Jonathan P Bennett1,2, Yong En Liu2, Brandon K Quon2, Nisa N Kelly2, Lambert T Leong2, Michael C Wong1,2, Samantha F Kennedy3, Dominic C Chow4, Andrea K Garber5, Ethan J Weiss6, Steven B Heymsfield3, John A Shepherd1,2. 1. Graduate Program in Human Nutrition, University of Hawai'i Manoa, Honolulu, Hawaii, USA. 2. Department of Epidemiology, University of Hawai'i Cancer Center, Honolulu, Hawaii, USA. 3. Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, Louisiana, USA. 4. John A. Burns School of Medicine, University of Hawai'i Manoa, Honolulu, Hawaii, USA. 5. Division of Adolescent & Young Adult Medicine, University of California, San Francisco, California, USA. 6. Division of Cardiology, University of California School of Medicine, San Francisco, California, USA.
Abstract
OBJECTIVE: This study examined whether body shape and composition obtained by three-dimensional optical (3DO) scanning improved the prediction of metabolic syndrome (MetS) prevalence compared with BMI and demographics. METHODS: A diverse ambulatory adult population underwent whole-body 3DO scanning, blood tests, manual anthropometrics, and blood pressure assessment in the Shape Up! Adults study. MetS prevalence was evaluated based on 2005 National Cholesterol Education Program criteria, and prediction of MetS involved logistic regression to assess (1) BMI, (2) demographics-adjusted BMI, (3) 85 3DO anthropometry and body composition measures, and (4) BMI + 3DO + demographics models. Receiver operating characteristic area under the curve (AUC) values were generated for each predictive model. RESULTS: A total of 501 participants (280 female) were recruited, with 87 meeting the criteria for MetS. Compared with the BMI model (AUC = 0.819), inclusion of age, sex, and race increased the AUC to 0.861, and inclusion of 3DO measures further increased the AUC to 0.917. The overall integrated discrimination improvement between the 3DO + demographics and the BMI model was 0.290 (p < 0.0001) with a net reclassification improvement of 0.214 (p < 0.0001). CONCLUSIONS: Body shape measures from an accessible 3DO scan, adjusted for demographics, predicted MetS better than demographics and/or BMI alone. Risk classification in this population increased by 29% when using 3DO scanning.
OBJECTIVE: This study examined whether body shape and composition obtained by three-dimensional optical (3DO) scanning improved the prediction of metabolic syndrome (MetS) prevalence compared with BMI and demographics. METHODS: A diverse ambulatory adult population underwent whole-body 3DO scanning, blood tests, manual anthropometrics, and blood pressure assessment in the Shape Up! Adults study. MetS prevalence was evaluated based on 2005 National Cholesterol Education Program criteria, and prediction of MetS involved logistic regression to assess (1) BMI, (2) demographics-adjusted BMI, (3) 85 3DO anthropometry and body composition measures, and (4) BMI + 3DO + demographics models. Receiver operating characteristic area under the curve (AUC) values were generated for each predictive model. RESULTS: A total of 501 participants (280 female) were recruited, with 87 meeting the criteria for MetS. Compared with the BMI model (AUC = 0.819), inclusion of age, sex, and race increased the AUC to 0.861, and inclusion of 3DO measures further increased the AUC to 0.917. The overall integrated discrimination improvement between the 3DO + demographics and the BMI model was 0.290 (p < 0.0001) with a net reclassification improvement of 0.214 (p < 0.0001). CONCLUSIONS: Body shape measures from an accessible 3DO scan, adjusted for demographics, predicted MetS better than demographics and/or BMI alone. Risk classification in this population increased by 29% when using 3DO scanning.
Authors: Jonathan P Bennett; Yong En Liu; Brandon K Quon; Nisa N Kelly; Michael C Wong; Samantha F Kennedy; Dominic C Chow; Andrea K Garber; Ethan J Weiss; Steven B Heymsfield; John A Shepherd Journal: Clin Nutr Date: 2021-12-07 Impact factor: 7.643
Authors: D M Boudreau; D C Malone; M A Raebel; P A Fishman; G A Nichols; A C Feldstein; A N Boscoe; R H Ben-Joseph; D J Magid; L J Okamoto Journal: Metab Syndr Relat Disord Date: 2009-08 Impact factor: 1.894
Authors: Sandra N Slagter; Robert P van Waateringe; André P van Beek; Melanie M van der Klauw; Bruce H R Wolffenbuttel; Jana V van Vliet-Ostaptchouk Journal: Endocr Connect Date: 2017-04-18 Impact factor: 3.335