Targeted metabolomics reveals the association between central carbon metabolism and pulmonary nodules.

Yue-Yang Wu, Wen-Bin Shen, Jian-Wei Li, Meng-Yu Liu, Wen-Lei Hu, Sheng Wang, Jian-Jun Liu, Fen Huang, Qi-Rong Qin
Author Information
  1. Yue-Yang Wu: Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China.
  2. Wen-Bin Shen: Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China.
  3. Jian-Wei Li: Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China.
  4. Meng-Yu Liu: Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China.
  5. Wen-Lei Hu: Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China.
  6. Sheng Wang: The Center for Scientific Research of Anhui Medical University, Hefei, Anhui, China.
  7. Jian-Jun Liu: Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China.
  8. Fen Huang: Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China. ORCID
  9. Qi-Rong Qin: Maanshan Center for Disease Control and Provention, Maanshan, Anhui, China.

Abstract

With the widespread application of low-dose computed tomography (LDCT) technology, pulmonary nodules have aroused more attention. Significant alteration in plasma metabolite levels, mainly amino acid and lipid, have been observed in patients of PNs. However, evidence on the association between central carbon metabolism and PNs are largely unknown. The aim of this study was to investigate the underlying association of PNs and plasma central carbon metabolites. We measured the levels of 16 plasma central carbon metabolites in 1954 participants who gained LDCT screening in MALSC cohort. The inverse probability weighting (IPW) technique was used to control for bias due to self-selection for LDCT in the assessed high-risk population. The least absolute shrinkage and selection operator (LASSO) penalized regression was used to deal with the problem of multicollinearity among metabolites and the combined association of central carbon metabolites with PNs was estimated by using quantile g-computation (QgC) models. A quartile increase in 3-hydroxybutyric acid, gluconic acid, succinic acid and hippuric acid was positively associated with the PNs risk, whereas a quartile increase in 2-oxadipic acid and fumaric acid was negatively associated with the risk of PNs in multiple-metabolite models. A positive but insignificant joint associations of the mixture of 16 metabolites with PNs was observed by using QgC models analyses. Further studies are warranted to clarify the association between circulating metabolites and PNs and the biological mechanisms.

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MeSH Term

Humans
Carbon
Multiple Pulmonary Nodules
Metabolomics
Risk Factors

Chemicals

Carbon

Word Cloud

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