Breath biomarkers for lung cancer detection and assessment of smoking related effects--confounding variables, influence of normalization and statistical algorithms

Clin Chim Acta. 2010 Nov 11;411(21-22):1637-44. doi: 10.1016/j.cca.2010.06.005. Epub 2010 Jun 10.


Background: Up to now, none of the breath biomarkers or marker sets proposed for cancer recognition has reached clinical relevance. Possible reasons are the lack of standardized methods of sampling, analysis and data processing and effects of environmental contaminants.

Methods: Concentration profiles of endogenous and exogenous breath markers were determined in exhaled breath of 31 lung cancer patients, 31 smokers and 31 healthy controls by means of SPME-GC-MS. Different correcting and normalization algorithms and a principal component analysis were applied to the data.

Results: Differences of exhalation profiles in cancer and non-cancer patients did not persist if physiology and confounding variables were taken into account. Smoking history, inspired substance concentrations, age and gender were recognized as the most important confounding variables. Normalization onto PCO2 or BSA or correction for inspired concentrations only partially solved the problem. In contrast, previous smoking behaviour could be recognized unequivocally.

Conclusion: Exhaled substance concentrations may depend on a variety of parameters other than the disease under investigation. Normalization and correcting parameters have to be chosen with care as compensating effects may be different from one substance to the other. Only well-founded biomarker identification, normalization and data processing will provide clinically relevant information from breath analysis.

Publication types

  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Adult
  • Aged
  • Algorithms
  • Biomarkers / analysis
  • Breath Tests / methods*
  • Case-Control Studies
  • Data Interpretation, Statistical
  • Female
  • Humans
  • Lung Neoplasms / diagnosis*
  • Male
  • Reference Values
  • Research Design / standards
  • Smoking / adverse effects*


  • Biomarkers