M. Wong, J. Bennett, L. Leong, I. Tian, Y. E. Liu, N. Kelly, C. McCarthy, Julia M. W. Wong, C. Ebbeling, D. S. Ludwig, B. Irving, Matthew C. Scott, James E. Stampley, B. Davis, Neil M. Johannsen, Rachel Matthews, Cullen M Vincellette, A. Garber, G. Maskarinec, E. Weiss, J. Rood, Alyssa N. Varanoske, S. Pasiakos, S. Heymsfield, J. Shepherd
{"title":"先进的三维光学成像技术与双能x射线吸收仪比较,监测身体成分变化进行干预研究","authors":"M. Wong, J. Bennett, L. Leong, I. Tian, Y. E. Liu, N. Kelly, C. McCarthy, Julia M. W. Wong, C. Ebbeling, D. S. Ludwig, B. Irving, Matthew C. Scott, James E. Stampley, B. Davis, Neil M. Johannsen, Rachel Matthews, Cullen M Vincellette, A. Garber, G. Maskarinec, E. Weiss, J. Rood, Alyssa N. Varanoske, S. Pasiakos, S. Heymsfield, J. Shepherd","doi":"10.1101/2022.11.14.22281814","DOIUrl":null,"url":null,"abstract":"Recent 3D optical (3DO) imaging advancements have provided more accessible, affordable, and self-operating opportunities for assessing body composition. 3DO is accurate and precise with respect to clinical measures made by dual-energy X-ray absorptiometry (DXA). However, the sensitivity for monitoring body composition change over time with 3DO body shape is unknown. Therefore, this study aimed to evaluate 3DO ability to monitor body composition changes across multiple intervention studies. A retrospective analysis was performed using intervention studies on healthy adults that were complimentary to the cross-sectional study, Shape Up! Adults. Each participant received a DXA (Hologic Discovery/A system) and 3DO (Fit3D ProScanner) scan at baseline and follow-up. 3DO meshes were digitally registered and reposed using Meshcapade to standardize the vertices and pose. Each 3DO mesh was transformed into principal components (PCs) using an established statistical shape model. The PCs were used to predict whole-body and regional body composition values using published equations. Body composition changes (follow-up minus baseline) were compared to DXA with linear regression. The analysis included 128 participants (43 females) in six studies. The mean (SD) length of follow-up was 13 (5) weeks, range of 3-23 weeks; change in percent fat was 2.8% (2.1%), with a range of 2.4 - 8.3%. Agreement between 3DO and DXA (R2) for changes in total fat mass (FM), total fat-free mass (FFM), and appendicular lean mass, respectively, were 0.89, 0.77, and 0.69 with RMSEs of 1.78 kg, 1.42 kg, and 0.37 kg in females, and 0.77, 0.76, and 0.46 with RMSEs of 2.28 kg, 1.67 kg, and 0.51 kg in males. Statistical significance of individual changes agreed for both DXA and 3DO in the majority of the sample for total FM (70%) and FFM (81%). Further adjustment with demographic descriptors improved the 3DO change agreement to changes observed with DXA. Compared to DXA, 3DO was highly sensitive in detecting body shape changes over time. The 3DO method was sensitive enough to detect even small changes in body composition during intervention studies. The safety and accessibility of 3DO allow users to self-monitor frequently throughout interventions.","PeriodicalId":315016,"journal":{"name":"The American journal of clinical nutrition","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Monitoring Body Composition Change for Intervention Studies with Advancing 3D Optical Imaging Technology in Comparison to Dual-Energy X-Ray Absorptiometry\",\"authors\":\"M. Wong, J. Bennett, L. Leong, I. Tian, Y. E. Liu, N. Kelly, C. McCarthy, Julia M. W. Wong, C. Ebbeling, D. S. Ludwig, B. Irving, Matthew C. Scott, James E. Stampley, B. Davis, Neil M. Johannsen, Rachel Matthews, Cullen M Vincellette, A. Garber, G. Maskarinec, E. Weiss, J. Rood, Alyssa N. Varanoske, S. Pasiakos, S. Heymsfield, J. Shepherd\",\"doi\":\"10.1101/2022.11.14.22281814\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recent 3D optical (3DO) imaging advancements have provided more accessible, affordable, and self-operating opportunities for assessing body composition. 3DO is accurate and precise with respect to clinical measures made by dual-energy X-ray absorptiometry (DXA). However, the sensitivity for monitoring body composition change over time with 3DO body shape is unknown. Therefore, this study aimed to evaluate 3DO ability to monitor body composition changes across multiple intervention studies. A retrospective analysis was performed using intervention studies on healthy adults that were complimentary to the cross-sectional study, Shape Up! Adults. Each participant received a DXA (Hologic Discovery/A system) and 3DO (Fit3D ProScanner) scan at baseline and follow-up. 3DO meshes were digitally registered and reposed using Meshcapade to standardize the vertices and pose. Each 3DO mesh was transformed into principal components (PCs) using an established statistical shape model. The PCs were used to predict whole-body and regional body composition values using published equations. Body composition changes (follow-up minus baseline) were compared to DXA with linear regression. The analysis included 128 participants (43 females) in six studies. The mean (SD) length of follow-up was 13 (5) weeks, range of 3-23 weeks; change in percent fat was 2.8% (2.1%), with a range of 2.4 - 8.3%. Agreement between 3DO and DXA (R2) for changes in total fat mass (FM), total fat-free mass (FFM), and appendicular lean mass, respectively, were 0.89, 0.77, and 0.69 with RMSEs of 1.78 kg, 1.42 kg, and 0.37 kg in females, and 0.77, 0.76, and 0.46 with RMSEs of 2.28 kg, 1.67 kg, and 0.51 kg in males. Statistical significance of individual changes agreed for both DXA and 3DO in the majority of the sample for total FM (70%) and FFM (81%). Further adjustment with demographic descriptors improved the 3DO change agreement to changes observed with DXA. Compared to DXA, 3DO was highly sensitive in detecting body shape changes over time. The 3DO method was sensitive enough to detect even small changes in body composition during intervention studies. The safety and accessibility of 3DO allow users to self-monitor frequently throughout interventions.\",\"PeriodicalId\":315016,\"journal\":{\"name\":\"The American journal of clinical nutrition\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The American journal of clinical nutrition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1101/2022.11.14.22281814\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The American journal of clinical nutrition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2022.11.14.22281814","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Monitoring Body Composition Change for Intervention Studies with Advancing 3D Optical Imaging Technology in Comparison to Dual-Energy X-Ray Absorptiometry
Recent 3D optical (3DO) imaging advancements have provided more accessible, affordable, and self-operating opportunities for assessing body composition. 3DO is accurate and precise with respect to clinical measures made by dual-energy X-ray absorptiometry (DXA). However, the sensitivity for monitoring body composition change over time with 3DO body shape is unknown. Therefore, this study aimed to evaluate 3DO ability to monitor body composition changes across multiple intervention studies. A retrospective analysis was performed using intervention studies on healthy adults that were complimentary to the cross-sectional study, Shape Up! Adults. Each participant received a DXA (Hologic Discovery/A system) and 3DO (Fit3D ProScanner) scan at baseline and follow-up. 3DO meshes were digitally registered and reposed using Meshcapade to standardize the vertices and pose. Each 3DO mesh was transformed into principal components (PCs) using an established statistical shape model. The PCs were used to predict whole-body and regional body composition values using published equations. Body composition changes (follow-up minus baseline) were compared to DXA with linear regression. The analysis included 128 participants (43 females) in six studies. The mean (SD) length of follow-up was 13 (5) weeks, range of 3-23 weeks; change in percent fat was 2.8% (2.1%), with a range of 2.4 - 8.3%. Agreement between 3DO and DXA (R2) for changes in total fat mass (FM), total fat-free mass (FFM), and appendicular lean mass, respectively, were 0.89, 0.77, and 0.69 with RMSEs of 1.78 kg, 1.42 kg, and 0.37 kg in females, and 0.77, 0.76, and 0.46 with RMSEs of 2.28 kg, 1.67 kg, and 0.51 kg in males. Statistical significance of individual changes agreed for both DXA and 3DO in the majority of the sample for total FM (70%) and FFM (81%). Further adjustment with demographic descriptors improved the 3DO change agreement to changes observed with DXA. Compared to DXA, 3DO was highly sensitive in detecting body shape changes over time. The 3DO method was sensitive enough to detect even small changes in body composition during intervention studies. The safety and accessibility of 3DO allow users to self-monitor frequently throughout interventions.