Joan Serrano-Marín, Silvia Marin, Alberto Iglesias, Jaume Lillo, Claudia Garrigós, Toni Capó, Irene Reyes-Resina, Hanan Awad Alkozi, Marta Cascante, Juan Sánchez-Navés, Rafael Franco, David Bernal-Casas
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引用次数: 0
Abstract
Objectives: Human tear analysis holds promise for biomarker discovery, but its clinical utility is hindered by the lack of standardized reference values, limiting interindividual comparisons. This study aimed at developing a protocol for normalizing metabolomic data from human tears, enhancing its potential for biomarker identification.
Methods: Tear metabolomic profiling was conducted on 103 donors (64 females, 39 males, aged 18-82 years) without ocular pathology, using the AbsoluteIDQ™ p180 Kit for targeted metabolomics. A predictive normalization model incorporating age, sex, and fasting time was developed to correct for interindividual variability. Key metabolites from six compound families (amino acids, biogenic amines, acylcarnitines, lysophosphatidylcholines, phosphatidylcholines, and sphingomyelins) were identified as normalization references. The approach was validated using Linear Discriminant Analysis (LDA) to test its ability to classify donor sex based on metabolite concentrations.
Results: Metabolite concentrations exhibited significant interindividual variability. The normalization model, which predicted metabolite concentrations based on a reference "concomitant" metabolite from each compound family, successfully reduced this variability. Using the ratio of observed-to-predicted concentrations, the model enabled robust comparisons across individuals. LDA classification of donor sex using acylcarnitine C4 achieved 78 % accuracy, correctly identifying 92 % of female donors. This approach outperformed traditional statistical and machine learning methods (Lasso logistic regression and Random Forest classification) in sex discrimination based on tear metabolomics.
Conclusions: This novel normalization protocol significantly improves the reliability of tear metabolomics by enabling standardized interindividual comparisons. The approach facilitates biomarker discovery by mitigating variability in metabolite concentrations and may be extended to other biological fluids, enhancing its applicability in precision medicine.
期刊介绍:
Clinical Chemistry and Laboratory Medicine (CCLM) publishes articles on novel teaching and training methods applicable to laboratory medicine. CCLM welcomes contributions on the progress in fundamental and applied research and cutting-edge clinical laboratory medicine. It is one of the leading journals in the field, with an impact factor over 3. CCLM is issued monthly, and it is published in print and electronically.
CCLM is the official journal of the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) and publishes regularly EFLM recommendations and news. CCLM is the official journal of the National Societies from Austria (ÖGLMKC); Belgium (RBSLM); Germany (DGKL); Hungary (MLDT); Ireland (ACBI); Italy (SIBioC); Portugal (SPML); and Slovenia (SZKK); and it is affiliated to AACB (Australia) and SFBC (France).
Topics:
- clinical biochemistry
- clinical genomics and molecular biology
- clinical haematology and coagulation
- clinical immunology and autoimmunity
- clinical microbiology
- drug monitoring and analysis
- evaluation of diagnostic biomarkers
- disease-oriented topics (cardiovascular disease, cancer diagnostics, diabetes)
- new reagents, instrumentation and technologies
- new methodologies
- reference materials and methods
- reference values and decision limits
- quality and safety in laboratory medicine
- translational laboratory medicine
- clinical metrology
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