Infection and Drug Resistance (Mar 2023)
A Nomogram for Early Diagnosis of Community-Acquired Pneumonia Based on Bronchoalveolar Lavage Fluid Metabolomics
Abstract
Siqin Chen,1,* Minhong Su,2,* Wei Lei,1,* Zhida Wu,1 Shuhong Wu,1 Jing Liu,1 Xiaoyan Huang,1 Guiyang Chen,1 Qian Zhang,1 Hua Zhong,1 Fu Rong,1 Xi Li,1 Qiang Xiao1 1Pulmonary and Critical Care Medicine, Shunde Hospital, Southern Medical University (The First People’s Hospital of Shunde Foshan), Foshan, Guangdong, People’s Republic of China; 2Pulmonary and Critical Care Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, People’s Republic of China*These authors contributed equally to this workCorrespondence: Qiang Xiao; Xi Li, Pulmonary and Critical Care Medicine, Shunde Hospital, Southern Medical University (The First People’s Hospital of Shunde Foshan), No. 1, Jiazi Road, Lunjiao Street, Shunde District, Foshan, Guangdong, 528300, People’s Republic of China, Tel +86-757-22318689 ; +86-757-22318690, Email [email protected]; [email protected]: There is a high disease burden associated with community-acquired pneumonia (CAP) around the world. A timely and correct diagnosis of CAP can facilitate early treatment and prevent illness progression. The present study aimed to find some novel biomarkers of CAP by metabolic analysis and construct a nomogram model for precise diagnosis and individualized treatment of CAP patients.Patients and Methods: 42 CAP patients and 20 controls were enrolled in this study. The metabolic profiles of bronchoalveolar lavage fluid (BALF) samples were identified by untargeted LC-MS/MS analysis. With a VIP score ≥ 1 in OPLS-DA analysis and P < 0.05, the significantly dysregulated metabolites were estimated as potential biomarkers of CAP, which were further included in the construction of the diagnostic prediction model along with laboratory inflammatory indexes via stepwise backward regression analysis. Discrimination, calibration, and clinical applicability of the nomogram were evaluated by the C-index, the calibration curve, and the decision curve analysis (DCA) estimated by bootstrap resampling.Results: The metabolic profiles differed obviously between CAP patients and healthy controls, as shown by PCA and OPLS-DA plots. Seven metabolites significantly dysregulated in CAP were established: dimethyl disulfide, oleic acid (d5), N-acetyl-a-neuraminic acid, pyrimidine, choline, LPC (12:0/0:0) and PA (20:4/2:0). Multivariate logistic regression revealed that the expression levels of PA (20:4/2:0), N-acetyl-a-neuraminic acid, and CRP were associated with CAP. After being validated by bootstrap resampling, this model showed satisfactory diagnostic performance.Conclusion: A novel nomogram prediction model containing metabolic potential biomarkers in BALF that was developed for the early diagnosis of CAP offers insights into the pathogenesis and host response in CAP.Keywords: community-acquired pneumonia, biomarker, precision medicine, metabolomics, prediction model, logistic regression