Proof of concept for identifying cystic fibrosis from perspiration samples.

Abstract

The gold standard for cystic fibrosis (CF) diagnosis is the determination of chloride concentration in sweat. Current testing methodology takes up to 3 h to complete and has recognized shortcomings on its diagnostic accuracy. We present an alternative method for the identification of CF by combining desorption electrospray ionization mass spectrometry and a machine-learning algorithm based on gradient boosted decision trees to analyze perspiration samples. This process takes as little as 2 min, and we determined its accuracy to be 98 ± 2% by cross-validation on analyzing 277 perspiration samples. With the introduction of statistical bootstrap, our method can provide a confidence estimate of our prediction, which helps diagnosis decision-making. We also identified important peaks by the feature selection algorithm and assigned the chemical structure of the metabolites by high-resolution and/or tandem mass spectrometry. We inspected the correlation between mild and severe CFTR gene mutation types and lipid profiles, suggesting a possible way to realize personalized medicine with this noninvasive, fast, and accurate method.

• Authors: Zhenpeng Zhou, Daniel Alvarez, Carlos Milla, Richard N Zare
• Journal: Proceedings of the National Academy of Sciences of the United States of America (Proc Natl Acad Sci U S A) Vol. 116 Issue 49 Pg. 24408-24412 (12 03 2019) ISSN: 1091-6490 [Electronic] United States
• PMID: 31740593 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't, Research Support, U.S. Gov't, Non-P.H.S.)

Chemical References

• CFTR protein, human
• Chlorides
• Lipids
• Cystic Fibrosis Transmembrane Conductance Regulator

Topics

• Algorithms
• Case-Control Studies
• Chlorides (analysis)
• Cystic Fibrosis (diagnosis)
• Cystic Fibrosis Transmembrane Conductance Regulator (genetics)
• Humans
• Lipids (analysis, chemistry, genetics)
• Machine Learning
• Mutation
• Proof of Concept Study
• Reproducibility of Results
• Spectrometry, Mass, Electrospray Ionization (methods, statistics & numerical data)
• Sweat (chemistry)
• Tandem Mass Spectrometry (methods, statistics & numerical data)