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Journal article
1 H NMR-Based Quantitative Lipoprotein Measurement Cross-Validation with Enzymatic Methods Applied to the OMNI-Heart Dietary Intervention Study
Journal of proteome research
2025
PMID: 41460641
Abstract
Nuclear magnetic resonance (NMR) spectroscopy is increasingly employed in research to quantify lipoprotein subfractions, offering potential utility in clinical diagnostics, particularly for cardiovascular risk assessment. However, the independent validation of proprietary NMR-based lipoprotein profiling methods is crucial for verifying clinical accuracy and reliability. This study presents a posthoc evaluation of concordance between the NMR-based B.I.LISA method and standard enzymatic assays for total cholesterol (TC), triglycerides (TGs), and high-density lipoprotein cholesterol (HDL-C), measured in 620 plasma samples from the OMNI-Heart study, focusing on their performance in evaluating the dietary intervention outcomes. Despite involving independently acquired data not designed for an intermethod validation, the comparison showed a high correlation between methods (R = 0.85–0.92), with median deviations of −4, −5, and −15% for HDL-C, TC, and TGs, respectively. The larger TG deviations are attributed to known issues arising from heterogeneity in high-TG samples, although intervention outcomes remained unaffected. Albumin was identified as a potential interfering factor affecting the TC and HDL-C measurements. HDL-C could also be affected by lipoprotein degradation, contributing to divergence in comparisons of marginal intervention outcomes. Extreme discrepancies were observed in atypical hypercholesterolemia samples. These findings highlight the reliability of the NMR approach despite revealing minor but significant deviations that warrant further research.
Details
- Title
- 1 H NMR-Based Quantitative Lipoprotein Measurement Cross-Validation with Enzymatic Methods Applied to the OMNI-Heart Dietary Intervention Study
- Authors/Creators
- Andong Zhu - Murdoch UniversityReika Masuda - Murdoch UniversityPhilipp Nitschke - Murdoch UniversityGengjie Jia - Agricultural Genomics Institute at ShenzhenJianbin Yan - Chinese Academy of Agricultural SciencesElaine Holmes - Murdoch University, Centre for Computational and Systems MedicineJeremy K Nicholson - Murdoch University, Health Futures InstituteJulien Wist - Murdoch University, Centre for Computational and Systems MedicineAndres Bernal - Universidad del ValleRuey Leng Loo - Murdoch University, Centre for Computational and Systems Medicine
- Publication Details
- Journal of proteome research
- Publisher
- ACS Publications
- Number of pages
- 7
- Identifiers
- 991005849488907891
- Copyright
- © XXXX The Authors.
- Murdoch Affiliation
- Australian National Phenome Centre; Health Futures Institute; Centre for Computational and Systems Medicine
- Language
- English
- Resource Type
- Journal article
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