Personalized evaluation based on quantitative proteomics for drug-treated patients with chronic kidney disease

The patient's response to drug treatment is usually systems-wide based on multi-spots through either direct or indirect targets. Thus, the evaluation of the treatment cannot rely on single targeted biomarker, especially for complex diseases such as chronic kidney disease. In the present study, we performed a systems-wide analysis using proteomic approach to quantify changes in the proteomic profiles of the plasma from IgA nephropathy (IgAN) patients before and after treatment. In particular, the patient-to-health distances based on global proteome quantification before and after treatment were calculated and considered as quantitative readouts to measure patient divergences from the healthy condition. We found that the patient-to-health distance nicely correlated with the patient's response to drug treatment and long-term prognosis, which created a self-tracking platform for personalized evaluation. In addition, the steroid treatment plays a role in immunosuppression, while the Chinese Traditional Medicine (TCM) can modulate whole-body systems. Our results indicated that STC therapy normalized the proteomic profile more significantly than SA therapy. This work provides an omics-based and systematic platform for personalized evaluation of disease treatment. This strategy could help us to evaluate treatment outcomes and predict prognosis in patients with IgAN and other complex diseases.