Meng Yang

Underweight Is an Independent Risk Factor for Renal Function Deterioration in Patients with IgA Nephropathy.

Studies on the relationship between body mass index (BMI) and renal progression in IgA Nephropathy (IgAN) were limited, especially for underweight patients with IgAN. To elucidate the clinical features and effect of underweight on renal function deterioration in this disease, we recruited IgAN patients with diagnostic age ≥18 years old and a baseline estimated glomerular filtration rate (eGFR) ≥15 ml/min/1.73m2 from our center between 1985 and 2014. Patients secondary to systemic diseases or follow-up less than 6 months were excluded. All patients’ clinical data at renal biopsy and during follow-up were recorded. Renal outcome was defined as end-stage kidney disease (ESRD). Baseline body mass index (BMI) was calculated by weight (kg) over squared height (m2). According to WHO Asian guideline, BMI was categorized as follows: <18.5kg/m2 (underweight), 18.5–22.99kg/m2 (normal weight), 23–27.49kg/m2 (overweight) and obese (≥27.5 kg/m2). Of 930 primary IgAN patients enrolled in this study, mean age at renal biopsy was 37.6 years and 49.2% were men. Totally, 114 (12.3%) ESRD occurred after a mean follow-up of 47.1 months. More ESRD happened in underweight patients (17.3%) compared to patients with normal weight (13.2%), overweight (11.0%) or obesity (9.5%). By multivariate Cox regression analysis, underweight was independently associated with a higher risk of ESRD after adjustment for demographic characteristics and clinical variables (HR: 3.5, 95% CI: 1.3–9.5, P = 0.01) comparing to normal weight. Underweight patients had lower hemoglobin, serum uric acid, triglycerides, cholesterol and lymphocyte counts than patients with normal weight. Furthermore, BMI was positively correlated with serum C3 (r = 0.25, p <0.001). Our research finds that underweight is an independent risk factor for kidney disease progression in IgAN, which might be associated with malnutrition status and decreased C3 levels.

ABO blood type is associated with renal outcomes in patients with IgA nephropathy

ABO blood group antigens have been reported to be associated with inflammation and infections which have been largely implicated in the onset and progression of immune-mediated diseases. This study aimed to evaluate the association between ABO blood group and progression of IgA nephropathy (IgAN). We retrospectively enrolled 919 biopsy-proven IgAN patients with a minimum follow-up of 1 year and eGFR≥15ml/min/1.73m2 at the time of renal biopsy. Patients in non-B antigen group (type O/A) had lower baseline eGFR, higher systolic blood pressure (SBP), uric acid, lactate dehydrogenase, high-sensitive C-reactive protein and tumor necrosis factor-α compared to patients in B antigen group(type B/AB). After a median follow-up of 57.46 months, 124(13.5%) patients progressed to end-stage renal disease (ESRD) including 98(17.7%) in non-B antigen group and 26(7.1%) in B antigen group. Kaplan-Meier analysis showed the median ESRD-free survival time of patients in non-B antigen group was significantly shorter than patients in B antigen group [143.09±6.38 vs 159.05±4.94months, p < 0.001]. Furthermore, non-B antigen blood group was associated with an independently increased risk of ESRD (HR=2.21, 95%CI 1.35-3.62, p = 0.002) after fully adjusted by age, sex, SBP, eGFR, blood urea nitrogen, hypoalbuminemia, uric acid, triglycerides, hemoglobin, serum C3, urine protein, Oxford classification and glucocorticoid treatment. In conclusion, our study suggests that ABO blood type is a new risk factor for IgAN progression. IgAN patients with blood type O or A have an independent increased risk for renal function deterioration which might be explained by an increased level of inflammatory status.

Kidney Failure Risk Prediction Equations in IgA Nephropathy: A Multicenter Risk Assessment Study in Chinese Patients

BACKGROUND The clinical course of immunoglobulin A (IgA) nephropathy (IgAN) is highly variable, making it difficult to predict which patients are at risk for rapid progression. The aim of this study was to develop and validate a kidney failure risk prediction equation for adults with IgAN. STUDY DESIGN Multicenter retrospective cohort study of 2,155 Chinese patients with IgAN. CANDIDATE PREDICTORS Clinical and histology variables. OUTCOMES Time to end-stage renal disease (ESRD). ANALYTICAL APPROACH The association of baseline predictors with the outcome was tested using cause-specific hazards models by treating death as a censoring event. RESULTS The discovery cohort was composed of 934 IgAN cases with a mean follow-up of 56.3 months. The independent validation cohort was composed of 1,221 additional patients with a mean follow-up of 47.8 months. There were 212 ESRD events in the combined cohort. The best clinical predictive model of ESRD included 5 variables: age, sex, estimated glomerular filtration rate, hemoglobin concentration, and urine protein excretion. The best model combining clinical and histologic data included 2 clinical variables (age and estimated glomerular filtration rate) and 2 pathology scores (M and T scores from the Oxford classification). Both models predicted ESRD well at 10 years in the validation cohort (C statistics of 0.86 [95% CI, 0.83-0.90] and 0.83 [95% CI, 0.77-0.89], respectively). Continuous net reclassification index and integrated discrimination improvement indicated superior performance of the new models compared with previously published models. The performance of the new clinical model was similar to that of the new model that incorporated histologic variables. LIMITATIONS Retrospective study design, differences in severity of disease between the discovery and validation cohorts, the competing risk of death, lack of validation in ethnically diverse patients. CONCLUSIONS Kidney failure risk in the setting of IgAN is able to be predicted in a Chinese population using clinical and histologic variables. Additional evaluation of these equations needs to be implemented in more ethnically diverse patients before they can be applied to clinical practice broadly.

IgA Nephropathy Susceptibility Loci and Disease Progression

BACKGROUND AND OBJECTIVES At least 20 susceptibility loci of IgA nephropathy have been identified by genome-wide association studies to date. Whether these loci were associated with disease progression is unclear. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS We enrolled 613 adult patients with IgA nephropathy for a follow-up of ≥12 months. All 20 IgA nephropathy susceptibility loci were selected and their tag single nucleotide polymorphisms (SNPs) were genotyped. After strict quality control, 16 SNPs and 517 patients with IgA nephropathy were eligible for subsequent analysis. Progression was defined as ESKD or 50% decrease in eGFR. A stepwise Cox regression analysis of all SNPs on Akaike information criterion was performed to select the best model. RESULTS A four-SNP model, rs11150612 (ITGAM-ITGAX), rs7634389 (ST6GAL1), rs2412971 (HORMAD2), and rs2856717 (HLA-DQ/DR), was selected as the best predictive model. The genetic risk score calculated on the basis of the four SNPs was independently associated with disease progression before (hazard ratio [HR], 1.65; 95% confidence interval [95% CI], 1.29 to 2.12) and after adjustment by a recently reported clinical model (HR, 1.29; 95% CI, 1.03 to 1.62) or clinical-pathologic model (HR, 1.35; 95% CI, 1.03 to 1.77). Compared with low genetic risk, patients with middle genetic risk had a 2.12-fold (95% CI, 1.33 to 3.40) increase of progression risk, whereas patients with high genetic risk had 3.61-fold (95% CI, 2.00 to 6.52) progression risk increase. In addition, incorporation of genetic risk score could potentially increase discrimination of the clinical model (c-statistic increase from 0.83 to 0.86) or the clinical-pathologic model (c-statistic increase from 0.82 to 0.85) in predicting 5-year progression risk. CONCLUSIONS The four-SNP genetic risk score was independently associated with IgA nephropathy progression and could enhance the performance of clinical and clinical-pathologic risk models.