Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163103
Title: Clinical- and omics-based models of subclinical atherosclerosis in healthy Chinese adults: a cross-sectional exploratory study
Authors: Valsesia, Armand
Egli, Leonie
Bosco, Nabil
Magkos, Faidon
Kong, Siew Ching
Sun, Lijuan
Goh, Hui Jen
Huang, Weiting
Arigoni, Fabrizio
Leow, Melvin Khee-Shing
Yeo, Khung Keong
Actis-Goretta, Lucas
Keywords: Science::Medicine
Issue Date: 2021
Source: Valsesia, A., Egli, L., Bosco, N., Magkos, F., Kong, S. C., Sun, L., Goh, H. J., Huang, W., Arigoni, F., Leow, M. K., Yeo, K. K. & Actis-Goretta, L. (2021). Clinical- and omics-based models of subclinical atherosclerosis in healthy Chinese adults: a cross-sectional exploratory study. The American Journal of Clinical Nutrition, 114(5), 1752-1762. https://dx.doi.org/10.1093/ajcn/nqab269
Journal: The American Journal of Clinical Nutrition
Abstract: Background: Classical risk factors, such as fasting cholesterol, blood pressure (BP), and diabetes status are used today to predict the risk of developing cardiovascular disease (CVD). However, accurate prediction remains limited, particularly in low-risk groups such as women and younger individuals. Growing evidence suggests that biomarker concentrations following consumption of a meal challenge are better and earlier predictors of disease development than biomarker concentrations. Objective: To test the hypothesis that postprandial responses of circulating biomarkers differ between healthy subjects with and without subclinical atherosclerosis (SA) in an Asian population at low risk of coronary artery disease (CAD). Methods: One hundred healthy Chinese subjects (46 women, 54 men) completed the study. Subjects consumed a mixed-meal test and 164 blood biomarkers were analyzed over 6 h by using a combination of chemical and NMR techniques. Models were trained using different methodologies (including logistic regression, elastic net, random forest, sparse partial least square) on a random 75% subset of the data, and their performance was evaluated on the remaining 25%. Results: We found that models based on baseline clinical parameters or fasting biomarkers could not reliably predict SA. By contrast, an omics model based on magnitude and timing of postprandial biomarkers achieved high performance [receiving operating characteristic (ROC) AUC: 91%; 95% CI: 77, 100). Investigation of key features of this model enabled derivation of a considerably simpler model, solely based on postprandial BP and age, with excellent performance (AUC: 91%; 95% CI: 78, 100). Conclusion: We report a novel model to detect SA based on postprandial BP and age in a population of Asian subjects at low risk of CAD. The use of this model in large-scale CVD prevention programs should be explored.
URI: https://hdl.handle.net/10356/163103
ISSN: 0002-9165
DOI: 10.1093/ajcn/nqab269
Rights: © 2021 The Author(s). Published by Oxford University Press on behalf of the American Society for Nutrition. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:LKCMedicine Journal Articles

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