B-353 Circulating lipidomic patterns are associated with pulmonary nontuberculous mycobacteria infection status

Abstract

Nontuberculous mycobacterial pulmonary disease (NTM-PD) increases worldwide; however, the current diagnosis and disease monitoring methods are far from optimal. The standard diagnostic approach relies heavily on culturing respiratory samples, which remains the gold standard for both diagnosis and evaluating disease activity. However, respiratory sample culture-based methods have limitations, including prolonged turnaround time, dependency on viable bacterial load and reliance on the patient's ability to expectorate sputum and the quality of the collected sample. In this study, we aimed to identify serum metabolic profiles capable of distinguishing NTM-PD patients (both at baseline and post-treatment) from healthy individuals through targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. Participants with NTM-PD (n=96) and health controls (n=110) were recruited at a tertiary referral hospital in South Korea. Blood samples were collected from patients prior to the initiation of NTM-PD treatment, with additional samples obtained during or at the end of treatment in 21 patients. A targeted quantitative approach using a LC-MS/MS assay (AbsoluteIDQ 180 kit, Biocrates, Innsbruck, Austria) was able to simultaneously quantify 188 metabolites. A total of 57 of the 188 metabolites were not detected in one or more samples and were therefore excluded from the subsequent analysis. The remaining 131 metabolites were selected for statistical analysis. To assess population clustering and the parameters responsible for the distinction among groups, multivariate analyses were conducted using unsupervised principal component analysis (PCA) by in-house R code. Comparison between healthy control group and NTM-PD baseline groups was intended for diagnostic marker discovery, while comparison between NTM-PD pre-treatment group and NTM-PD post-treatment group in pairwise manner was aimed for prognostic marker discovery. PCA was conducted to distinguish baseline NTM-PD patients from healthy controls for diagnostic biomarker discovery and to evaluate metabolic changes pre- and post-treatment for prognostic biomarker identification. The glycerophospholipids demonstrated strong clustering power for both diagnostic (Figure 1A) and prognostic purposes (Figure 1B). In contrast, no clear clustering patterns were observed when non-lipid metabolites, such as amino acids and biogenic amines, or acyl-carnitines were applied for grouping. Glycerophospholipids were further categorized into three subgroups: lysophosphatidylcholines, phosphatidylcholines (PC) diacyl (PC aa), and PC acyl-alkyl (PC ae). The volcano plot analysis identified PC ae C38:1, PC ae C38:2, PC aa C40:2, and PC aa C40:3 as elevated metabolites and PC ae C36:4 as a decreased metabolite in the baseline NTM group compared to controls (Figure 1C). These metabolites showed inverse fold change patterns between pre- and post-treatment comparisons (Figure 1D). These five lipid metabolites showed excellent discriminative power for distinguishing NTM infection from healthy controls, however, these lipid metabolites demonstrated moderate discriminative power for differentiating pre- and post-treatment statuses (Figure 1E). Unique circulating lipidomic pattern in patients with NTM-PD was revealed through metabolomics profiling. Novel serum biomarkers of PC ae C38:1, PCaeC38:2, PC aa C40:3 and PC ae C36:4 are potentially useful for NTM-PD diagnosis and treatment monitoring.