 2021. The participants were divided into dyslipidemia (n = 119) and non-dyslipidemia (n = 115), and the dyslipidemia group was also divided into the following 4 groups: hypertriglyceridemia group, hypercholesterolemia group, mixed hyperlipidemia group, and low high-density lipoprotein cholesterol group. The estimated glomerular filtration rate (eGFR) was estimated by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation.
Results
The prevalence of dyslipidemia in IgAN patients in our center was 50.9% (119/234). The patients with dyslipidemia presented with higher systolic blood pressure (BP), diastolic BP, serum creatinine, uric acid, hemoglobin, proteinuria, and eGFR (p < 0.05). Proportions of males, hypertension, and chronic kidney disease stage 2∼5 were also higher in the dyslipidemia group (p < 0.05). Similarly, the pathological characteristics performed were worse in the dyslipidemia group. Most dyslipidemia patients had a higher percentage of mesangial hypercellularity (M1) and tubular atrophy/interstitial fibrosis (T1∼2) in the Oxford Classification’s scoring system (p < 0.05). Multivariate logistic regression analysis revealed that male gender (odds ratio [OR] = 2.397, 95% confidence interval [CI]: 1.051–5.469, p = 0.038) and proteinuria (OR = 1.000, 95% CI: 1.000–1.001, p = 0.035) were possible risk factors for dyslipidemia. A total of 13 patients (13.8%) in the dyslipidemia group had an endpoint event, of which 6 patients (6.4%) had a ≥50% decrease in eGFR from baseline and 7 patients (7.4%) reached the end-stage renal disease stage. Kaplan-Meier survival curve analysis showed that patients in the dyslipidemia group had a worse outcome than those in the non-dyslipidemia group (log-rank test, p = 0.048).
Conclusions
IgAN patients with dyslipidemia presented more severe clinicopathological characteristics. Male gender and proteinuria are significantly associated with the occurrence of dyslipidemia in IgAN patients. Patients in the dyslipidemia group had a worse prognosis than those in the non-dyslipidemia group, which may be essential for the disease management of IgAN and help identify the high-risk patients.
Keywords: Immunoglobulin A nephropathy, Dyslipidemia, Clinicopathological characteristics, Risk factor, Prognosis
Introduction
Immunoglobulin A nephropathy (IgAN) is currently the most common glomerulonephritic disease in China and even the world [1], accounting for about half of primary glomerular diseases in China [2, 3]. The long-term renal prognosis of IgAN patients is generally considered to be poor, and about 20–30% of patients with IgAN will progress to end-stage renal disease (ESRD) in 20 years of onset, requiring renal replacement therapy, which brings a huge burden to the family and society [4–7].
Dyslipidemia is present in many chronic kidney disease (CKD) patients, and it is also one of the independent risk factors for the progression of kidney disease [8]. Several lines of evidence have suggested that dyslipidemia is related to clinical symptoms, disease progression, and prognosis of the patients with IgAN [9, 10], and it is also a risk factor for IgAN to enter ESRD [11]. Nevertheless, few studies involve pathological factors (especially the Oxford classification). The association between clinicopathological characteristics and dyslipidemia in IgAN patients is not truly understood and deserves further exploration and research. In this single-center, retrospective study, we summarize the clinicopathological characteristics of IgAN patients with dyslipidemia and set out to identify the possible risk factors of dyslipidemia and prognosis in IgAN patients from China.
Materials and Methods
Participants
Patients with biopsy-proven idiopathic IgAN from January 2015 to June 2021 at the Department of Nephrology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, were enrolled in this study. All the participants were older than 14 years and signed an informed consent form. Criteria for excluding the subjects were as follows: secondary IgAN caused by systemic lupus erythematosus, Henoch-Schonlein purpura, and hepatitis-B; IgAN patients with severe malignancy; biopsy sample with fewer than ten glomeruli. At last, a total of 234 patients were included in our study. This project was approved by the Medical Ethics Board of Longhua Hospital, Shanghai University of Traditional Chinese Medicine.
Data Collection
Demographic and laboratory parameters at the time of kidney biopsy were collected, including age, gender, history of hypertension, blood pressure (BP), triglycerides (TG), serum cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol, serum creatinine, blood urea nitrogen (BUN), uric acid, serum albumin, hemoglobin, and proteinuria. The estimated glomerular filtration rate (eGFR) was estimated by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation [12].
All renal tissue specimens were examined using light microscopy, immunofluorescence microscopy, and electron microscopy. The results of histological slides were independently reviewed by two renal pathologists according to the Oxford classification [13–15], of which the specific scoring rules are as follows: M0/M1 as a mesangial score ≤0.5 or >0.5; absence (E0) or presence (E1) of endocapillary hypercellularity; absence (S0) or presence (S1) of segmental sclerosis or tuft adhesions; T0/T1/T2 as the grade of tubular atrophy/interstitial fibrosis ≤25%, 26–50%, and >50%, respectively; absence of cellular/fibrocellular crescents (C0), exist in at least 1 glomerulus (C1), or exist in >25% of glomeruli (C2). Global glomerulosclerosis, segmental adhesion, interstitial cell infiltration, and vascular lesions were also included in pathological features.
Variable Definitions
Dyslipidemia was determined by the 2016 “Chinese guideline for the management of dyslipidemia in adults” [16], including 4 clinical types: hypertriglyceridemia (TG ≥2.3 mmol/L and TC <6.2 mmol/L), hypercholesterolemia (TC ≥6.2 mmol/L and TG <2.3 mmol/L), mixed hyperlipidemia (TG ≥2.3 mmol/L and TC ≥6.2 mmol/L), and low high-density lipoproteinemia (HDL-C <1.0 mmol/L, TC <6.2 mmol/L and TG <2.3 mmol/L). Hypertension refers to a BP ≥140/90 mm Hg in a resting state: systolic BP ≥140 mm Hg and/or diastolic BP ≥90 mm Hg or who has been diagnosed with hypertension before. Proteinuria was estimated using 24-h urine protein collections. CKD [17] was defined as an abnormal renal structure or function or eGFR <60 mL·min-1·(1.73 m2)−1, which has a course of more than 3 months. CKD stage 1 as the eGFR ≥90 mL·min-1·(1.73 m2)−1, CKD stage 2 as the eGFR 60–89 mL·min-1·(1.73 m2)−1, CKD stage 3 as the eGFR 30–59 mL·min-1·(1.73 m2)−1, CKD stage 4 as the eGFR 15–29 mL·min-1·(1.73 m2)−1, and CKD stage 5 as the eGFR <15 mL·min-1· (1.73 m2)−1.
Follow-Up and Study Endpoint
Patients were followed up and evaluated at least every 3 months, with laboratory data and medication administration recorded at each follow-up visit. 53 of 234 patients with IgAN were followed up for less than 12 months and were excluded, leaving 181 patients included in the study analysis. The study endpoint was a ≥50% decrease in eGFR from baseline values or entry into the ESRD phase.
Statistical Analysis
Continuous variables were presented using means ± SD deviation for normal distribution, but for skewed distribution, results were expressed as medians of the 25th and 75th percentiles, which were analyzed by t test, Wilcoxon’s signed-rank test, one-way ANOVA test, or Kruskal-Wallis H test. The results of categorical variables were expressed as the frequency (percentages), which were analyzed by Fisher’s test or χ2 test. The Kaplan-Meier method was used to calculate the cumulative renal survival, and the difference between the two curves was tested using the log-rank test. Correlates between dyslipidemia and the variables of clinicopathology in IgAN were identified using multivariate logistic regression, and the results were expressed as odds ratios (ORs) with a 95% confidence interval (CI). All data analyses were performed using SPSS 25.0, and p < 0.05 was considered statistically significant.
Results
Baseline Clinical Characteristics
The baseline characteristics of 234 IgAN patients were analyzed in this study, including 101 males and 133 females, of which the sex ratio was 1:1.32 and their median age was 39.06 ± 12.20. The prevalence of dyslipidemia in IgAN patients in our center was 119 (50.9%), and 100 (42.7%) showed hypertension. According to clinical classification, the dyslipidemia group was divided into the following 4 groups: hypertriglyceridemia group, 43 (36.1%); hypercholesterolemia group, 13 (10.9%); mixed hyperlipidemia group, 19 (16%); and low HDL-C group, 44 (37%). IgAN patients with CKD stage 1 and CKD stages 2–5 was 115 (49.1%) and 119 (50.9%), respectively (Table 1).
Table 1.
Clinical and histopathological characteristics of IgAN patients between dyslipidemia and non-dyslipidemia group
| Characteristics | All (n = 234) | Dyslipidemia group (n = 119) | Non-dyslipidemia group (n = 115) | p values |
|---|---|---|---|---|
| Age, years | 39.06±12.20 | 40.40±12.67 | 37.68±11.59 | 0.088 |
| Male, n (%) | 101 (43.2) | 69 (58.0) | 32 (27.8) | <0.001 |
| Interval between presentation and biopsy, months | 12 (3.5, 28) | 12 (3, 28) | 22 (6, 28) | 0.081 |
| Hypertension, n (%) | 100 (42.7) | 61 (51.3) | 39 (34.0) | 0.007 |
| Systolic BP, mm Hg | 125.21±13.77 | 127.39±13.85 | 122.96±13.38 | 0.013 |
| Diastolic BP, mm Hg | 80.31±9.25 | 81.51±10.23 | 79.07±7.97 | 0.043 |
| TG, mmol/L | 1.93±1.30 | 2.64±1.47 | 1.20±0.43 | <0.001 |
| Cholesterol, mmol/L | 4.93±1.39 | 5.24±1.74 | 4.61±0.79 | <0.001 |
| HDL-C, mmol/L | 1.18±0.35 | 1.06±0.39 | 1.31±0.25 | <0.001 |
| LDL-C, mmol/L | 3.23±1.15 | 3.50±1.42 | 2.95±0.67 | <0.001 |
| Serum creatinine, µmol/L | 94.02±53.62 | 102.51±62.39 | 85.24±41.14 | 0.013 |
| BUN, mmol/L | 6.05±2.96 | 6.33±3.44 | 5.76±2.35 | 0.143 |
| Uric acid, µmol/L | 376.57±101.22 | 404.27±96.85 | 347.90±97.99 | <0.001 |
| Serum albumin, g/L | 37.53±5.79 | 37.02±6.96 | 38.06±4.22 | 0.168 |
| Hemoglobin, g/L | 130.16±19.91 | 134.15±20.09 | 126.03±18.93 | 0.002 |
| Proteinuria, g/day | 1.68±1.26 | 1.88±1.33 | 1.47±1.16 | 0.013 |
| eGFR, mL/min/1.73 m2 | 87.32±30.27 | 83.30±30.03 | 91.48±30.08 | 0.038 |
| CKD stage, n (%) | ||||
| CKD stage 1 | 115 (49.1) | 50 (42.0) | 65 (56.5) | 0.026 |
| CKD stage 2 | 67 (28.7) | 38 (31.9) | 29 (25.2) | 0.256 |
| CKD stage 3 | 44 (18.8) | 27 (22.7) | 17 (14.8) | 0.122 |
| CKD stage 4 | 7 (3.0) | 3 (2.6) | 4 (3.5) | 0.963 |
| CKD stage 5 | 1 (0.4) | 1 (0.8) | 0 (0) | 1.000 |
| CKD stage 2–5 | 119 (50.9) | 69 (58.0) | 50 (43.5) | 0.026 |
| Oxford classification, n (%) | ||||
| Mesangial hypercellularity, M1 | 175 (74.8) | 96 (80.7) | 79 (68.7) | 0.035 |
| Endocapillary hypercellularity, E1 | 19 (8.1) | 10 (8.4) | 9 (7.8) | 0.872 |
| Segmental sclerosis, S1 | 178 (76.1) | 95 (79.8) | 83 (72.2) | 0.170 |
| Tubular atrophy/interstitial fibrosis, T1∼2 | 82 (35.0) | 50 (42.0) | 32 (27.8) | 0.023 |
| Cellular/fibrocellular crescents, C1∼2 | 42 (17.9) | 23 (19.3) | 19 (16.5) | 0.576 |
Values for continuous variables are expressed as mean ± SD or shown as the median (interquartile range); values for categorical variables are expressed in percentage.
BP, blood pressure; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; BUN, blood urea nitrogen; eGFR, estimated glomerular filtration rate; CKD, chronic kidney disease.
Clinical and Histopathological Characteristics between Dyslipidemia Group and Non-Dyslipidemia Group
The clinical and histopathological characteristics of IgAN patients between those who did and did not have an episode of dyslipidemia are presented in Table 1. Compared with people in the non-dyslipidemia group, people in the dyslipidemia group had a higher presentation of systolic BP, diastolic BP, serum creatinine, uric acid, hemoglobin, proteinuria as well as eGFR (p < 0.05). Proportions of males, hypertension, and CKD stage 2–5 were also higher in the dyslipidemia group (p < 0.05). Nevertheless, no significant differences were found in age, the interval between presentation and biopsy, BUN, and serum albumin between the patients in both groups (p > 0.05). IgAN patients in the dyslipidemia group had a higher proportion of M1 and T1∼2 than that in the non-dyslipidemia group (p < 0.05).
Clinical and Histopathological Characteristics in Different Types of Dyslipidemia Groups
Table 2 shows significant differences in uric acid, serum albumin, and proteinuria can be found in the four types of dyslipidemia groups (p < 0.05). However, no significant differences in age, gender, the interval between presentation and biopsy, systolic BP, diastolic BP, serum creatinine, BUN, hemoglobin, eGFR, and CKD stage were found among these groups (p > 0.05). There were also no significant differences detected in the four groups for the histopathological characteristics (p > 0.05).
Table 2.
Clinical and histopathological characteristics of IgAN patients in different types of dyslipidemia groups
| Characteristics | Hypertriglyceridemia group (n = 43) | Hypercholesterolemia group (n = 13) | Mixed hyperlipidemia group (n = 19) | Low high-density lipoproteinemia group (n = 44) | p values |
|---|---|---|---|---|---|
| Age, years | 40.47±9.80 | 39.77±17.39 | 40.00±11.31 | 40.70±14.44 | 0.995 |
| Male, n (%) | 29 (67.4) | 5 (38.5) | 7 (36.8) | 28 (63.6) | 0.053 |
| Interval between presentation and biopsy, months | 12 (3, 28) | 6 (2, 12) | 8 (2, 34) | 12 (3, 28) | 0.461 |
| Hypertension, n (%) | 27 (62.8) | 5 (38.5) | 9 (47.4) | 20 (45.5) | 0.278 |
| Systolic BP, mm Hg | 128.05±15.08 | 125.23±10.12 | 124.95±12.55 | 128.45±14.29 | 0.743 |
| Diastolic BP, mm Hg | 82.26±12.26 | 79.23±5.61 | 83.00±11.48 | 80.82±8.54 | 0.693 |
| TG, mmol/L | 3.55±1.47 | 1.51±0.42 | 3.77±1.23 | 1.58±0.43 | <0.001 |
| Cholesterol, mmol/L | 4.84±0.71 | 7.83±1.99 | 7.24±1.25 | 4.01±0.74 | <0.001 |
| HDL-C, mmol/L | 0.96±0.22 | 1.68±0.51 | 1.31±0.44 | 0.86±0.11 | <0.001 |
| LDL-C, mmol/L | 2.99±0.67 | 5.55±1.97 | 4.53±1.04 | 2.94±1.13 | <0.001 |
| Serum creatinine, µmol/L | 110.03±46.60 | 86.18±39.79 | 91.01±30.07 | 104.96±46.42 | 0.532 |
| BUN, mmol/L | 6.31±2.92 | 6.52±3.27 | 5.66±1.62 | 6.58±4.45 | 0.805 |
| Uric acid, µmol/L | 425.86±97.19 | 339.23±91.76 | 407.89±78.89 | 400.82±98.98 | 0.042 |
| Serum albumin, g/L | 38.67±5.13 | 32.30±10.85 | 32.62±9.65 | 38.70±3.79 | <0.001 |
| Hemoglobin, g/L | 136.19±22.70 | 137.46±12.86 | 134.05±22.75 | 131.23±18.03 | 0.636 |
| Proteinuria, g/day | 2.01±1.50 | 2.05±1.53 | 2.69±1.21 | 1.37±0.89 | 0.002 |
| eGFR, mL/min/1.73 m2 | 79.57±28.19 | 98.88±36.76 | 82.32±25.63 | 82.77±30.97 | 0.243 |
| CKD stage, n (%) | |||||
| CKD stage 1 | 15 (34.9) | 8 (61.5) | 8 (42.1) | 19 (43.2) | 0.398 |
| CKD stage 2 | 18 (41.9) | 2 (15.4) | 5 (26.3) | 13 (29.5) | 0.264 |
| CKD stage 3 | 8 (18.6) | 2 (15.4) | 6 (31.6) | 11 (25.0) | 0.616 |
| CKD stage 4 | 2 (4.6) | 1 (7.7) | 0 (0) | 0 (0) | 0.219 |
| CKD stage 5 | 0 (0) | 0 (0) | 0 (0) | 1 (2.3) | 1.000 |
| CKD stage 2–5 | 28 (65.1) | 5 (38.5) | 11 (57.9) | 25 (56.8) | 0.398 |
| Oxford classification, n (%) | |||||
| Mesangial hypercellularity, M1 | 36 (83.7) | 9 (69.2) | 17 (89.5) | 34 (77.3) | 0.467 |
| Endocapillary hypercellularity, E1 | 4 (9.3) | 2 (15.4) | 3 (15.8) | 1 (2.3) | 0.112 |
| Segmental sclerosis, S1 | 36 (83.7) | 7 (53.8) | 17 (89.5) | 35 (79.5) | 0.101 |
| Tubular atrophy/interstitial fibrosis, T1∼2 | 20 (46.5) | 3 (23.1) | 9 (47.4) | 18 (40.9) | 0.472 |
| Cellular/fibrocellular crescents, C1∼2 | 8 (18.6) | 3 (23.1) | 2 (10.5) | 10 (22.7) | 0.716 |
Values for continuous variables are expressed as mean ± SD or shown as the median (interquartile range); values for categorical variables are expressed in percentage.
BP, blood pressure; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; BUN, blood urea nitrogen; eGFR, estimated glomerular filtration rate; CKD, chronic kidney disease.
Potential Associated Risk Factors for Dyslipidemia
Table 3 reveals the variables that are independently associated with dyslipidemia. Univariate analysis showed that male (OR = 3.579, 95% CI: 2.072–6.183, p < 0.001), hypertension (OR = 2.050, 95% CI: 1.209–3.473, p = 0.008), systolic BP (OR = 1.025, 95% CI: 1.005–1.046, p = 0.016), diastolic BP (OR = 1.030, 95% CI: 1.000–1.061, p = 0.047), serum creatinine (OR = 1.007, 95% CI: 1.001–1.013, p = 0.019), uric acid (OR = 1.006, 95% CI: 1.003–1.009, p < 0.001), hemoglobin (OR = 1.022, 95% CI: 1.008–1.036, p = 0.002), proteinuria (OR = 1.000, 95% CI: 1.000–1.000, p = 0.015), eGFR (OR = 0.991, 95% CI: 0.982–1.000, p = 0.040), and tubular atrophy/interstitial fibrosis (T1∼2) (OR = 1.880, 95% CI: 1.088–3.247, p = 0.024) were positively correlated with dyslipidemia. Variables with p < 0.05 in univariate analysis were entered into multivariate analysis as covariates. The multivariate logistic regression analysis found that dyslipidemia was most strongly associated with the two clinical findings: male (OR = 2.397, 95% CI: 1.051–5.469, p = 0.038) and proteinuria (OR = 1.000, 95% CI: 1.000–1.001, p = 0.035).
Table 3.
Risk factors of dyslipidemia in IgAN patients with univariate and multivariate analysis
| Variable | Univariate analysis | Multivariate analysis | ||||
|---|---|---|---|---|---|---|
| OR | 95% CI | p values | OR | 95% CI | p values | |
| Male (vs. female) | 3.579 | 2.072∼6.183 | <0.001 | 2.397 | 1.051∼5.469 | 0.038 |
| Hypertension (vs. no) | 2.050 | 1.209∼3.473 | 0.008 | 1.243 | 0.647∼2.387 | 0.514 |
| Systolic BP, mm Hg | 1.025 | 1.005∼1.046 | 0.016 | 1.003 | 0.974∼1.033 | 0.844 |
| Diastolic BP, mm Hg | 1.030 | 1.000∼1.061 | 0.047 | 1.018 | 0.975∼1.063 | 0.411 |
| Serum creatinine, µmol/L | 1.007 | 1.001∼1.013 | 0.019 | 0.998 | 0.987∼1.009 | 0.692 |
| Uric acid, µmol/L | 1.006 | 1.003∼1.009 | <0.001 | 1.003 | 1.000∼1.007 | 0.088 |
| Hemoglobin, g/L | 1.022 | 1.008∼1.036 | 0.002 | 1.011 | 0.991∼1.031 | 0.283 |
| Proteinuria, g/day | 1.000 | 1.000∼1.000 | 0.015 | 1.000 | 1.000∼1.001 | 0.035 |
| eGFR, mL/min/1.73 m2 | 0.991 | 0.982∼1.000 | 0.040 | 0.998 | 0.981∼1.016 | 0.867 |
| CKD stage 2∼5 (vs. CKD stage 1) | 1.565 | 0.934∼2.621 | 0.089 | |||
| M1 (vs. M0) | 1.826 | 0.998∼3.341 | 0.051 | |||
| T1∼2 (vs. T0) | 1.880 | 1.088∼3.247 | 0.024 | 1.295 | 0.639∼2.624 | 0.473 |
Variables with p < 0.05 in univariate analysis for a relationship with dyslipidemia were entered into multivariate analysis as covariates.
OR, odds ratio; CI, confidence interval; BP, blood pressure; CKD, chronic kidney disease; M, mesangial hypercellularity; T, tubular atrophy/interstitial fibrosis.
Follow-Up and Treatment
The median follow-up was 29 months (range, 12–68 months) in the dyslipidemia group and 24 months (range, 12–65 months) in the non-dyslipidemia group. Patients with proteinuria levels >0.3 g/24 h were treated with angiotensin-converting enzyme inhibitors or angiotensin receptor blockers. 181 patients with IgAN had a total of 17 (9.4%) endpoint events, of which 8 patients (4.4%) had a ≥50% decrease in eGFR from baseline values and 9 patients (5.0%) reached the ESRD stage. A total of 13 patients (13.8%) in the dyslipidemia group had an endpoint event, of which 6 patients (6.4%) had a ≥50% decrease in eGFR from baseline and 7 patients (7.4%) reached the ESRD stage. Kaplan-Meier survival curve analysis showed that patients in the dyslipidemia group had a worse prognosis than those in the non-dyslipidemia group (log-rank test, p = 0.048), as shown in Figure 1.
Fig. 1.
Kidney survival curve in IgAN patients with dyslipidemia and non-dyslipidemia group.
Discussion
Many previous studies on dyslipidemia have revealed that it is not only a risk factor for the occurrence and development of cardiovascular diseases but aggravates the progression and prognosis of IgAN [10]. Research has shown that the incidence of dyslipidemia in adult IgAN is 45.3–61.1% [9, 18, 19]. At present, there are few studies on the clinicopathological characteristics and renal outcomes of IgAN complicated with dyslipidemia in China. Understanding and analyzing the prevalence, clinicopathological characteristics, risk factors, and outcomes of dyslipidemia in patients with IgAN is of great clinical significance, which is helpful to optimize the prevention and treatment strategies of IgAN and provide meaningful evidence for clinical treatment.
The results obtained from our work revealed that 50.9% of the IgAN patients in our center were accompanied by dyslipidemia, and the proportions of CKD stage 2–5 were higher in the dyslipidemia group. In addition, IgAN patients in the dyslipidemia group had more severe clinical changes than the non-dyslipidemia patients, and a recent clinical investigation from China illustrated similar results [20]. Likewise, pathological features also showed a higher proportion of M1 (mesangial score >0.5) and T1∼2 (tubular atrophy/interstitial fibrosis 26–50% and >50%) in IgAN patients in the dyslipidemia group, which are similar to the previous descriptions of IgAN [20, 21].
These results suggest that dyslipidemia may aggravate the structural and functional damage to the kidneys in IgAN patients. In animal experiments, imaging mass spectrometry analysis found that the molecular distribution of lipids is mainly located in the cortex area, hilum area, and tubular area in hyper-IgA murine kidneys [22]. By the direct toxic effects of lipids on renal cells, dyslipidemia would aggravate the progression of CKD patients [23, 24]. Moreover, clinical and experimental evidence shows that the abnormalities of lipid metabolism can promote the progress of kidney injury by causing inflammatory, oxidative, and ER stress [25, 26].
Multivariate logistic regression analysis revealed that male sex and proteinuria were two risk factors for dyslipidemia in IgAN patients. The research has also shown that the prevalence of dyslipidemia was higher in male IgAN patients than that of females, which is the same as the former findings [27–29], and the reason for this may be that males have more fat deposited viscerally and the differences in sex steroid hormones between the genders [30–32]. In addition, some studies have also revealed that the clinicopathological characteristics of male IgAN patients are worse than those of females, and maleness is seen as an independent risk factor for poor prognosis [33]. These discrepancies in genders may be because of inadequate health awareness and unhealthy lifestyles in male IgAN patients. Men are generally less aware than women of the common symptoms of potentially serious health problems [34]. In terms of unhealthy lifestyles, a retrospective cohort study has reported a higher proportion of male smokers than female smokers (63.7 vs. 36.3%) [35], and higher smoking doses are associated with more severe renal damage and a worse prognosis [36]. Therefore, healthy lifestyle choices and health awareness should be encouraged as part of treatment for IgAN. Numerous clinical data have shown that patients with persistent proteinuria fare poorly. As an independent risk factor, proteinuria is a powerful predictor of the most significant risk for progressive renal loss in those patients [37]. Min Tan et al. [38] also found that proteinuria, as an independent predictor, may help identify the risk of severe renal pathological damage in IgAN patients with benign clinical characteristics. Therefore, it is critical to reduce proteinuria levels to the greatest extent, even in IgAN patient who has a more benign clinical presentation.
Our study showed that patients with IgAN with dyslipidemia have a worse prognosis. Nakamura et al. [39] found that hyperlipidemia was significantly negatively correlated with eGFR, which causes ischemic kidney injury, promotes the inflammatory response, and aggravates oxidative stress, leading to the development of ESRD. In addition, dyslipidemia can lead to glomerular podocyte injury in patients and promote glomerulosclerosis, thus aggravating the disease progression [24]. Therefore, attention should be paid to the management of lipid levels in IgAN patients and early intervention therapy to reduce the harmful effects of lipid metabolism on the organism, thereby delaying the development of the disease and improving the prognosis of IgAN patients.
However, we must acknowledge some potential limitations of our research. First, our retrospective study is single-center. Due to regional restrictions, the patient source is relatively single, leading to a selection bias, and the results may not apply to IgAN patients in other regions. Second, the current study is limited by the relatively small sample, and the data are incomplete. Third, because the data collection has a retrospective nature, the loss of some information is unavoidable, which may have some impact on the reliability of this work. So, more well-designed prospective multicenter cohort studies with larger sample sizes are demanded in the future.
Conclusion
In conclusion, IgAN patients with dyslipidemia showed more severe clinicopathological features, and multivariate analyses in this study revealed that the male gender and proteinuria could be risk factors for IgAN with dyslipidemia. A higher proportion of patients in the dyslipidemia group had a ≥50% decrease in eGFR from baseline values or entered the ESRD stage with a worse prognosis compared to the non-dyslipidemia group. This may be essential for the favorable disease management of IgAN and help identify the high-risk patients.
Acknowledgments
We would like to thank all the patients included in this study.
Statement of Ethics
This retrospective study complied with the guidelines of the Declaration of Helsinki and was approved by the Medical Ethics Committee of Longhua Hospital of Shanghai University of Traditional Chinese Medicine (2018-008). Written informed consent was obtained from participants (or obtained from the parent/legal guardian of all child participants under 18 years of age) to participate in the study.
Conflict of Interest Statement
The authors declare that they have no conflicts of interest.
Funding Sources
This work was supported by Shanghai Municipal Key Clinical Specialty (No. shslczdzk04201), the National Natural Science Foundation of China (No. 82174320), and the National Key Research and Development Program – Research on the Modernization of Traditional Chinese Medicine (No. 2019YFC1709403).
Author Contributions
Research idea, study design, and writing – original draft: Sidi Liu; clinical and pathological data acquisition: Zhenzhen Lu and Zhike Fu; data analysis/interpretation: Huijie Li and Chuying Gui; and supervision and writing – review and editing: Yueyi Deng. The authors read and approved the final manuscript.
Funding Statement
This work was supported by Shanghai Municipal Key Clinical Specialty (No. shslczdzk04201), the National Natural Science Foundation of China (No. 82174320), and the National Key Research and Development Program – Research on the Modernization of Traditional Chinese Medicine (No. 2019YFC1709403).
Data Availability Statement
Research data are not publicly available for ethical reasons due to participant privacy concerns. The data used and/or analyzed in this study are available from the corresponding author on reasonable request.
References
- 1. Donadio JV, Grande JP. IgA nephropathy. N Engl J Med. 2002;347(10):738–48. 10.1056/NEJMra020109. [DOI] [PubMed] [Google Scholar]
- 2. Li LS, Liu ZH. Epidemiologic data of renal diseases from a single unit in China: analysis based on 13,519 renal biopsies. Kidney Int. 2004;66(3):920–3. 10.1111/j.1523-1755.2004.00837.x. [DOI] [PubMed] [Google Scholar]
- 3. Zhou FD, Zhao MH, Zou WZ, Liu G, Wang H. The changing spectrum of primary glomerular diseases within 15 years: a survey of 3,331 patients in a single Chinese centre. Nephrol Dial Transplant. 2009 Mar;24(3):870–6. 10.1093/ndt/gfn554. [DOI] [PubMed] [Google Scholar]
- 4. Koyama A, Igarashi M, Kobayashi M. Natural history and risk factors for immunoglobulin A nephropathy in Japan. Research group on progressive renal diseases. Am J Kidney Dis. 1997;29(4):526–32. 10.1016/s0272-6386(97)90333-4. [DOI] [PubMed] [Google Scholar]
- 5. Lv J, Zhang H, Zhou Y, Li G, Zou W, Wang H. Natural history of immunoglobulin A nephropathy and predictive factors of prognosis: a long-term follow up of 204 cases in China. Nephrology. 2008;13(3):242–6. 10.1111/j.1440-1797.2007.00898.x. [DOI] [PubMed] [Google Scholar]
- 6. Radford MG Jr, Donadio JV Jr, Bergstralh EJ, Grande JP. Predicting renal outcome in IgA nephropathy. J Am Soc Nephrol. 1997;8(2):199–207. 10.1681/ASN.V82199. [DOI] [PubMed] [Google Scholar]
- 7. Berthoux FC, Mohey H, Afiani A. Natural history of primary IgA nephropathy. Semin Nephrol. 2008;28(1):4–9. 10.1016/j.semnephrol.2007.10.001. [DOI] [PubMed] [Google Scholar]
- 8. Gai Z, Wang T, Visentin M, Kullak-Ublick GA, Fu X, Wang Z. Lipid accumulation and chronic kidney disease. Nutrients. 2019;11(4):722. 10.3390/nu11040722. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Mo MQ, Pan L, Lu QM, Li QL, Liao YH. The association of the CMIP rs16955379 polymorphism with dyslipidemia and the clinicopathological features of IgA nephropathy. Int J Clin Exp Pathol. 2018;11(10):5008–23. [PMC free article] [PubMed] [Google Scholar]
- 10. Syrjänen J, Mustonen J, Pasternack A. Hypertriglyceridaemia and hyperuricaemia are risk factors for progression of IgA nephropathy. Nephrol Dial Transplant. 2000 Jan;15(1):34–42. 10.1093/ndt/15.1.34. [DOI] [PubMed] [Google Scholar]
- 11. Kovács T, Vas T, Kovesdy CP, Késõi I, Sági B, Wittmann I, et al. Metabolic syndrome and other cardiovascular risk factors associated with the progression of IgA nephropathy. Clin Kidney J. 2013;6(4):395–401. 10.1093/ckj/sfs131. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF 3rd, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604–12. 10.7326/0003-4819-150-9-200905050-00006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Working Group of the International IgA Nephropathy Network and the Renal Pathology Society; Cattran DC, Coppo R, Cook HT, Feehally J, Roberts ISD, et al. The Oxford classification of IgA nephropathy: rationale, clinicopathological correlations, and classification. Kidney Int. 2009;76(5):534–45. 10.1038/ki.2009.243. [DOI] [PubMed] [Google Scholar]
- 14. Working Group of the International IgA Nephropathy Network and the Renal Pathology Society; Roberts ISD, Cook HT, Troyanov S, Alpers CE, Amore A, et al. The Oxford classification of IgA nephropathy: pathology definitions, correlations, and reproducibility. Kidney Int. 2009;76(5):546–56. 10.1038/ki.2009.168. [DOI] [PubMed] [Google Scholar]
- 15. Trimarchi H, Barratt J, Cattran DC, Cook HT, Coppo R, Haas M, et al. Oxford classification of IgA nephropathy 2016: an update from the IgA nephropathy classification working group. Kidney Int. 2017;91(5):1014–21. 10.1016/j.kint.2017.02.003. [DOI] [PubMed] [Google Scholar]
- 16. Chu JR, Gao RL, Zhao SP, Lu GP, Zhao D, Li JJ. 2016 Chinese guideline for the management of dyslipidemia in adults. Chin J Health Manage. 2017;11(01):7–28. [Google Scholar]
- 17. Levin A, Stevens PE. Summary of KDIGO 2012 CKD Guideline: behind the scenes, need for guidance, and a framework for moving forward. Kidney Int. 2014;85(1):49–61. 10.1038/ki.2013.444. [DOI] [PubMed] [Google Scholar]
- 18. Liu R, Guan X, Chen GL, Zhang XX, Tang HL, Li CC, et al. Clinical features and pathological changes in adult IgA nephropathy with dyslipidemia. J Clin Nephrol. 2019;19(03):187–90. [Google Scholar]
- 19. Peng X, Li XH, Ma YH, Wu Y, Nong FW, Liao YH. Impact of different types of dyslipidemia on clinical and pathological characteristics of IgA nephropathy. Chin J Kidney Dis Invest. 2016;5(05):214–7. [Google Scholar]
- 20. Xu YY, Wen SJ, Cheng C, Chen LZ, Mo Y, Li W, et al. Clinical and pathological features of different type of dyslipidemia in children with IgA nephropathy. Chin J Nephrol. 2020;36(08):609–17. [Google Scholar]
- 21. Lai YP, Zhang Z, Liang YJ, You YS, Li G, Liu HM. Analysis on clinicopathological features and dyslipidemia in IgA nephropathy. Chongqing Med. 2016;45(32):4511–3+6. [Google Scholar]
- 22. Kaneko Y, Obata Y, Nishino T, Kakeya H, Miyazaki Y, Hayasaka T, et al. Imaging mass spectrometry analysis reveals an altered lipid distribution pattern in the tubular areas of hyper-IgA murine kidneys. Exp Mol Pathol. 2011;91(2):614–21. 10.1016/j.yexmp.2011.07.002. [DOI] [PubMed] [Google Scholar]
- 23. Kim HJ, Moradi H, Yuan J, Norris K, Vaziri ND. Renal mass reduction results in accumulation of lipids and dysregulation of lipid regulatory proteins in the remnant kidney. Am J Physiol Renal Physiol. 2009;296(6):F1297–306. 10.1152/ajprenal.90761.2008. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Joles JA, Kunter U, Janssen U, Kriz W, Rabelink TJ, Koomans HA, et al. Early mechanisms of renal injury in hypercholesterolemic or hypertriglyceridemic rats. J Am Soc Nephrol. 2000;11(4):669–83. 10.1681/ASN.V114669. [DOI] [PubMed] [Google Scholar]
- 25. Moradi H, Vaziri ND. Molecular mechanisms of disorders of lipid metabolism in chronic kidney disease. Front Biosci. 2018;23(1):146–61. 10.2741/4585. [DOI] [PubMed] [Google Scholar]
- 26. Ruan XZ, Varghese Z, Moorhead JF. An update on the lipid nephrotoxicity hypothesis. Nat Rev Nephrol. 2009;5(12):713–21. 10.1038/nrneph.2009.184. [DOI] [PubMed] [Google Scholar]
- 27. Pan L, Yang Z, Wu Y, Yin RX, Liao Y, Wang J, et al. The prevalence, awareness, treatment and control of dyslipidemia among adults in China. Atherosclerosis. 2016;248:2–9. 10.1016/j.atherosclerosis.2016.02.006. [DOI] [PubMed] [Google Scholar]
- 28. Cai L, Zhang L, Liu A, Li S, Wang P. Prevalence, awareness, treatment, and control of dyslipidemia among adults in Beijing, China. J Atheroscler Thromb. 2012;19(2):159–68. 10.5551/jat.10116. [DOI] [PubMed] [Google Scholar]
- 29. Huang Y, Gao L, Xie X, Tan SC. Epidemiology of dyslipidemia in Chinese adults: meta-analysis of prevalence, awareness, treatment, and control. Popul Health Metr. 2014;12(1):28. 10.1186/s12963-014-0028-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Bredella MA. Sex differences in body composition. Adv Exp Med Biol. 2017;1043:9–27. 10.1007/978-3-319-70178-3_2. [DOI] [PubMed] [Google Scholar]
- 31. Jiang J, Deng S, Chen Y, Liang S, Ma N, Xu Y, et al. Comparison of visceral and body fat indices and anthropometric measures in relation to untreated hypertension by age and gender among Chinese. Int J Cardiol. 2016;219:204–11. 10.1016/j.ijcard.2016.06.032. [DOI] [PubMed] [Google Scholar]
- 32. Tchernof A, Després JP. Sex steroid hormones, sex hormone-binding globulin, and obesity in men and women. Horm Metab Res. 2000;32(11–12):526–36. 10.1055/s-2007-978681. [DOI] [PubMed] [Google Scholar]
- 33. Wen D, Tang Y, Tan L, Tan J, Chen D, Zhang Y, et al. Sex disparities in IgA nephropathy: a retrospective study in Chinese patients. Int Urol Nephrol. 2021;53(2):315–23. 10.1007/s11255-020-02631-7. [DOI] [PubMed] [Google Scholar]
- 34. Baker P. Men’s health: an overlooked inequality. Br J Nurs. 2016;25(19):1054–7. 10.12968/bjon.2016.25.19.1054. [DOI] [PubMed] [Google Scholar]
- 35. Yamamoto R, Nagasawa Y, Shoji T, Iwatani H, Hamano T, Kawada N, et al. Cigarette smoking and progression of IgA nephropathy. Am J Kidney Dis. 2010;56(2):313–24. 10.1053/j.ajkd.2010.02.351. [DOI] [PubMed] [Google Scholar]
- 36. Wang S, Qin A, Pei G, Jiang Z, Dong L, Tan J, et al. Cigarette smoking may accelerate the progression of IgA nephropathy. BMC Nephrol. 2021;22(1):239. 10.1186/s12882-021-02453-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37. Keane WF. Proteinuria: its clinical importance and role in progressive renal disease. Am J Kidney Dis. 2000;35(4 Suppl 1):S97–105. 10.1016/s0272-6386(00)70237-x. [DOI] [PubMed] [Google Scholar]
- 38. Tan M, Li W, Zou G, Zhang C, Fang J. Clinicopathological features and outcomes of IgA nephropathy with hematuria and/or minimal proteinuria. Kidney Blood Press Res. 2015;40(2):200–6. 10.1159/000368495. [DOI] [PubMed] [Google Scholar]
- 39. Nakamura T, Ushiyama C, Hirokawa K, Osada S, Inoue T, Shimada N, et al. Effect of cerivastatin on proteinuria and urinary podocytes in patients with chronic glomerulonephritis. Nephrol Dial Transplant. 2002 May;17(5):798–802. 10.1093/ndt/17.5.798. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Research data are not publicly available for ethical reasons due to participant privacy concerns. The data used and/or analyzed in this study are available from the corresponding author on reasonable request.
