A Machine Learning Algorithm for Quantitatively Diagnosing Oxidative Stress Risks in Healthy Adult Individuals Based on Health Space Methodology: A Proof-of-Concept Study Using Korean Cross-Sectional Cohort Data
- 주제(키워드) elastic net regularized generalized linear model , diagnostic model , oxidative stress , composite biomarker
- 주제(기타) Biochemistry & Molecular Biology
- 주제(기타) Chemistry, Medicinal
- 주제(기타) Food Science & Technology
- 설명문(일반) [Kim, Youjin; Kim, Yunsoo; Kwon, Oran] Ewha Womans Univ, Dept Nutr Sci & Food Management, 52 Ewhayeodae Gil, Seoul 03760, South Korea; [Hwang, Jiyoung; Kwon, Oran] Ewha Womans Univ, Dept Nutr Sci & Food Management, Grad Program Syst Hlth Sci & Engn, 52 Ewhayeodae Gil, Seoul 03760, South Korea; [van den Broek, Tim J.; Wopereis, Suzan; Bouwman, Jildau] Netherlands Org Appl Sci Res TNO, Dept Microbiol & Syst Biol, Utrechtseweg 48, NL-3704 HE Zeist, Netherlands; [Oh, Bumjo] Seoul Natl Univ, Seoul Metropolitan Govt, Dept Family Med, Boramae Med Ctr, 20 Boramae Ro 5 Gil, Seoul 07061, South Korea; [Kim, Ji Yeon] Seoul Natl Univ Sci & Technol, Dept Food Sci & Technol, 232 Gongneung Ro, Seoul 01811, South Korea
- 등재 SCIE, SCOPUS
- 발행기관 MDPI
- 발행년도 2021
- URI http://www.dcollection.net/handler/ewha/000000182301
- 본문언어 영어
- Published As http://dx.doi.org/10.3390/antiox10071132
초록/요약
Oxidative stress aggravates the progression of lifestyle-related chronic diseases. However, knowledge and practices that enable quantifying oxidative stress are still lacking. Here, we performed a proof-of-concept study to predict the oxidative stress status in a healthy population using retrospective cohort data from Boramae medical center in Korea (n = 1328). To obtain binary performance measures, we selected healthy controls versus oxidative disease cases based on the "health space" statistical methodology. We then developed a machine learning algorithm for discrimination of oxidative stress status using least absolute shrinkage and selection operator (LASSO)/elastic net regression with 10-fold cross-validation. A proposed fine-tune model included 16 features out of the full spectrum of diverse and complex data. The predictive performance was externally evaluated by generating receiver operating characteristic curves with area under the curve of 0.949 (CI 0.925 to 0.974), sensitivity of 0.923 (CI 0.879 to 0.967), and specificity of 0.855 (CI 0.795 to 0.915). Moreover, the discrimination power was confirmed by applying the proposed diagnostic model to the full dataset consisting of subjects with various degrees of oxidative stress. The results provide a feasible approach for stratifying the oxidative stress risks in the healthy population and selecting appropriate strategies for individual subjects toward implementing data-driven precision nutrition.
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