Lipid profiling in nontuberculous mycobacterial pulmonary disease based on geospatial locations and disease severity

The role of lipid profiles in the pathogenesis of nontuberculous mycobacterial pulmonary disease (NTM-PD) remains largely unexplored. This study aimed to identify lipid profile variations across geospatial lung lesions, their reflection in serum relative to disease severity and their diagnostic discriminative ability using lipidomic analysis.

Lipidomics was performed using hydrophilic interaction liquid chromatography-tandem mass spectrometry on lung tissues and serum samples. 960 lipid species were analysed across geospatial lung lesions (cavity wall, centre of cavity, granuloma, bronchiectasis and normal lungs) and assessed in serum according to disease severity. Python-based machine-learning models in PyCaret were used to classify NTM-PD based on lipidomic signatures.

This study included 23 lung specimens from seven patients with NTM-PD and 332 serum samples comprising 134 patients with NTM-PD, 136 with non-NTM bronchiectasis and 62 healthy controls. Triacylglycerol (TG) levels were elevated in lung lesions affected by NTM-PD, particularly in the centre of the cavity. In serum, TG levels were higher in patients with NTM-PD than controls but decreased in patients with more severe disease, including those with acid-fast bacilli smear positivity, cavitation or higher BACES (body mass index, age, cavity, erythrocyte sedimentation rate and sex) scores. The top five models, developed using lipid species characteristically altered in NTM-PD, effectively discriminated patients with NTM-PD from healthy controls.

TG levels were elevated in lung lesions affected by NTM-PD but decreased in serum as disease severity increased, suggesting TG accumulation in lung tissues. These findings highlighted the role of lipid metabolism in the pathogenesis of NTM-PD.