학술지
| 발행연도 | 2026/07/31 | ||
|---|---|---|---|
| 권 | 14 | 호 | 2 |
| 페이지 | 65-71(7) | ||
| 제목 | 체성분(BMI·신장·체중)이 폐기능(FVC·FEV1)에 미치는 영향 : 230명을 대상으로 한 예측모형 개발 연구 | ||
| 저자 | 이한결 | ||
| 교신저자 | 이한결 | ||
| 소속 | ■상무병원 | ||
| Abstract | Body composition influences thoracic mechanics, lung expansion, and respiratory muscle performance, making it a key determinant of pulmonary function. This study was conducted to examine the effects of body mass index(BMI), height, and weight on forced vital capacity(FVC) and forced expiratory volume in 1s(FEV1) among 2 30 adults and develop predictive models based on body composition. Methods: Height, weight, BMI, FVC, and FEV₁ were measured in 230 healthy adults. Descriptive statistics, Pearson correlation analyses, and multiple regression analyses(models 1-3) were conducted to determine the independent contributions of body composition variables. Model performance was evaluated using R², adjusted R², Akaike information criterion(AIC), and Bayesian information criterion(BIC). Independent-samples t-tests were performed to compare pulmonary function between sexes. Results: Height demonstrated the strongest positive correlation with both FVC(r=0.488) and FEV1(r=0.486), followed by weight. Although BMI exhibited a positive correlation in the univariate analysis, it emerged as a significant negative predictor in multivariable regression models. Among the models tested, model 3(included BMI, height, weight, age, and sex) demonstrated the highest explanatory power and the lowest AIC and BIC, indicating superior predictive performance. Sex-based analysis revealed that men had significantly higher FVC and FEV1 than women(p<.001). Conclusion: In this study, height was the strongest positive determinant of pulmonary function in adults, whereas BMI was independently associated with reduced lung capacity. The proposed multivariable prediction model incorporating both body composition and demographic variables yielded the most accurate estimates of FVC and FEV1. These findings support the value of body composition-based prediction models to improve respiratory assessment in clinical practice. | ||
| 키워드 | Body composition, Pulmonary function, FVC, FEV1, Predictive model | ||
| 첨부파일 #1 | 08_이한결(65-71).pdf | ||