The serum immunoglobulin A (IgA)/C3 ratio is considered to be an effective predictor of IgA nephropathy (IgAN). This study sought to explore the diagnostic value of the IgA/C3 ratio in IgAN among primary glomerular nephropathy patients in China.
We recruited 1095 biopsy-diagnosed primary glomerular nephropathy patients, including 757 IgAN patients and 338 non-IgAN patients. Patient demographics, serum immunological indices, and other clinical examinations were measured. IgAN cases were propensity score matched (PSM) to non-IgAN cases on the logit of the propensity score using nearest neighbor matching in a 1:1 fashion, with a caliper of 0.02 with no replacements, according to age, gender, BMI, proteinuria level, and estimated glomerular filtration rate (eGFR).
We found that in both the full cohort and PSM cohort, the IgA/C3 ratio in the IgAN group was significantly higher than that of the non-IgAN group. The same results were also obtained with stratification by different levels of proteinuria and renal function. In the PSM cohort, there was no difference in IgA/C3 ratio in patients with IgAN between different proteinuria groups and different chronic kidney disease (CKD) groups. The area under the ROC curve (AUROC) of the IgA/C3 ratio in distinguishing IgAN among primary glomerular disease was 0.767 in the full cohort, and 0.734 in the PSM cohort. The highest AUROC of the IgA/C3 ratio was in the ≤1 g/d proteinuria group (0.801 in the full cohort, and 0.803 in the PSM cohort); however, there was no difference between all CKD groups. Meanwhile, the diagnostic accordance rate for the diagnosis of IgAN among all patients with an IgA/C3 ratio > 3.5304 was as high as 92.02% in the full cohort. IgAN was independently correlated with IgA/C3 ratio in the full cohort by multivariate logistic regression analysis.
The present study provides clear evidence that the IgA/C3 ratio is an effective predictor of IgA diagnosis, especially in patients with proteinuria ≤1 g/d. In order to study the effectiveness of this biomarker, and to determine a standardized cut-off value, additional multicenter large-scale studies are needed.