3D BodyScanner의 Body Volume Index를 활용한 비만 진단 준거기준 설정
Establishment of Obesity Diagnosis Criteria Using Body Volume Index of 3D BodyScanner
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[목적] 이 연구는 3D BodyScanner을 통해 추출되는 신체부위별 Body volume index를 활용하여 비만 진단 기준설정하는 것이 목적이다. [방법] DEXA(Dual Energy X-ray Absorptiometer)를 이용하여 225명의 참가자(남성=119, 여성=106)를 대상으로 체지방률을 측정하였고, 3D BodyScanner를 이용하여 신체 8개 부위에 대한 BVI를 측정하였다. 분석 방법으로는독립 t-검정과 ROC(Receiver Operating Characteristic) 분석을 실시하였다. ROC 분석은 AUC(Area Under the Curve)를 계산하고 Youden의 J 지수를 통해 최적의 cut-point를 산출하였다. 또한 최적의 cut-point의 타당도를 확인하기 위해 분류정확확률, 민감도, 특이도, BCR, F1-Score를 산출하였다. [결과] 남성과 여성 모두 비만 여부에 따라 신체 부위별 BVI에 통계적으로 유의한 차이가 있었으며 비만군이 정상군에비해 높은 것으로 나타났다. 남자의 경우 비만 진단을 위한 신체 부위별 최적 cut-point는 어깨 7.96, 가슴 9.79, 상복부 7.15, 하복부 7.71, 전체 복부 14.89, 허벅지 9.79, 종아리 5.70, 전체 체적 74.96로 나타났다. 여자는 어깨 6.04 가슴 9.82, 상복부 4.96, 하복부 6.23, 복부 전체 11.63, 허벅지 8.88, 종아리 4.05, 전체 체적 58.15로 나타났으며, 전체적인 정확도는 0.6~0.9로 나타났다. [결론] 이 연구를 통해 BVI는 비만을 진단하는데 유용한 지표로 활용될 수 있음 확인하였으며 준거기준도 제시하였다. 다만, 인종이나 연령에 따라 차이가 있으수 있으므로 아시아 성인에게만 적용이 가능하다.
Abstract
PURPOSE This study sought to establish obesity diagnosis criteria by using the Body Volume Index (BVI) by body part extracted through 3D BodyScanner. METHODS The body fat percentage was measured using Dual Energy X-ray Absorptiometer (DEXA) for 225 participants (male = 119, female = 106), and BVI for eight body parts was measured using 3D BodyScanner. Independent t-test and Receiver Operating Characteristic (ROC) analysis were conducted. ROC analysis calculated the Area Under the Curve (AUC), and the optimal cut-point by Youden's J index. Sensitivity, Specificity, Accuracy, Balanced Classification Rate (BCR), and F1-score (harmonic mean of recall and precision) values were calculated to verify the validity of the optimal cut-point. RESULTS A statistically significant difference was observed in BVI by body part according to whether obesity was present for both men and women, and the obese group higher than the normal group. The optimal cut-point for each body part to diagnose obesity was 7.96 for shoulder, 9.79 for chest, 7.15 for upper abdominal, 7.71 for lower abdominal, 14.89 for total abdominal, 9.79 for thigh, 5.70 for calf, and 74.96 for total body volume in men. In case of women, this was 6.04 for shoulder, 9.82 for chest, 4.96 for upper abdominal, 6.23 for lower abdominal, 11.63 for total abdominal, 8.88 for thigh, 4.05 for calf, and 58.15 for total body volume, and the accuracy was 0.6~0.9. CONCLUSIONS BVI is a useful indicator for diagnosing obesity. However, this can be applicable only to Asian adults since there may be differences depending on race or age.
참고문헌 (44)
- ACSM(American College of Sports Medicine), "ACSM’s guidelines for exercise testing and prescription" (2022), Wolters Kluwer
- Ashwell, M., "Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors : Systematic review and metaanalysis", Obesity Reviews, 13(3), pp.275~286 (2012)
- Bennett, J. P., "Assessment of clinical measures of total and regional body composition from a commercial 3-dimensional optical body scanner", Clinical Nutrition, 41(1), pp.211~218 (2022)
- Blüher, M., "Obesity : Global epidemiology and pathogenesis", Nature Reviews Endocrinology, 15(5), pp.288~298 (2019)
- Browning, L. M., "A systematic review of waist-to-height ratio as a screening tool for the prediction of cardiovascular disease and diabetes : 0·5 could be a suitable global boundary value", Nutrition Research Reviews, 23(2), pp.247~269 (2010)
- Burkhauser, R. V., "Beyond BMI : The value of more accurate measures of fatness and obesity in social science research", Journal of Health Economics, 27(2), pp.519~529 (2008)
- Fang, H., "How to best assess abdominal obesity", Current Opinion in Clinical Nutrition &Metabolic Care, 21(5), pp.360~365 (2018)
- Faul, F., "G*Power 3 : A flexible statistical power analysis program for the social, behavioral, and biomedical sciences", Behavior research methods, 39(2), pp.175~191 (2007)
- Giachetti, A., "Robust automatic measurement of 3 D scanned models for the human body fat estimation", IEEE journal of biomedical and health informatics, 19(2), pp.660~667 (2014)
- Goodpaster, B. H., "Composition of skeletal muscle evaluated with computed tomography", Annals of the New York Academy of Sciences, 904(1), pp.18~24 (2000)
- Heymsfield, S. B., "Digital anthropometry : A critical review", European Journal of Clinical Nutrition, 72(5), pp.680~687 (2018)
- Hyun, Y. J., "Evaluation of metabolic syndrome risk in Korean premenopausal women : Not waist circumference but visceral fat", Circulation Journal, 72(8), pp.1308~1315 (2008)
- Jackson, A. S., "Generalized equations for predicting body density of women", Medicine and Science in Sports and Exercise, 12(3), pp.175~181 (1980)
- Jeon, S., "Machine learning-based obesity classification considering 3D body scanner measurements", Scientific Reports, 13(1), pp.3299~ (2023)
- Lambert, B. S., "DEXA or BMI : Clinical considerations for evaluating obesity in collegiate division I-A American football athletes", Clinical Journal of Sport Medicine, 22(5), pp.436~438 (2012)
- Lee, C. M. Y., "Indices of abdominal obesity are better discriminators of cardiovascular risk factors than BMI : A meta-analysis", Journal of Clinical Epidemiology, 61(7), pp.646~653 (2008)
- Li, R., "Relationships between indices of obesity and its cardiovascular comorbidities in a Chinese population", Circulation Journal, 72(6), pp.973~978 (2008)
- Lohman, T. G., "Skinfolds and body density and their relation to body fatness : A review", Human Biology, 53(2), pp.181~225 (1981)
- Lohman, T. G., "Methodological factors and the prediction of body fat in female athletes", Medicine and Science in Sports and Exercise, 16(1), pp.92~96 (1984)
- Medina-Inojosa, J., "Reliability of a 3D body scanner for anthropometric measurements of central obesity", Obesity, Open Access, 2(3) (2016)[DOI]
- Medina-Inojosa, J., "Validation of a white-light 3D body volume scanner to assess body composition", Obesity, Open Access, 3(1) (2017)[DOI]
- Nam, G. E., "Obesity fact sheet in Korea, 2019 : Prevalence of obesity and abdominal obesity from 2009 to 2018and social factors", Journal of Obesity & Metabolic Syndrome, 29(2), pp.124~132 (2020)
- Nuttall, F. Q., "Body Mass Index: Obesity, BMI, and health: A critical review", Nutrition Today, 50(3), pp.117~128 (2015)
- Lee, S. Y., "The Classification Validity of Obese Body Shapes using 3D Body Data", Korean journal of physical education, 61(6), pp.515~527 (2022)
- O’brien, C., "Bioelectrical impedance to estimate changes in hydration status", International journal of sports medicine, 23(05), pp.361~366 (2002)
- Oh, S. W., "Obesity and metabolic syndrome in Korea", Diabetes & Metabolism Journal, 35(6), pp.561~566 (2011)
- Park, H. J., "Trends and cut-point changes in obesity parameters by age groups considering metabolic syndrome", Journal of Korean Medical Science, 33(7), pp.e47~ (2018)
- Park, H. -Y., "Twelve weeks of aerobic exercise at the lactate threshold improves autonomic nervous system function, body composition, and aerobic performance in women with obesity", Journal of Obesity & Metabolic Syndrome, 29(1), pp.67~75 (2020)
- Peterson, C. M., "Tri-ponderal mass index vs Body Mass Index in estimating body fat during adolescence", JAMA Pediatrics, 171(7), pp.629~636 (2017)
- Pischon, T., "General and abdominal adiposity and risk of death in Europe", The New England Journal of Medicine, 359(20), pp.2105~2120 (2008)
- Prentice, A. M., "Beyond Body Mass Index", Obesity Reviews, 2(3), pp.141~147 (2001)
- Romero-Corral, A., "Accuracy of Body Mass Index in diagnosing obesity in the adult general population", International Journal of Obesity, 32(6), pp.959~966 (2008)
- Ross, R., "Magnetic resonance imaging in human body composition research : From quantitative to qualitative tissue measurement", Annals of the New York Academy of Sciences, 904(1), pp.12~17 (2000)
- Rothman, K. J., "BMI-related errors in the measurement of obesity", International Journal of Obesity, 32(3), pp.S56~S59 (2008)
- Savva, S. C., "Predicting cardiometabolic risk: Waist-to-height ratio or BMI. A metaanalysis", Diabetes, Metabolic Syndrome and Obesity, 6, pp.403~419 (2013)
- Segal, K. R., "Estimation of human body composition by electrical impedance methods : a comparative study", Journal of applied physiology, 58(5), pp.1565~1571 (1985)
- Seo, J. A, "The cutoff values of visceral fat area and waist circumference for identifying subjects at risk for metabolic syndrome in elderly Korean: Ansan Geriatric (AGE) cohort study", BMC Public Health, 9, pp.443~ (2009)
- Shao, J., "Waist-to-height ratio, an optimal predictor for obesity and metabolic syndrome in Chinese adults", The Journal of Nutrition, Health & Aging, 14(9), pp.782~785 (2010)
- Silver, B., "Reliability and minimal detectable change of the Styku 3D body scanner", Measurement in Physical Education and Exercise Science, 24(3), pp.165~171 (2020)
- Šimundić, A.-M., "Measures of diagnostic accuracy : Basic definitions", eJIFCC, 19(4), pp.203~211 (2009)
- Smit, H. J., "Electrical impedance tomography to measure pulmonary perfusion : is the reproducibility high enough for clinical practice?", Physiological measurement, 24(2), pp.491~ (2003)
- Tahrani, A., "Body Volume Index: Time to replace Body Mass Index?", Endocrine Abstracts, 15, pp.P104~ (2008)
- Watts, K., "Do skinfolds accurately assess changes in body fat in obese children and adolescents", Medicine and Science in Sports and Exercise, 38(3), pp.439~444 (2006)
- "Obesity and overweight" (2021)[URL]