Fig. 4: Correlation and association of urine biomarkers of lupus nephritis with clinical and laboratory indices.

a Creatinine-normalized urine levels of the 12 proteins (listed vertically) were Pearson correlated with various clinical and laboratory yardsticks, as listed on the x axis, in both the African-American (14 active LN, 19 inactive SLE, 14 healthy controls), Caucasian (13 active LN, 28 inactive SLE, 7 healthy controls), and Asian (80 active LN, 80 inactive SLE, 67 active non-renal lupus, 53 healthy control) patient cohorts. It should be noted that the renal biopsy data included is not from concurrent biopsies, but from previous biopsies, executed 1-mo to 20 years before urine procurement. Positive and negative correlations are denoted by orange and blue circles respectively, while statistical significance is denoted using gray-scale boxes. b The levels of the 12 urine proteins in the combined cohort (27 active LN, 47 inactive SLE, and 21 healthy controls) and their respective clinical features were subjected to Bayesian network analysis using BayesiaLab. The network shown was constructed in an unsupervised manner, using the EQ algorithm and a structural coefficient of 0.4. The circular nodes that make up the Bayesian Network represent the variables of interest, including urine biomarkers (purple-colored), clinical indices (green-colored), other features (colored gray) and disease status (active LN vs inactive SLE vs no disease; colored brown). The size of each node denotes the “node force”, which is related to its impact on other nodes in the network, based on conditional probabilities. The links (arcs) that interconnect the nodes represent informational or causal dependencies among the variables, including the correlation coefficients between neighboring nodes (as indicated), with the thickness of the link being proportional to the correlation coefficient.