When statistics are used to determine the significant predictors for a diagnosis or classification of a disease, different statistical algorithms, biological datasets, and parameters may result in different outputs [18
]. Furthermore, multicollinearity is almost always present with medical laboratory parameters, which may also bring out variability and instability in a statistical model [21
]. Thus, choosing appropriate variables for multiparameter analysis is very important.
The present study was designed as a cohort study, and was based on a previous retrospective study [15
]. Compared with the previous study, this study had more parameters, including fibrinogen, D-dimer, serum IgA, and complement C3, all of which are known biomarkers of kidney diseases [22
]. Based on univariate analysis, correlation analysis, and clinical experience, 13 out of 57 routine and useful parameters were selected as predictors of IgAN. These were as follows: manifestation, FIB, D2, sIgA, sIgG, UN, ALB, TG, CH, DB, ALP, CA199, and CA153. Three indicators, specifically TP, LDL, and Ca, were screened out, as they demonstrated the highest correlations with the other two indicators (correlation coefficients: TP/ALB
0.968 and Ca/ALB
0.813). Similar results were obtained with two of the most frequently-used multiparameter analyses, in particular logistic regression and discriminant analyses, indicating that these three parameters are truly significant in classifying IgAN and non-IgAN.
Furthermore, 180 new cases were used to validate the two equations derived equations for classifying IgAN. The discerning power of the two classification equations was similar in the validation cases. The different cut-off points of the predicted probabilities resulted in different diagnostic efficiencies, indicating that the cases near the cut-off point require more attention. Further analysis indicated that the misdiagnosis rate of cases with predicted probabilities between 0.26-0.59 was higher than of those with predicted probabilities of <0.25 and >0.59 (the cut-off point
0.4). These results are very interesting and important, as: a) if the predicted probability of a patient is between 0.26-0.59, then the patient needs more testing for diagnosis, such as a renal biopsy; b) if the predicted probability of a patient is >0.59, then the patient has at least an 85.0% possibility of IgAN; and c) if the predicted probability of a patient is <0.26, then the patient has at least an 88.5% possibility of non-IgAN.
The net reclassification improvement (NRI), produced by Penica et al., is used for evaluating the classification improvement when a new marker is put into a primary model [24
]. For further investigating the classification power of the pre-selected biological parameters, we used “gender” and “manifestation” to create a basic linear logistic regression model and a linear discriminant analysis model. The results of NRI indicated only sIgA and FIB were positive for discriminating IgAN from non-IgAN in this dataset (Table
The exact pathogenesis of IgAN has not been elucidated up to now. Aberrant IgA1 molecular with the glycans (galactose or sialic acid) deficiencies in the hinge region in circulation is deemed generally to be a crucial and initial factor for the development and pathological characteristics of IgAN [25
]. The previous reports indicated that abnormally glycosylated IgA1 molecular had more affinity with the specific IgA1 receptor in the mesangial cells [29
], was apt to deposit in kidneys combined with circulating IgG molecular or self-assembled macromolecular [30
], and was hard to clear by liver [32
]. Since IgA1 is a predominant isotype of IgA in circulation [33
], serum IgA level could reflect serum IgA1 level. Some reports showed that patients with IgAN had elevated serum IgA levels, and consequently, it might be used as a potential diagnostic marker for IgAN [34
]. Nevertheless, the method by using varying degrees of serum IgA level to make a differential diagnosis for discriminating IgAN from other subtypes of kidney disease is not widely accepted. The present study indicated serum IgA level elevated in patients with both IgAN (331.3
mg/dl) and non-IgAN (241.5
mg/dl) according to the reference range 70
mg/dl (Table 1). Serum IgA, seemed like not a specific marker for IgAN, still had significant difference and differentially diagnostic value (area under curve of ROC curve: 75.6%, P
0.0001), which corroborated the views of some previously study [23
When serum IgA was combined with the other 2 parameters, particularly manifestation and fibrinogen, the diagnostic accuracy of serum IgA increased from 75.6 to 83.9%, as determined by ROC curve analysis, suggesting that, with the exception of serum IgA, clotting mechanisms might be different in the development of IgAN and non-IgAN, which reflected in the proportion of nephrotic syndrome in IgAN (17.2%) and non-IgAN (52.4%). To be precise, serum IgA was a relatively specific marker for IgAN, however fibrinogen and manifestation were two relatively specific markers for non-IgAN. In 63 non-IgAN of the modeling group, 55.6% patients were with membranous nephropathy or minor change disease (Additional file 1
). Nephrotic syndrome is the most common clinical manifestation of these two subtypes of glomerular disease [36
]. Patients with nephrotic syndrome are always in a state of hypercoagulability and hyperfibrinolysis [37
], which could be caused by the increased synthesis of blood coagulation factors in liver, the increased consumption of antithrombin, and the decreased levels of protein S, protein C and plasminogen [39
]. Therefore, as Factor I, serum fibrinogen level was higher in non-IgAN characterized by the predominance of nephrotic syndrome than in IgAN, and accordingly had discerning power between the two groups, as well as D-dimer.
Other significantly different biological parameters between IgAN and non-IgAN, such as TP, ALB, CH, TG, LDL and sIgG, were also linked to the different proportion of nephrotic syndrome (Table
), which is characterized by mass proteinuria, hypoalbuminemia, edema, and varying degrees of hyperlipidemia [36
]. Moreover, given that Ca combines with ALB in blood [41
], non-IgAN patients that appeared to have nephrotic syndrome demonstrated decreases in serum levels of Ca after a decrease in ALB. This was confirmed by the high correlation coefficient between Ca and ALB (0.813) in our analysis.
Furthermore, though DB was significantly different between IgAN and non-IgAN, the disparity of the averages was little (3.1
μmol/L vs. 2.4
μmol/L), and DB levels in most patients were normal. It is reported serum DB correlated with estimated glomerular filtration rate (eGFR) [42
], however, we did not find this correlation with eGFR calculated by the CKD-EPI equation [43
0.35, correlation coefficient
0.086) in this study. So, we believed the difference of DB between IgAN and non-IgAN had no clinical significance.
We have carried out a study for analyzing the clinical significance of serum CA125 and CA199 levels and their correlation factors in patients with chronic nephropathy, and the results indicated when patients with chronic nephropathy complicated with serous effusions or other factors favoring the formation of serous effusions, such as nephrotic syndrome, serum levels of CA125 and CA199 were apt to increase [44
]. And CA153 were also correlated with ALB (correlation coefficient
0.0001), CH (correlation coefficient
0.0001), nephrotic syndrome (correlation coefficient
0.0001), FIB (correlation coefficient
0.0001) and LDL (correlation coefficient
0.0001). Thus, these two parameters having significant difference between IgAN and non-IgAN could also due to the different proportion of nephrotic syndrome.