3. Results
The demographic characteristics of the participants in the study, includes 29 patients diagnosed with CCHF and 10 healthy individuals serving as controls. The mean age of the control group was 50.1 years, with an age range between 40 to 64 years, while the CCHF patient group had a slightly higher mean age of 50.5 years, though with a wider age range of 22 to 77 years. In total 126 blood samples were collected, their serum extracted by methanol extraction, and NMR samples were prepared for all samples. Following metabolite extraction, NMR analyses were conducted on the samples. Finally, a statistical analysis approach was applied. In the statistical analyses, all samples collected over a period of 4 days. All groups were subjected to statistical evaluation as moderate, severe, and control groups as described in materials and methods.
In PLS-DA analysis, a model is developed to include the primary components (latent variables) of the data. The first five components are considered important in classifying the data and explain the majority of the dataset. The matching score plots exhibit these first five components in two-dimensional graphs and compare each component with another (Figure 1a). Subsequently, the next step involved obtaining the Partial Least Squares Discriminant Analysis (PLS-DA) score plot, as shown in Figure 1b demonstrates a high discrimination between healthy and diseased individuals. PLS-DA effectively deals with highly dimensional data, highlighting the relevant variables responsible for the distinction, thereby providing a clear differentiation between health states based on the variation and correlation within the biomarker data. In order to examine the significant data packets responsible for this discrimination, the PLS-DA VIP Projection variable importance score plot is illustrated in Figure 1c. This technique, graphically demonstrated in Figure 1c, reveals the variables that have substantial influence on the discrimination. VIP scores signify the importance of each variable in the model, enabling us to identify and focus on the key biomarkers that are significantly altering between healthy and diseased states. This plot aids in the in-depth interpretation of the complex multivariate data and fosters a more nuanced understanding of the disease markers. Figure 1c showcases the top 15 variables that have the greatest impact on discrimination among these variables. As shown in Figure 1c, it is noticeable that several data packets exhibit low concentration values (blue) in the control group and gradually increase towards higher concentrations with the passage of time. Ultimately as depicted in Figure 1d, the listed compounds exhibited a notable increase in their concentration, as substantiated by increased signals from their corresponding data buckets. Using a statistical analysis approach consisting of three different groups. Samples taken on four consecutive days from the two patient groups were successfully differentiated from the control group. However, no discernible distinction was observed between the moderate and severe groups. Overall these compounds were detected to be elevated compared to the control: SAH, GTP, Carnosine, maleate, 2-Deoxyuridine, IMP, AMP and NADP+.
As a secondary approach to data analysis, data collected on the day 1 and day 2 were used as severe patient and moderate patient groups to further investigate the metabolite profile of the patients during the initial stages of hospitalization. Subsequently, only samples obtained on day 1 and day 2 were categorized as diseased samples, and the blood samples obtained from healthy individuals were designated as the control group for statistical analysis (Fig 2). In addition to compounds detected in our initial analysis novel compounds with significant increase during day 1 and 2 detected that may have a more active role in the patogenesis.
Finally, in a comprehensive metabonomic analysis of CCHF patients’ blood serum, distinct patterns of metabolites were observed between the severe and moderate infection levels. Specifically, through the PLS-DA score plots and Variable Importance Score (VIP) projections detailed in Figure 3, notable variations in the metabolite profiles were discerned. In the case of severe CCHF infections, the compounds AMP, IMP, and NAAD were identified as present within the serum, whereas they were not as significantly detected in the samples from patients with moderate infection levels. Conversely, GTP was found to be significantly increased in the samples of patients with moderate CCHF infection levels, but not in those categorized as severe. Overall the results outlined in our final analysis reveal a marked metabolic variety between the moderate and severe CCHF infection levels which may indicate the critical pathways in CCHF patogenesis.