Statistical modeling of health space based on metabolic stress and oxidative stress scores

• Published in: BMC public health Vol. 22; no. 1; pp. 1 - 12
• Main Authors: Park, Cheolgyun, Kim, Youjin, Lee, Chanhee, Kim, Ji Yeon, Kwon, Oran, Park, Taesung
• Format: Journal Article
• Language: English
• Published: London BioMed Central 08.09.2022; BioMed Central Ltd; BMC
• Subjects: Biostatistics; Body mass index; Care and treatment; Chronic illnesses; Datasets; Diabetes; Diagnosis; Disease; Environmental Health; Epidemiology; Evaluation; Health space; Health status indicators; Hypertension; Lifestyles; Mathematical models; Medicine; Medicine & Public Health; Metabolic diseases; Metabolic stress; Metabolic syndrome; Oxidative stress; Public Health; Regression analysis; Regression models; Samples; Simulation; Statistical analysis; Statistical models; Statistics; Vaccine; Variance analysis; South Korea; Oxidative stress; Health space; Metabolic stress
• ISSN: 1471-2458; 1471-2458
• DOI: 10.1186/s12889-022-14081-0

Background Health space (HS) is a statistical way of visualizing individual’s health status in multi-dimensional space. In this study, we propose a novel HS in two-dimensional space based on scores of metabolic stress and of oxidative stress. Methods These scores were derived from three statistical models: logistic regression model, logistic mixed effect model, and proportional odds model. HSs were developed using Korea National Health And Nutrition Examination Survey data with 32,140 samples. To evaluate and compare the performance of the HSs, we also developed the Health Space Index (HSI) which is a quantitative performance measure based on the approximate 95% confidence ellipses of HS. Results Through simulation studies, we confirmed that HS from the proportional odds model showed highest power in discriminating health status of individual (subject). Further validation studies were conducted using two independent cohort datasets: a health examination dataset from Ewha-Boramae cohort with 862 samples and a population-based cohort from the Korea association resource project with 3,199 samples. Conclusions These validation studies using two independent datasets successfully demonstrated the usefulness of the proposed HS.