High-coverage targeted lipidomics could reveal lipid alterations and evaluate therapeutic efficacy of membranous nephropathy

Abstract

Background: Membrane nephropathy (MN) often presents as nephrotic syndrome with characteristic lipid metabolism that could not be explained by lipid indicators commonly used in clinical practice. Studies have shown that invigorating spleen and qi, activating blood and detoxication in the treatment of MN is an effective method proved by randomized controlled clinical trial. However, the alterations of lipid profile before and after traditional Chinese medicine (TCM) treatment and the related lipid markers that affect the therapeutic effect have not been fully clarified.

Methods: We analyzed plasma lipid profiles of 92 patients with MN before and after TCM treatment by high-coverage targeted lipidomics.

Results: 675 lipids were identified, of which 368 stably expressed lipids (coefficient of variation less than 30% and deletion value less than 10%) were eventually included for statistical analysis. 105 lipids were altered mainly including spingolipids, glycerides, glycerophosholipid, fatty acyl and steroids, among which, the abundance of ceramides (Cers), sphingomyelins (SMs), diacylglycerols (DGs), phosphatidylcholines (PCs) were lower than those before treatment with statistically significant difference. The WGCNA network to analyze the correlation between the collective effect and the therapeutic effect showed that the triglyceride (TG) molecules were most relevant to the therapeutic effect. Analysis of 162 triglyceride molecules showed that 11 TGs were significantly down-regulated in the effective group which were concentrated in carbon atom number of 52-56 and double bond number of 0-4. TGs molecules including TG56:2-FA20:0, TG56:2-FA20:1, TG56:3-FA20:0 and TG56:5-FA20:2 were most closely related to the therapeutic effect of TCM after adjusting the influence of clinical factors. ROC curve analysis showed that these four lipids could further improve the predictive efficacy of treatment based on clinical indicators.

Conclusion: Our work demonstrated that the therapeutic effect of invigorating spleen and qi, activating blood and detoxication in the treatment of MN may be exerted by regulating lipid metabolism. High-coverage targeted lipidomics provided a non-invasive tool for discovery of lipid markers to improve the predictive efficacy of TCM therapy.

Keywords: High-coverage targeted; Lipidomics; Membranous nephropathy; Traditional Chinese medicine.

Fig. 1

Principal component analysis of test sample and QC sample

Fig. 2

Forest plot for baseline lipid moleculars and 24hpro correlation analysis

Fig. 3

Forest plot for baseline lipid moleculars and albumin correlation analysis

Fig. 4

The lipid categories with statistical difference before and after TCM treatment

Fig. 5

Boxplot of alteration in lipid abundance before and after TCM treatment

Fig. 6

Correlation heatmap among the 7 modules. (Different color bars on the left and at the bottom represent different modules, and gradient color on the right indicate Pearson correlation coefficients among modules)

Fig. 7

7 different color network plots represent the modules detected by topological overlap measure

Fig. 8

Volcano plot analysis of baseline lipid species with significant changes in different therapeutic effect groups (The significance level was P < 0.05 and the ploidy change value (|log2 (fold change) | > 0.26). Down: the relative abundance of the lipids was significantly lower than those in ineffective group. No change: the relative abundance of the lipids had no significant difference.)

Fig. 9

Ballplot analysis of structures of TGs lipid molecules in different therapeutic effect groups (Dots represent significance less than 0.05, and different colors represent different fold change expression)

Fig. 10

The correlation between 11 lipids selected and clinical indicators (○ represents the correlation is statistically significant. The size of ○ represents the correlation coefficient, × represents the correlation is not statistically significant)

Fig. 11

ROC curve for prediction of therapeutic effect of TCM. (Model 1: Adjusted for age, gender, hypertensive, diabetes, uric acid, creatinine, albumin, 24hpro; Model 2: Adjusted for Model 1, HDL, LDL, TG, CHOL; Model 3: Adjusted for Model2, TG56:2-FA20:0, TG56:2-FA20:1, TG56:3-FA20:0, TG56:5-FA20:2)