Novel protocol for metabolomics data normalization and biomarker discovery in human tears

Area of Science:

• Biochemistry
• Biomarker Discovery
• Precision Medicine

Background:

• Human tear analysis offers potential for biomarker discovery.
• Clinical utility is limited by a lack of standardized reference values for interindividual comparisons.

Purpose of the Study:

• Develop a protocol for normalizing human tear metabolomic data.
• Enhance the potential for tear biomarker identification through standardized comparisons.

Main Methods:

• Targeted metabolomic profiling of tear samples from 103 healthy donors using the AbsoluteIDQ™ p180 Kit.
• Developed a predictive normalization model incorporating age, sex, and fasting time to correct for interindividual variability.
• Identified key metabolites from six compound families as normalization references and validated the approach using Linear Discriminant Analysis (LDA).

Main Results:

• Significant interindividual variability in metabolite concentrations was observed.
• The normalization model successfully reduced variability by predicting metabolite concentrations based on reference metabolites.
• The approach enabled robust interindividual comparisons and outperformed traditional methods in sex discrimination using tear metabolomics.

Conclusions:

• The novel normalization protocol significantly enhances the reliability of tear metabolomics for standardized interindividual comparisons.
• This method facilitates biomarker discovery by mitigating metabolite concentration variability.
• The protocol's applicability may extend to other biological fluids, advancing precision medicine.