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

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 retrospecti...

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Vydáno v:Antioxidants Ročník 10; číslo 7; s. 1132
Hlavní autoři: Kim, Youjin, Kim, Yunsoo, Hwang, Jiyoung, van den Broek, Tim J., Oh, Bumjo, Kim, Ji Yeon, Wopereis, Suzan, Bouwman, Jildau, Kwon, Oran
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 16.07.2021
MDPI
Témata:
adults
Algorithms
Artificial intelligence
Blood
Chronic illnesses
composite biomarker
data collection
Datasets
diagnostic model
Disease
elastic net regularized generalized linear model
Fines & penalties
Generalized linear models
Hemoglobin
Korean Peninsula
Learning algorithms
Machine learning
medical facilities
Metabolic syndrome
nutrition
Nutrition research
Oxidative stress
Principal components analysis
Statistical analysis
Variables
Korean Peninsula
ISSN:2076-3921, 2076-3921
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Abstract 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.
AbstractList 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.
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.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.
Author Kim, Youjin
Hwang, Jiyoung
Wopereis, Suzan
Bouwman, Jildau
Kim, Ji Yeon
Kim, Yunsoo
Oh, Bumjo
Kwon, Oran
van den Broek, Tim J.
AuthorAffiliation 3 Netherlands Organization for Applied Scientific Research (TNO), Department of Microbiology and Systems Biology, Utrechtseweg 48, 3704 HE Zeist, The Netherlands; tim.vandenbroek@tno.nl (T.J.v.d.B.); suzan.wopereis@tno.nl (S.W.)
2 Department of Nutritional Science and Food Management, Graduate Program in System Health Science and Engineering, Ewha Womans University, 52 Ewhayeodae-gil, Seodeamun-gu, Seoul 03760, Korea; cindy.jyhwang@gmail.com
5 Department of Food Science and Technology, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul 01811, Korea; jiyeonk@seoultech.ac.kr
1 Department of Nutritional Science and Food Management, Ewha Womans University, 52 Ewhayeodae-gil, Seodeamun-gu, Seoul 03760, Korea; Youjin.Kim631782@tufts.edu (Y.K.); sookim726@gmail.com (Y.K.)
4 Boramae Medical Center, Department of Family Medicine, Seoul Metropolitan Government-Seoul National University, 20 Boramae-ro 5-gil, Dongjak-gu, Seoul 07061, Korea; bo39@snu.ac.kr
AuthorAffiliation_xml – name: 4 Boramae Medical Center, Department of Family Medicine, Seoul Metropolitan Government-Seoul National University, 20 Boramae-ro 5-gil, Dongjak-gu, Seoul 07061, Korea; bo39@snu.ac.kr
– name: 3 Netherlands Organization for Applied Scientific Research (TNO), Department of Microbiology and Systems Biology, Utrechtseweg 48, 3704 HE Zeist, The Netherlands; tim.vandenbroek@tno.nl (T.J.v.d.B.); suzan.wopereis@tno.nl (S.W.)
– name: 2 Department of Nutritional Science and Food Management, Graduate Program in System Health Science and Engineering, Ewha Womans University, 52 Ewhayeodae-gil, Seodeamun-gu, Seoul 03760, Korea; cindy.jyhwang@gmail.com
– name: 1 Department of Nutritional Science and Food Management, Ewha Womans University, 52 Ewhayeodae-gil, Seodeamun-gu, Seoul 03760, Korea; Youjin.Kim631782@tufts.edu (Y.K.); sookim726@gmail.com (Y.K.)
– name: 5 Department of Food Science and Technology, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul 01811, Korea; jiyeonk@seoultech.ac.kr
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Snippet Oxidative stress aggravates the progression of lifestyle-related chronic diseases. However, knowledge and practices that enable quantifying oxidative stress...
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SubjectTerms adults
Algorithms
Artificial intelligence
Blood
Chronic illnesses
composite biomarker
data collection
Datasets
diagnostic model
Disease
elastic net regularized generalized linear model
Fines & penalties
Generalized linear models
Hemoglobin
Korean Peninsula
Learning algorithms
Machine learning
medical facilities
Metabolic syndrome
nutrition
Nutrition research
Oxidative stress
Principal components analysis
Statistical analysis
Variables
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Title 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
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