1. Demographic and clinical characteristics of participants
The final study population consisted of 28 patients with CAP (15 NSCAP and 13 SCAP) and 20 age, sex-matched healthy controls (HC) at multiple centers including 6 hospitals in China. As indicated in Table 1, there was a range of aetiologies of CAP including bacteria, virus, and fungus, which was typical for a heterogeneous CAP patient population. Particularly, there were no significant differences in age, sex, basal metabolic rate, smoking history, underlying disease among the three groups (p > 0.05). However, in laboratory test, the inflammatory response related indicators such as the percentages of neutrophil (NE%), lymphocyte (LY%), and monocyte (MO%), also, the levels of white blood cell (WBC), neutrophil (NE) and lymphocyte(LY) were all significantly different in the SCAP compared with that in the NSCAP (all p < 0.05). The levels of serum C-reactive protein (CRP) and procalcitonin (PCT) were both greater in the SCAP group (p < 0.05). In terms of physical examination, respiratory frequency of SCAP patients dramatically increased (p < 0.05). CURB-65, PSI was all prominently higher in the severe CAP group than in the NSCAP group (p < 0.05). Comparing the detection of pathogens between the two groups, we found that the detection rate of bacteria in patients with SCAP was higher. In addition, hospitalization days and 30-day mortality were both substantially higher in patients with SCAP than in those with NSCAP (p < 0.05) (Table 1).
2. Global lipidomic profiles of human serum
Serum lipidomic profiles of 48 subjects were generated through untargeted lipidomic profiling analysis using HPLC-MS/MS. Overall, a total of 6 categories (509 species) lipid were detected in the election spray ionization positive (ESI+) mode. Top 3 dominant categories comprised over 99% of the total lipid signal, including glycerol-phospholipids (GP, 44.78%), glycerolipids (GL, 38.19%), and sphingolipids (SP, 16.06%). The lipid profile of each of the three groups was different. The relative abundance of GP, GL, and SP in the CAP group (NSCAP and SCAP) were decreased compared with that in HC. In addition, as the severity of the CAP increases, GL continued to decrease, while the relative abundance of GP and SP increased slightly. The relative abundances of sterol lipids (ST), prenol lipids (PR), and fatty acyls (FA) were also higher in the CAP group than HC.
In the election spray ionization negative (ESI-) mode, 195 lipid species were detected, and FA was more effectively recognized. Only three types of compounds were detected in the negative ion mode, namely GP, SP, and FA. They accounted for 57.14%, 25.83%, and 17.03%, respectively. Where subclasses such as phosphatidylcholine (PC, 47.14%), sphingomyelin (SM, 22.35%) and free fatty acid (FFA, 17.03%) contributed 74.05% to the total lipid signal. Similarly, there are numerous differences in serum lipid profiles between NSCAP, SCAP and HC. Compared with HC, the relative abundance of NSCAP and SCAP group was decreased, and as the condition worsened, it continued to decline.
3. Multivariate models established by untargeted lipidomics analysis.
PCA is an effective mean for classifying data, detecting outliers, and validating the stability and reproducibility of an analytical method. All identified lipids were subjected to PCA using MetaboAnalyst 4.0 to explore the major effects that potentially drive the differences in lipid profiles in CAP patients (NSCAP and SCAP) and HC (Fig. 1a). The optimal PCA model contains eight components. First and second components explain 24.6% and 12.2% of the variability between samples, respectively. The value of R2 (cum) and Q2 (cum) (0.674 and 0.449), respectively, represented the fit and predicted power of the model. All QC samples were tightly clustered indicating that this instrument has good repeatability and stability. As shown in Fig. 1a, no obvious abnormal points were found in all serum samples. Importantly, the obvious intra-group clustering and inter-group separation between three groups suggested that whether CAP or SCAP, the patient's serum lipid profile has changed significantly.
OPLS-DA analysis identified the biggest variation in lipid profiling using a few orthogonal latent variables. In order to further eliminate the interference factors of the disease and maximize the extraction of the information on the differences in lipid mass spectra between different groups, we used a supervised clustering method to verify the OPLS-DA model of the serum samples of the subjects. To prevent over fitting, we performed a permutation test (500 iterations) on those models. The OPLS-DA score plot showed obvious discriminatory trend between both CAP group versus HC, NSCAP versus HC, SCAP versus HC, and SCAP versus SCAP (Fig. 1: b-e). The CV-ANOVA p-values for all models were less than 0.0001, indicating that all the differences between the groups were significant. The Q2Y of all models was higher than 0.9, which revealed the model has excellent interpretation ability and superior predictive power (Q2>0.5) (Table 2). After the 500-it iteration of the permutation test, the R2 and Q2 values were smaller than the original model (Table 2), and the Q2 regression line was less than 0 in the Y-axis intercept (Additional file 1: Fig. S1), which proved that the model was robust and there was no over-fitting. Notably, in the OPLS-DA model, the predictability of separating SCAP from the HC (Q2 = 0.848) was better compared with separating NSCAP from the HC (Q2 = 0.703). That is, compared with NSCAP, more changes were observed in SCAP and better isolation from the HC.
4. Differential lipids associated with CAP disease and acute exacerbations
In order identify lipids with significant differences between CAP versus HC and NSCAP versus SCAP, we normalized the relative peak intensity data of all lipids detected and performed univariate analysis. A total of 295 lipids were statistically different (FDR adjusted p-value < 0.05) between the CAP and HC. The VIP score was used to quantify the contribution of each lipid to the overall separation between two groups in the OPLS-DA model. There were 226 lipids with VIP values greater than 1 between the CAP and HC. Compared with NSCAP and SCAP at the same time, 297 lipids had statistical differences, and VIP of 246 lipids exceeded 1.
Interestingly, we found that not only did many lipids differ significantly in the CAP group, but the relative abundance also changed significantly, as the disease worsened. In order to screen out the highest potential lipid that could distinguish CAP from HC and assess the severity at the early stage. VIP> 1 and FDR <0.05 were set as the selection criteria. A total of 85 lipids met the screening criteria both in the comparison of CAP versus HC and NSCAP versus SCAP. It is a remarkable fact that most of the differential lipids were continuously up or down regulated in the CAP and SCAP group (Fig. 2a).
5. Receiver operating characteristic (ROC) curve analysis
The ROC was applied according to above results for the area under the curve (AUC) and sensitivity/specificity at the best cut-off points. We further selected lipids with AUC greater than 0.85 in the two comparisons as target lipids. There are 5 lipids in total, which are PC (16:0_18:1), PC (18:2_20:4), PC (20:5_18:2), PC (36:4), and PC (38:6). AUC (95% CI) for all five lipids above were superior to PSI (0.749, 0.550-0.892) and CURB-65 (0.772, 0.575-0.908), so they might be considered as a potential panel of lipid biomarkers for assessing the severity of CAP (Additional file 1: Fig. S2). Simultaneously, the relative abundance of PC (18: 2_20: 4), PC (20: 5_18: 2), PC (36: 4), and PC (38: 6) was significantly lower in the CAP group than in the HC group, and their abundance continued to decrease as the disease worsened. However, the relative abundance of PC (16: 0_18: 1) showed the opposite trend (Fig. 2b-f).
6. Correlation between putative biomarkers and clinical indicators
Since these clinical indicators showed abnormal distribution, Spearman’s rank correlation test was applied to further explore if the target lipids correlated with the clinical parameters: WBC, NE, LY, LY (%), MO (%) and NE (%) in serum, CRP, PCT, FiO2, PaO2/FiO2, CURB-65, and PSI. Owing to the lack of laboratory test data in the HC, we calculated the correlation between clinical indicators and differential lipids in the NSCAP and SCAP (Additional file 1: Fig. S3; Additional file 2: Table S1-S2).
According to the results of correlation analysis, target lipids mentioned above were related to indices of infection. Considering the strong co-linearity between those lipids, MLR analysis was conducted to evaluate the biochemical indices, which were independently correlated to lipids. Finally we identified that PC (18: 2_20: 4), PC (38: 6) and PC (36: 4) were negatively related to FiO2 after p value adjustment. In addition, PC (18: 2_20: 4) was inversely correlated with PCT (Fig. 3).
7. Putative lipids monitor disease progression and assess prognosis
Further, to investigate whether the putative biomarker can predict the prognosis of patients, we utilized the SCAP diagnostic threshold, which was the cut-off value, of the grouping criterion according to the ROC calculation results. Depending on the relative abundance of those lipids in plasma, patients were re-divided into low-abundance group and high-abundance group. Immediately afterwards, we calculated whether there were statistical differences in hospitalization days between the two groups. The results demonstrate that the hospitalization days in the two groups of PC (16:0_18:1), PC (36:4), PC (20:5_18:2), and PC (38:6) were statistically significant (p<0.05, Fig. 4).
Kaplan–Meier curves were used to determine if there were statistically significant differences in mortality between the high-abundance group and low-abundance group of PC (16: 0_18: 1), PC (36: 4) and PC (20: 5_18: 2) (p=0.0072, 0.0219, and 0.0491 respectively) (Fig. 5). Obviously, by monitoring the fluctuations of the above lipids, it is possible to effectively monitor the evolution of the disease and systematically assess the patient's prognosis early.